%processed by gecco2014_toc.awk $Revision: 1.46 $ ARGC=3 Sun Aug 10 19:35:13 BST 2014 %1 gecco2014_toc.txt %2 gecco2014.bib %WBL 1 Aug 2017 ensure passes bibclean v3.02 @inproceedings{Ahmed:2014:GECCO, author = {Hazem Radwan Ahmed and Janice I. Glasgow}, title = {An improved multi-start particle swarm-based algorithm for protein structure comparison}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {1--8}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598212}, doi = {doi:10.1145/2576768.2598212}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper proposes a novel particle-swarm based approach for protein structure alignment and comparison. Applying heuristic search to discover similar protein substructure patterns can be easily trapped in certain regions of the sparse and challenging problem search space. Diversification, or restarting the heuristic search, is one of the common strategies used to escape local optima. Agile Particle Swarm Optimisation (APSO) is a recent multi-start PSO that addresses the question of when to best restart swarm particles. This paper focuses on where and how to restart the swarm. Another challenge of applying a heuristic search to protein structures is that the fitness landscape does not necessarily guide to the optimal region. To address this issue, we propose the Targeted Agile PSO (TA-PSO) that uses a dynamic window-based search for automatic, variable-size pattern discovery in protein structures. The TA-PSO automatically builds a guiding list of potential patterns and uses it during the search process, which helps to find better solutions faster. The proposed TA-PSO showed up to 4 times improved performance that is 3.5 times faster and 6 times more robust/consistent compared with the traditional --non-targeted search}, notes = {Also known as \cite{2598212} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Bonyadi:2014:GECCO, author = {Mohammad Reza Bonyadi and Zbigniew Michalewicz}, title = {SPSO 2011: analysis of stability; local convergence; and rotation sensitivity}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {9--16}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598263}, doi = {doi:10.1145/2576768.2598263}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In a particle swarm optimisation algorithm (PSO) it is essential to guarantee convergence of particles to a point in the search space (this property is called stability of particles). It is also important that the PSO algorithm converges to a local optimum (this is called the local convergence property). Further, it is usually expected that the performance of the PSO algorithm is not affected by rotating the search space (this property is called the rotation sensitivity). In this paper, these three properties, i.e. stability of particles, local convergence, and rotation sensitivity are investigated for a variant of PSO called Standard PSO2011 (SPSO2011). We experimentally define boundaries for the parameters of this algorithm in such a way that if the parameters are selected in these boundaries, the particles are stable, i.e. particles converge to a point in the search space. Also, we show that, unlike earlier versions of PSO, these boundaries are dependent on the number of dimensions of the problem. Moreover, we show that the algorithm is not locally convergent in general case. Finally, we provide a proof and experimental evidence that the algorithm is rotation invariant.}, notes = {Also known as \cite{2598263} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Gonzalez-Fernandez:2014:GECCO, author = {Yasser Gonzalez-Fernandez and Stephen Chen}, title = {Identifying and exploiting the scale of a search space in particle swarm optimization}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {17--24}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598280}, doi = {doi:10.1145/2576768.2598280}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Multi-modal optimisation involves two distinct tasks: identifying promising attraction basins and finding the local optima in these basins. Unfortunately, the second task can interfere with the first task if they are performed simultaneously. Specifically, the promise of an attraction basin is often estimated by the fitness of a single sample solution, so an attraction basin represented by a random sample solution can appear to be less promising than an attraction basin represented by its local optimum. The goal of thresheld convergence is to prevent these biased comparisons by disallowing local search while global search is still in progress. Ideally, thresheld convergence achieves this goal by using a distance threshold that is correlated to the size of the attraction basins in the search space. In this paper, a clustering-based method is developed to identify the scale of the search space which threshold convergence can then exploit. The proposed method employed in the context of a multi-start particle swarm optimization algorithm has led to large improvements across a broad range of multi-modal problems.}, notes = {Also known as \cite{2598280} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Hernandez-Ocana:2014:GECCO, author = {Betania Hernandez-Ocana and Ma. {Del Pilar Pozos-Parra} and Efr\'{e}n Mezura-Montes}, title = {Stepsize control on the modified bacterial foraging algorithm for constrained numerical optimization}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {25--32}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598379}, doi = {doi:10.1145/2576768.2598379}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The step size value is one of the most sensitive parameters in the bacterial foraging optimisation algorithm when solving constrained numerical optimization problems. In this paper, four step size control mechanisms are proposed and analysed in the modified bacterial foraging optimization algorithm. The first one is based on a random value which remains fixed during the search, the second one generates a random value per cycle, the third one is based on a nonlinear decreasing function and the last one is an adaptive approach. Seven experiments are proposed to evaluate the abilities of each mechanism to: (1) obtain competitive final results, (2) find feasible solutions, (3) find the feasible global optimum, (4) promote successful swims, and (5) decrease the constraint violation. A comparison against two state-of-the-art algorithms is considered to evaluate the performance of the most competitive control mechanism. A well-known set of constrained numerical optimisation problems is used in the experiments as well as six performance measures. The results obtained show that the control mechanism based on the nonlinear decreasing function is the most competitive and provides the ability to generate better solutions late in the search.}, notes = {Also known as \cite{2598379} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Lin:2014:GECCO, author = {Ying Lin and Jing-Jing Li and Jun Zhang and Meng Wan}, title = {A tribal ecosystem inspired algorithm (TEA) for global optimization}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {33--40}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598253}, doi = {doi:10.1145/2576768.2598253}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Evolution mechanisms of different biological and social systems have inspired a variety of evolutionary computation (EC) algorithms. However, most existing EC algorithms simulate the evolution procedure at the individual-level. This paper proposes a new EC mechanism inspired by the evolution procedure at the tribe-level, namely tribal ecosystem inspired algorithm (TEA). In TEA, the basic evolution unit is not an individual that represents a solution point, but a tribe that covers a subarea in the search space. More specifically, a tribe represents the solution set locating in a particular subarea with a coding structure composed of three elements: tribal chief, attribute diversity, and advancing history. The tribal chief represents the locally best-so-far solution, the attribute diversity measures the range of the subarea, and the advancing history records the local search experience. This way, the new evolution unit provides extra knowledge about neighbourhood profiles and search history. Using this knowledge, TEA introduces four evolution operators, reforms, self-advance, synergistic combination, and augmentation, to simulate the evolution mechanisms in a tribal ecosystem, which evolves the tribes from potentially promising subareas to the global optimum. The proposed TEA is validated on benchmark functions. Comparisons with three representative EC algorithms confirm its promising performance.}, notes = {Also known as \cite{2598253} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Liu:2014:GECCO, author = {Xiao-Fang Liu and Zhi-Hui Zhan and Ke-Jing Du and Wei-Neng Chen}, title = {Energy aware virtual machine placement scheduling in cloud computing based on ant colony optimization approach}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {41--48}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598265}, doi = {doi:10.1145/2576768.2598265}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Cloud computing provides resources as services in pay-as-you-go mode to customers by using virtualisation technology. As virtual machine (VM) is hosted on physical server, great energy is consumed by maintaining the servers in data centre. More physical servers means more energy consumption and more money cost. Therefore, the VM placement (VMP) problem is significant in cloud computing. This paper proposes an approach based on ant colony optimization (ACO) to solve the VMP problem, named as ACO-VMP, so as to effectively use the physical resources and to reduce the number of running physical servers. The number of physical servers is the same as the number of the VMs at the beginning. Then the ACO approach tries to reduce the physical server one by one. We evaluate the performance of the proposed ACO-VMP approach in solving VMP with the number of VMs being up to 600. Experimental results compared with the ones obtained by the first-fit decreasing (FFD) algorithm show that ACO-VMP can solve VMP more efficiently to reduce the number of physical servers significantly, especially when the number of VMs is large.}, notes = {Also known as \cite{2598265} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Solum:2014:GECCO, author = {Timothy Solum and Brent E. Eskridge and Ingo Schlupp}, title = {Consensus costs and conflict in a collective movement}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {49--56}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598357}, doi = {doi:10.1145/2576768.2598357}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Aggregation, whether it be in natural or artificial systems, provides numerous benefits to both the individual and the group. However, aggregation has costs and frequently involves inter-individual conflict. Although conflicts in natural systems is understood to be at times beneficial, as well as detrimental, conflict in artificial systems, such as a team of robots, is frequently viewed as inhibiting consensus and, therefore, success. This is particularly the case in large-scale aggregations where ensuring consensus is especially challenging. In response, mechanisms are often integrated into the group's control systems to minimise, or even eliminate, conflicts of interest. As a result, the potential benefits of losing consensus, such as increased diversity and reduced consensus costs, are not available. Using a biologically-based collective movement model, we demonstrate that not enforcing consensus and allowing conflict to evolve as agents make decisions results in a system in which agents meet their own needs, thus minimising consensus costs, while still maintaining group cohesion when possible. Simulations predict that conflict balances consensus costs with individual preferences such that both individual and group goals are met.}, notes = {Also known as \cite{2598357} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Volkel:2014:GECCO, author = {Gunnar V\"{o}lkel and Markus Maucher and Uwe Sch\"{o}ning and Hans A. Kestler}, title = {Ant colony optimization with group learning}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {57--64}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598214}, doi = {doi:10.1145/2576768.2598214}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We introduce Group Learning for Ant Colony Optimization applied to combinatorial optimisation problems with group-structured solution encodings. In contrast to the common assignment of one pheromone value per solution component in Group Learning each solution component has one pheromone value per group. Hence, the algorithm has the possibility to learn the optimal group membership of the components. We present different strategies for Group Learning and evaluate these in simulation experiments for the Vehicle Routing Problem with Time Windows using the problem instances of Solomon. We describe a revised Ant Colony System (ACS) algorithm which does not use a local pheromone update while maintaining the general ideas of ACS. We evaluate the revised ACS experimentally comparing it to the original ACS. Our experimental results show that Group Learning is a valuable modification for Ant Colony Optimisation. Additionally, the results indicate that the revised ACS performs at least as well as the original algorithms.}, notes = {Also known as \cite{2598214} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{vonderOsten:2014:GECCO, author = {Friedrich Burkhard {von der Osten} and Michael Kirley and Tim Miller}, title = {Anticipatory stigmergic collision avoidance under noise}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {65--72}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598389}, doi = {doi:10.1145/2576768.2598389}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Reactive path planning to avoid collisions with moving obstacles enables more robust agent systems. However, many solutions assume that moving objects are passive; that is, they do not consider that the moving objects are themselves re-planning to avoid collisions, and thus may change their trajectory. In this paper we present a model, Anticipatory Stigmergic Collision Avoidance (ASCA) for reciprocal collision avoidance using anticipatory stigmergy. Unlike standard stigmergy, in which agents leave pheromones to indicate a trace of previous actions, anticipatory stigmergy deposits pheromones on intended future paths. By sharing their intended future paths with each other at regular intervals, agents can re-plan to attempt to avoid collisions. We experimentally evaluate ASCA over three scenarios, and compare with a state of art approach, Reciprocal Velocity Obstacles (RVO). Our evaluation showed that ASCA is consistently more robust in noisy environments in which transmitted information can be lost or degraded. Further, using ASCA without noise results in fewer collisions than RVO when agents are in formation, but more collisions when formed randomly.}, notes = {Also known as \cite{2598389} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Yu:2014:GECCO, author = {Wei-Jie Yu and Jing-Jing Li and Jun Zhang and Meng Wan}, title = {Differential evolution using mutation strategy with adaptive greediness degree control}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {73--80}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598236}, doi = {doi:10.1145/2576768.2598236}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Differential evolution (DE) has been demonstrated to be one of the most promising evolutionary algorithms (EAs) for global numerical optimisation. DE mainly differs from other EAs in that it employs difference of the parameter vectors in mutation operator to search the objective function landscape. Therefore, the performance of a DE algorithm largely depends on the design of its mutation strategy. In this paper, we propose a new kind of DE mutation strategies whose greediness degree can be adaptively adjusted. The proposed mutation strategies use the information of top t solutions in the current population. Such a greedy strategy is beneficial to fast convergence performance. In order to adapt the degree of greediness to fit for different optimisation scenarios, the parameter t is adjusted in each generation of the algorithm by an adaptive control scheme. This way, the convergence performance and the robustness of the algorithm can be enhanced at the same time. To evaluate the effectiveness of the proposed adaptive greedy mutation strategies, the approach is applied to original DE algorithms, as well as DE algorithms with parameter adaptation. Experimental results indicate that the proposed adaptive greedy mutation strategies yield significant performance improvement for most of cases studied.}, notes = {Also known as \cite{2598236} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{ZapotecasMartinez:2014:GECCO, author = {Saul {Zapotecas Martinez} and Alfredo {Arias Montano} and Carlos A. {Coello Coello}}, title = {Constrained multi-objective aerodynamic shape optimization via swarm intelligence}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {81--88}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598372}, doi = {doi:10.1145/2576768.2598372}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper, we present a Multi-objective Particle Swarm Optimizer (MOPSO) based on a decomposition approach, which is proposed to solve Constrained Multi-Objective Aerodynamic Shape Optimisation Problems (CMO-ASOPs). The constraint-handling technique adopted in this approach is based on the well-known epsilon-constraint method. Since the ?-constraint method was initially proposed to deal with constrained single-objective optimisation Problems, we adapted it so that it could be incorporated into a MOPSO. Our main focus is to solve CMO-ASOPs in an efficient and effective manner. The proposed constrained MOPSO guides the search by updating the position of each particle using a set of solutions considered as the global best according to both the decomposition approach and the epsilon-constraint method. Our preliminary results indicate that our proposed approach is able to outperform a state-of-the-art MOEA in several CMO-ASOPs.}, notes = {Also known as \cite{2598372} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Zhang:2014:GECCO, author = {Chuan-Bin Zhang and Yue-Jiao Gong and Jing-Jing Li and Ying Lin}, title = {Automatic path planning for autonomous underwater vehicles based on an adaptive differential evolution}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {89--96}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598267}, doi = {doi:10.1145/2576768.2598267}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper proposes a path planner for autonomous underwater vehicles (AUVs) in 3-D underwater space. We simulate an underwater space with rugged seabed and suspending obstacles, which is close to real world. In the proposed representation scheme, the problem space is decomposed into parallel subspaces and each subspace is described by a grid method. The paths of AUVs are simplified as a set of successive points in the problem space. By jointing these way points, the entire path of the AUV is obtained. A cost function with penalty method takes into account the length, energy consumption, safety and curvature constraints of AUVs. It is applied to evaluate the quality of paths. Differential evolution (DE) algorithm is used as a black-box optimisation tool to provide optimal solutions for the path planning. In addition, we adaptively adjust the parameters of DE according to population distribution and the blockage of parallel subspaces so as to improve its performance. Experiments are conducted on 6 different scenarios. The results validate that the proposed algorithm is effective for improving solution quality and avoiding premature convergence.}, notes = {Also known as \cite{2598267} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Chelly:2014:GECCO, author = {Zeineb Chelly and Zied Elouedi}, title = {A two-leveled hybrid dendritic cell algorithm under imprecise reasoning}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {97--104}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598217}, doi = {doi:10.1145/2576768.2598217}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The Dendritic Cell Algorithm(DCA) is a bio-inspired algorithm based on the behaviour of Dendritic Cells(DCs). The DCA performance relies on its data pre-processing phase where feature extraction and signal categorisation are performed and which are based on the use of the Principal Component Analysis(PCA) technique. However, using PCA presents a limitation as it destroys the underlying semantics of the features after reduction. To overcome this limitation, Rough Set Theory(RST) was applied as a pre-processor; but, still the developed rough approach presents an information loss as data should be discretised beforehand. Indeed, DCA was known to be sensitive to the input class data order. This is due to the crisp separation between the two DCs contexts; semi-mature and mature. Thus, the aim of this paper is to develop a novel DCA version based on a two-leveled hybrid model handling the mentioned DCA shortcomings. In the top-level, our proposed algorithm applies a more adequate feature extraction technique based on Fuzzy Rough Set Theory(FRST) to build a solid data pre-processing phase. At the bottom level, our algorithm applies Fuzzy Set Theory to smooth the crisp separation between the DCs contexts. Results show that our proposed algorithm succeeds in obtaining significantly improved classification accuracy.}, notes = {Also known as \cite{2598217} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Ludwig:2014:GECCO, author = {Simone A. Ludwig}, title = {Clonal selection based fuzzy C-means algorithm for clustering}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {105--112}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598270}, doi = {doi:10.1145/2576768.2598270}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In recent years, fuzzy based clustering approaches have shown to outperform state-of-the-art hard clustering algorithms in terms of accuracy. The difference between hard clustering and fuzzy clustering is that in hard clustering each data point of the data set belongs to exactly one cluster, and in fuzzy clustering each data point belongs to several clusters that are associated with a certain membership degree. Fuzzy c-means clustering is a well-known and effective algorithm, however, the random initialisation of the centroids directs the iterative process to converge to local optimal solutions easily. In order to address this issue a clonal selection based fuzzy c-means algorithm (CSFCM) is introduced. CSFCM is compared with the basic Fuzzy C-Means (FCM) algorithm, a genetic algorithm based FCM (GAFCM) algorithm, and a particle swarm optimization based FCM (PSOFCM) algorithm.}, notes = {Also known as \cite{2598270} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Oliveto:2014:GECCO, author = {Pietro S. Oliveto and Dirk Sudholt}, title = {On the runtime analysis of stochastic ageing mechanisms}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {113--120}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598328}, doi = {doi:10.1145/2576768.2598328}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Ageing operators are applied in the field of artificial immune systems (AIS) to increase the diversity of the population during the optimisation process. Previous theoretical analyses have shown how static ageing operators can successfully escape local optima by implicitly performing a restart of the algorithm. However, showing naturally that ageing in an AIS is more effective than a conceptually simpler restart strategy has proved to be a hard task. We present a rigorous analysis of stochastic ageing mechanisms and show that superior performance compared to just simple restarts can be achieved. Since standard stochastic pure ageing is only effective for small population sizes, we present a hybrid pure ageing operator that achieves the same performance independent of the population size. For a benchmark function used in dynamic optimisation we rigorously prove that hybrid pure ageing allows to escape local optima beyond restarts while static pure ageing is inefficient. The results also apply to the non-dynamic setting. An analytical general framework for the analysis of standard stochastic pure ageing is presented along the way.}, notes = {Also known as \cite{2598328} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Sim:2014:GECCO, author = {Kevin Sim and Emma Hart}, title = {An improved immune inspired hyper-heuristic for combinatorial optimisation problems}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {121--128}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598241}, doi = {doi:10.1145/2576768.2598241}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The meta-dynamics of an immune-inspired optimisation system NELLI are considered. NELLI has previously shown to exhibit good performance when applied to a large set of optimisation problems by sustaining a network of novel heuristics. We address the mechanisms by which new heuristics are defined and subsequently generated. A new representation is defined, and a mutation-based operator inspired by clonal-selection introduced to control the balance between exploration and exploitation in the generation of new network elements. Experiments show significantly improved performance over the existing system in the bin-packing domain. New experiments in the job-scheduling domain further show the generality of the approach.}, notes = {Also known as \cite{2598241} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Textor:2014:GECCO, author = {Johannes Textor and Katharina Dannenberg and Macie Li\'{s}kiewicz}, title = {A generic finite automata based approach to implementing lymphocyte repertoire models}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {129--136}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598331}, doi = {doi:10.1145/2576768.2598331}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Artificial immune systems (AIS) inspired by lymphocyte repertoires include negative and positive selection, clonal selection, and B~cell algorithms. Such AISs are used in computer science for machine learning and optimisation, and in biology for modelling of fundamental immunological processes. In both cases, the necessary size of repertoire models can be huge. Here, we show that when lymphocyte repertoire models based on string patterns can be compactly represented as finite automata (FA), this allows to efficiently perform negative selection, positive selection, insertion into, deletion from, uniform sampling from, and counting the repertoire. Specifically, for r-contiguous pattern matching, all these tasks can be performed in polynomial time. But even in NP-hard cases like Hamming distance matching, the FA representation can still lead to practically important efficiency gains. We demonstrate the feasibility and flexibility of this approach by implementing T~cell positive selection simulations based on human genomic data using four different pattern rules. Hence, FA-based repertoire models generalise previous efficient negative selection algorithms to perform several related algorithmic tasks, are easy to implement and customise, and are applicable to real-world Bioinformatics problems.}, notes = {Also known as \cite{2598331} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Acre:2014:GECCO, author = {Jeremy Acre and Brent E. Eskridge and Nicholas Zoller and Ingo Schlupp}, title = {Adapting to a changing environment using winner and loser effects}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {137--144}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598355}, doi = {doi:10.1145/2576768.2598355}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Many animals form large aggregations that have no apparent consistent leader, yet are capable of highly coordinated movements. At any given time, it seems like an individual can emerge as a leader only to be replaced by another. Although individuals within a group are largely considered equal, even individuals in a homogeneous group are different. Clearly individuals will differ based on traits like sex, age, and experience. Of particular interest is the idea of individuals differing in their correlated traits, or personality. Different personalities can arise via complex interactions between genes and an environment and are often shaped by individual experience. For example, one would generally predict that individuals characterised as 'bold' would more frequently be leaders. However, if the environment changes, how do once successful leaders respond to failure and how do newly successful leaders emerge? Using a biologically-based collective movement model, we demonstrate that a self-assessment mechanism using winner and loser effects is capable of producing transitory leaders who change roles in response to changes in the environment. Furthermore, simulations predict that this self-assessment mechanism allows the group to adapt to drastic changes in the environment and remain successful.}, notes = {Also known as \cite{2598355} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Asher:2014:GECCO, author = {Derrik E. Asher and Jeffrey L. Krichmar and Nicolas Oros}, title = {Evolution of biologically plausible neural networks performing a visually guided reaching task}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {145--152}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598368}, doi = {doi:10.1145/2576768.2598368}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {An evolutionary strategy (ES) algorithm was used to evolve a simulated neural network based on the known anatomy of the posterior parietal cortex (PPC), to perform a visually guided reaching task. In this task, a target remained visible for the duration of a trial, and an agent's goal was to move its hand to the target as rapidly as possible and remain for the duration of that trial. The ES was used to tune the strength of 15609 connections between neural areas and 4 parameters governing the neural dynamics. The model had sensory latencies replicating those found in recording studies with monkeys. The ES ran 100 times and generated very diverse networks that could all perform the task well. The evolved networks 1) showed velocity profiles consistent with biological movements, and 2) found solutions that reflect short-range excitation and long-range, contralateral inhibition similar to neurobiological networks. These results provide theoretical evidence for the important parameters and projections governing sensori-motor transformations in neural systems.}, notes = {Also known as \cite{2598368} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Catteeuw:2014:GECCO, author = {David Catteeuw and The Anh Han and Bernard Manderick}, title = {Evolution of honest signaling by social punishment}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {153--160}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598312}, doi = {doi:10.1145/2576768.2598312}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {When facing dishonest behaviour of any form, individuals may choose to punish in order to enhance future honesty from others, even if it is costly for the punishers. Such behaviour can be found ubiquitously in human and animal communications, suggesting that it may play an important role in the evolution of honest signalling or reliable communication. By applying Evolutionary Game Theory to the Philip Sidney game, we provide a computational model to investigate whether costly punishment can be a viable strategy for the evolution of honest signalling. We identify four different forms of dishonesty, and study how punishing them affects the level of honesty in the final outcome of evolutionary dynamics. Our results show that punishing those that lie can significantly boost honest signaling when conflicts are moderate and signals are cheap or cost-free. It hence provides an important alternative to the well-known Handicap Principle, which states that honest signalling can evolve only if signals are sufficiently costly for their senders. Furthermore, punishing greedy responses promotes honest signalling if conflicts of interest are high and signals are costly. Lastly, punishing timid or worried individuals does not lead to a clear improvement of honesty.}, notes = {Also known as \cite{2598312} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Coleman:2014:GECCO, author = {Oliver J. Coleman and Alan D. Blair and Jeff Clune}, title = {Automated generation of environments to test the general learning capabilities of AI agents}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {161--168}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598257}, doi = {doi:10.1145/2576768.2598257}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Abstract Algorithms for evolving agents that learn during their lifetime have typically been evaluated on only a handful of environments. Designing such environments is labour intensive, potentially biased, and provides only a small sample size that may prevent accurate general conclusions from being drawn. In this paper we introduce a method for automatically generating MDP environments which allows the difficulty to be scaled in several ways. We present a case study in which environments are generated that vary along three key dimensions of difficulty: the number of environment configurations, the number of available actions, and the length of each trial. The study reveals interesting differences between three neural network models -- Fixed-Weight, Plastic-Weight, and Modulated Plasticity -- that would not have been obvious without sweeping across these different dimensions. Our paper thus introduces a new way of conducting reinforcement learning science: instead of manually designing a few environments, researchers will be able to automatically generate a range of environments across key dimensions of variation. This will allow scientists to more rigorously assess the general learning capabilities of an algorithm, and may ultimately improve the rate at which we discover how to create AI with general purpose learning.}, notes = {Also known as \cite{2598257} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Fairey:2014:GECCO, author = {Jason Fairey and Terence Soule}, title = {Evolution of communication and cooperation}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {169--176}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598377}, doi = {doi:10.1145/2576768.2598377}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In the wild, spotted hyenas have been observed to chase lions away from a recent kill. This is a high risk, high reward behaviour that requires significant teamwork and decision making skills. Modelling this behaviour and creating algorithms that can improve evolutionarily may lead to more adaptable artificial systems for robotics and other cooperative artificial agents. Previous research has shown that having a lead or 'flag bearer' hyena can significantly improve evolution. Thus, the complex social dynamics and coordination abilities required for this problem make it interesting artificial intelligence task. This also suggests that the type and encoding of the sensory inputs has a significant effect on the evolutionary trajectory and overall success at the task. Additionally, in the wild genetic diversity is driven by the migration of young males between packs, which leads to interesting evolutionary questions. To address the role of input encodings we introduce two evolutionary neural network variants, one using absolute headings as inputs/outputs and one using relative headings as inputs/outputs (headings defined relative to environmental elements). Our results show that the networks with relative inputs and outputs evolve significantly faster and result in better performance, suggesting that a critical difference is the existence of easily accessible, problem relevant, references for defining movement vectors. Our results also show that the inclusion of a leader in the team structure can improve the rate at which cooperative behaviours are evolved, but does not lead to better overall behaviours. In addition, we examine the emerging behaviours as the teams go from random behavior to a circling pattern to an aggressive charge towards the goal.}, notes = {Also known as \cite{2598377} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Johnson:2014:GECCO, author = {Anya E. Johnson and Heather J. Goldsby and Sherri Goings and Charles Ofria}, title = {The evolution of kin inclusivity levels}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {177--184}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598283}, doi = {doi:10.1145/2576768.2598283}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Altruism is a ubiquitous strategy among organisms ranging from microbes to mammals. Inclusive fitness theory indicates that altruistic strategies can be beneficial when an altruist acts to benefit organisms that share its genes. It is common for such altruistic strategies to be negatively affected by cheaters that do not act altruistically. A more subtle form of cheating involves altruists that are more selective. For example, a selective organism may benefit from a distant kin's altruistic actions without reciprocating. We consider an organism's kin inclusivity level to be the maximum number of mutational differences where the other organism will be considered kin. We use evolving computer programs (digital organisms) to explore competitions among organisms with different kin inclusivity levels. Using competition assays that vary environmental parameters, we find that high mutation rates favour more inclusive colonies. When we competed colonies with a wide range of kin inclusivity levels, we found that moderate mutation rates and populations sizes led to intermediate inclusivity levels winning the competitions, indicating that extreme inclusivity levels were not always optimal. However, when organisms could set their own kin inclusivity level, we found that high mutation rates selected for highly inclusive organisms.}, notes = {Also known as \cite{2598283} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Lehman:2014:GECCO, author = {Joel Lehman and Risto Miikkulainen}, title = {Overcoming deception in evolution of cognitive behaviors}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {185--192}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598300}, doi = {doi:10.1145/2576768.2598300}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {When scaling neuroevolution to complex behaviours, cognitive capabilities such as learning, communication, and memory become increasingly important. However, successfully evolving such cognitive abilities remains difficult. This paper argues that a main cause for such difficulty is deception, i.e. evolution converges to a behaviour unrelated to the desired solution. More specifically, cognitive behaviours often require accumulating neural structure that provides no immediate fitness benefit, and evolution often thus converges to non-cognitive solutions. To investigate this hypothesis, a common evolutionary robotics T-Maze domain is adapted in three separate ways to require agents to communicate, remember, and learn. Indicative of deception, evolution driven by objective-based fitness often converges upon simple non-cognitive behaviors. In contrast, evolution driven to explore novel behaviors, i.e. novelty search, often evolves the desired cognitive behaviors. The conclusion is that open-ended methods of evolution may better recognise and reward the stepping stones that are necessary for cognitive behaviour to emerge.}, notes = {Also known as \cite{2598300} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Li:2014:GECCO, author = {Jingyu Li and Jed Storie and Jeff Clune}, title = {Encouraging creative thinking in robots improves their ability to solve challenging problems}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {193--200}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598222}, doi = {doi:10.1145/2576768.2598222}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Evolutionary algorithms frequently get stuck on local optima--and fail to find the global optimum--when local gradients do not point the search process toward the direction of the global optimum. A recent breakthrough called Novelty Search ameliorates this problem by enabling the search process to explore in every direction by encouraging the production of novel, or not-yet-seen, phenotypes (e.g. new robot behaviours). However, a problem with Novelty Search is that it can get lost on 'novelty plateaus' wherein novel behaviours in offspring are not immediately produced by mutation and crossover (e.g. when a sequence of specific mutations is required to produce new behaviors, but the intermediate mutations are not rewarded because they do not produce novel behaviors). In such cases, Novelty Search and related approaches that reward behavioural diversity can get stuck. Here we introduce a new approach, borrowed from human psychology, that mitigates this problem: encouraging creative thinking. In addition to rewarding novel behaviour, we encourage evolving neural networks to 'think differently' by rewarding not-yet-seen firing patterns in hidden neurons, which we call the 'Creative Thinking Approach.' We suggest that encouraging novel thinking can reward stepping stones toward new behaviours. On a variety of challenging robotic control problems from previous publications we demonstrate that, as problem difficulty increases, adding the Creative Thinking Approach increasingly improves performance over simply encouraging novel behaviours. Our results suggest that the Creative Thinking Approach could help improve the scale and complexity of problems that can be solved by evolutionary algorithms.}, notes = {Also known as \cite{2598222} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Li:2014:GECCOa, author = {Wei Li and Melvin Gauci and Roderich Gross}, title = {Coevolutionary learning of swarm behaviors without metrics}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {201--208}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598349}, doi = {doi:10.1145/2576768.2598349}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We propose a coevolutionary approach for learning the behaviour of animals, or agents, in collective groups. The approach requires a replica that resembles the animal under investigation in terms of appearance and behavioural capabilities. It is able to identify the rules that govern the animals in an autonomous manner. A population of candidate models, to be executed on the replica, compete against a population of classifiers. The replica is mixed into the group of animals and all individuals are observed. The fitness of the classifiers depends solely on their ability to discriminate between the replica and the animals based on their motion over time. Conversely, the fitness of the models depends solely on their ability to 'trick' the classifiers into categorising them as an animal. Our approach is metric-free in that it autonomously learns how to judge the resemblance of the models to the animals. It is shown in computer simulation that the system successfully learns the collective behaviours of aggregation and of object clustering. A quantitative analysis reveals that the evolved rules approximate those of the animals with a good precision.}, notes = {Also known as \cite{2598349} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Moore:2014:GECCO, author = {Jared M. Moore and Philip K. McKinley}, title = {Evolving joint-level control with digital muscles}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {209--216}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598373}, doi = {doi:10.1145/2576768.2598373}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The neuromuscular systems of animals are governed by extremely complex networks of control signals, sensory feedback loops, and mechanical interactions. Morphology and control are inherently intertwined. In the case of animal joints, groups of muscles work together to provide power and stability to move limbs in a coordinated manner. In contrast, many robot controllers handle both high-level planning and low-level control of individual joints. In this paper, we propose a joint-level control method, called digital muscles, that operates in a manner analogous to biological muscles, yet is abstract enough to apply to conventional robotic joints. An individual joint is controlled by multiple muscle nodes, each of which responds to a control signal according to a node-specific activation function. Evolving the physical orientation of muscle nodes and their respective activation functions enables relatively complex and coordinated gaits to be realised with simple high-level control. Even using a sinusoid as the high-level control signal, we demonstrate the evolution of effective gaits for a simulated quadruped. The proposed model realises a control strategy for governing the behaviour of individual joints, and can be coupled with a high-level controller that focuses on decision making and planning.}, notes = {Also known as \cite{2598373} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Pugh:2014:GECCO, author = {Justin K. Pugh and Skyler Goodell and Kenneth O. Stanley}, title = {Directional communication in evolved multiagent teams}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {217--224}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598299}, doi = {doi:10.1145/2576768.2598299}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The question of how to best design a communication architecture is becoming increasingly important for evolving autonomous multiagent systems. Directional reception of signals, a design feature of communication that appears in most animals, is present in only some existing artificial communication systems. This paper suggests that such directional reception benefits the evolution of communicating autonomous agents because it simplifies the language required to express positional information, which is critical to solving many group coordination tasks. This hypothesis is tested by comparing the evolutionary performance of several alternative communication architectures (both directional and non-directional) in a multiagent foraging domain designed to require a basic 'come here' type of signal for the optimal solution. Results confirm that directional reception is a key ingredient in the evolutionary tractability of effective communication. Furthermore, the real world viability of directional reception is demonstrated through the successful transfer of the best evolved controllers to real robots. The conclusion is that directional reception is important to consider when designing communication architectures for more complicated tasks in the future.}, notes = {Also known as \cite{2598299} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Shorten:2014:GECCO, author = {David Peter Shorten and Geoff Stuart Nitschke}, title = {Generational neuro-evolution: restart and retry for improvement}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {225--232}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598295}, doi = {doi:10.1145/2576768.2598295}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper proposes a new Neuro-Evolution (NE) method for automated controller design in agent-based systems. The method is Generational Neuro-Evolution (GeNE), and is comparatively evaluated with established NE methods in a multi-agent predator-prey task. This study is part of an ongoing research goal to derive efficient (minimising convergence time to optimal solutions) and scalable (effective for increasing numbers of agents) controller design methods for adapting agents in neuro-evolutionary multi-agent systems. Dissimilar to comparative NE methods, GeNE employs tiered selection and evaluation as its generational fitness evaluation mechanism and, furthermore, re-initialises the population each generation. Results indicate that GeNE is an appropriate controller design method for achieving efficient and scalable behaviour in a multi-agent predator-prey task, where the goal was for multiple predator agents to collectively capture a prey agent. GeNE outperforms comparative NE methods in terms of efficiency (minimising the number of genotype evaluations to attain optimal task performance).}, notes = {Also known as \cite{2598295} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Woolley:2014:GECCO, author = {Brian G. Woolley and Kenneth O. Stanley}, title = {A novel human-computer collaboration: combining novelty search with interactive evolution}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {233--240}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598353}, doi = {doi:10.1145/2576768.2598353}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Recent work on novelty and behavioural diversity in evolutionary computation has highlighted the potential disadvantage of driving search purely through objective means. This paper suggests that leveraging human insight during search can complement such novelty-driven approaches. In particular, a new approach called novelty-assisted interactive evolutionary computation (NA-IEC) combines human intuition with novelty search to facilitate the serendipitous discovery of agent behaviours in a deceptive maze. In this approach, the human user directs evolution by selecting what is interesting from the on-screen population of behaviours. However, unlike in typical IEC, the user can now request that the next generation be filled with novel descendants. The experimental results demonstrate that combining human insight with novelty search not only finds solutions significantly faster and at lower genomic complexities than fully-automated processes guided purely by fitness or novelty, but it also finds solutions faster than the traditional IEC approach. Such results add to the evidence that combining human users and automated processes creates a synergistic effect in the search for solutions.}, notes = {Also known as \cite{2598353} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Zahadat:2014:GECCO, author = {Payam Zahadat and Thomas Schmickl}, title = {Wolfpack-inspired evolutionary algorithm and a reaction-diffusion-based controller are used for pattern formation}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {241--248}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598262}, doi = {doi:10.1145/2576768.2598262}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The implicit social structure of population groups have been previously investigated in the literature representing enhancements in the performance of optimisation algorithms. Here we introduce an evolutionary algorithm inspired by animal hunting groups (i.e. wolves). The algorithm implicitly maintains diversity in the population and performs higher than two state of the art evolutionary algorithms in the investigated case studies in this article. The case studies are to evolve a hormone-inspired system called AHHS (Artificial Homeostatic Hormone Systems) to develop spatial patterns. The complex spatial patterns are developed in the absence of any explicit spatial information. The results achieved by AHHS are presented and compared with a previous work with Artificial Neural Network (ANNs) indicating higher performance of AHHS.}, notes = {Also known as \cite{2598262} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Ahmed:2014:GECCOa, author = {Soha Ahmed and Mengjie Zhang and Lifeng Peng and Bing Xue}, title = {Multiple feature construction for effective biomarker identification and classification using genetic programming}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {249--256}, keywords = {genetic algorithms, genetic programming}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598292}, doi = {doi:10.1145/2576768.2598292}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Biomarker identification, i.e., detecting the features that indicate differences between two or more classes, is an important task in omics sciences. Mass spectrometry (MS) provide a high throughput analysis of proteomic and metabolomic data. The number of features of the MS data sets far exceeds the number of samples, making biomarker identification extremely difficult. Feature construction can provide a means for solving this problem by transforming the original features to a smaller number of high-level features. This paper investigates the construction of multiple features using genetic programming (GP) for biomarker identification and classification of mass spectrometry data. In this paper, multiple features are constructed using GP by adopting an embedded approach in which Fisher criterion and p-values are used to measure the discriminating information between the classes. This produces nonlinear high-level features from the low-level features for both binary and multi-class mass spectrometry data sets. Meanwhile, seven different classifiers are used to test the effectiveness of the constructed features. The proposed GP method is tested on eight different mass spectrometry data sets. The results show that the high-level features constructed by the GP method are effective in improving the classification performance in most cases over the original set of features and the low-level selected features. In addition, the new method shows superior performance in terms of biomarker detection rate.}, notes = {Also known as \cite{2598292} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Bautista:2014:GECCO, author = {Eddy J. Bautista and Ranjan Srivastava}, title = {Enhancing genetic algorithm-based genome-scale metabolic network curation efficiency}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {257--264}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598218}, doi = {doi:10.1145/2576768.2598218}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Genome-scale metabolic modelling using constraint-based analysis is a powerful modelling paradigm for simulating metabolic networks. Models are generated via inference from genome annotations. However, errors in the annotation or the identity of a gene's function could lead to 'metabolic inconsistency' rendering simulations infeasible. Uncovering the source of metabolic inconsistency is non-trivial due to network size and complexity. Recently published work uses genetic algorithms for curation by generating pools of models with randomly relaxed mass balance constraints. Models are evolved that allow feasible simulation while minimising the number of constraints relaxed. Relaxed constraints represent metabolites likely to be the root of metabolic inconsistency. Although effective, the approach can result in numerous false positives. Here we present a strategy, MassChecker, which evaluates all of the relaxed mass balance constraints in each generation prior to the next round of evolution to determine if they had become consistent due to recombination/mutation. If so, these constraints are enforced. This approach was applied to the development of genome-scale metabolic model of B. anthracis. The model consisted of 1,049 reactions and 1,003 metabolites. The result was a 60percent reduction in the number of relaxed mass balance constraints, significantly speeding up the curation process.}, notes = {Also known as \cite{2598218} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Carter:2014:GECCO, author = {Joi Carter and Daniel Beck and Henry Williams and Gerry Dozier and James A. Foster}, title = {GA-based selection of vaginal microbiome features associated with bacterial vaginosis}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {265--268}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598378}, doi = {doi:10.1145/2576768.2598378}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper, we successfully apply GEFeS (Genetic & Evolutionary Feature Selection) to identify the key features in the human vaginal microbiome and in patient meta-data that are associated with bacterial vaginosis (BV). The vaginal microbiome is the community of bacteria found in a patient, and meta-data include behavioural practices and demographic information. Bacterial vaginosis is a disease that afflicts nearly one third of all women, but the current diagnostics are crude at best. We describe two types of classifies for BV diagnosis, and show that each is associated with one of two treatments. Our results show that the classifiers associated with the 'Treat Any Symptom' version have better performances that the classifier associated with the 'Treat Based on N-Score Value'. Our long term objective is to develop a more accurate and objective diagnosis and treatment of BV.}, notes = {Also known as \cite{2598378} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Golan:2014:GECCO, author = {Rotem Golan and Christian Jacob and Savraj Grewal and J\"{o}rg Denzinger}, title = {Predicting patterns of gene expression during drosophila embryogenesis}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {269--276}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598250}, doi = {doi:10.1145/2576768.2598250}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Understanding how organisms develop from a single cell into a functioning multicellular organism is one of the key questions in developmental biology. Research in this area goes back decades ago, but only recently have improvements in technology allowed biologists to achieve experimental results that are more quantitative and precise. Here, we show how large biological datasets can be used to learn a model for predicting the patterns of gene expression in Drosophila melanogaster (fruit fly) throughout embryogenesis. We also explore the possibility of considering spatial information in order to achieve unique patterns of gene expression in different regions along the anterior-posterior (head-tail) axis of the egg. We then demonstrate how the resulting model can be used to (1) classify these regions into the various segments of the fly, and (2) to conduct a virtual gene knockout experiment. Our learning algorithm is based on a model that has biological meaning, which indicates that its structure and parameters have their correspondence in biology.}, notes = {Also known as \cite{2598250} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Ibragimov:2014:GECCO, author = {Rashid Ibragimov and Maximilian Malek and Jan Baumbach and Jiong Guo}, title = {Multiple graph edit distance: simultaneous topological alignment of multiple protein-protein interaction networks with an evolutionary algorithm}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {277--284}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598390}, doi = {doi:10.1145/2576768.2598390}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Motivation: We address the problem of multiple protein-protein interaction (PPI) network alignment. Given a set of such networks for different species we might ask how much the network topology is conserved throughout evolution. Solving this problem will help to derive a subset of interactions that is conserved over multiple species thus forming a 'core interactome'. Methods: We model the problem as Topological Multiple one-to-one Network Alignment (TMNA), where we aim to minimise the total Graph Edit Distance (GED) between pairs of the input networks. Here, the GED between two graphs is the number of deleted and inserted edges that are required to make one graph isomorphic to another. By minimising the GED we indirectly maximise the number of edges that are aligned in multiple networks simultaneously. However, computing an optimal GED value is computationally intractable. We thus propose an evolutionary algorithm and developed a software tool, GEDEVO-M, which is able to align multiple PPI networks using topological information only. We demonstrate the power of our approach by computing a maximal common subnetwork for a set of bacterial and eukaryotic PPI networks. GEDEVO-M thus provides great potential for computing the 'core interactome' of different species. Availability: http://gedevo.mpi-inf.mpg.de/multiple-network-alignment/.}, notes = {Also known as \cite{2598390} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Barry:2014:GECCO, author = {William Barry and Brian J. Ross}, title = {Virtual photography using multi-objective particle swarm optimization}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {285--292}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598210}, doi = {doi:10.1145/2576768.2598210}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Particle swarm optimisation (PSO) is a stochastic population-based search algorithm that is inspired by the flocking behaviour of birds. Here, a PSO is used to implement swarms of cameras flying through a virtual world in search of an image that satisfies a set of compositional objectives, for example, the rule of thirds and horizon line rules. To effectively process these multiple, and possible conflicting, criteria, a new multi-objective PSO algorithm called the sum of ranks PSO (SR-PSO) is introduced. The SR-PSO is useful for solving high-dimensional search problems, while discouraging degenerate solutions that can arise with other approaches. Less user intervention is required for the SR-PSO, as compared to a conventional PSO. A number of problems using different virtual worlds and user-supplied objectives were studied. In all cases, solution images were obtained that satisfied the majority of given objectives. The SR-PSO is shown to be superior to other algorithms in solving the high-dimensional virtual photography problems studied.}, notes = {Also known as \cite{2598210} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Jacobsen:2014:GECCO, author = {Emil Juul Jacobsen and Rasmus Greve and Julian Togelius}, title = {Monte Mario: platforming with MCTS}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {293--300}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598392}, doi = {doi:10.1145/2576768.2598392}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Monte Carlo Tree Search (MCTS) is applied to control the player character in a clone of the popular platform game Super Mario Bros. Standard MCTS is applied through search in state space with the goal of moving the furthest to the right as quickly as possible. Despite parameter tuning, only moderate success is reached. Several modifications to the algorithm are then introduced specifically to deal with the behavioural pathologies that were observed. Two of the modifications are to our best knowledge novel. A combination of these modifications is found to lead to almost perfect play on linear levels. Furthermore, when adding noise to the benchmark, MCTS outperforms the best known algorithm for these levels. The analysis and algorithmic innovations in this paper are likely to be useful when applying MCTS to other video games.}, notes = {Also known as \cite{2598392} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Machado:2014:GECCO, author = {Penousal Machado and Jo\,{a}o Correia}, title = {Semantic aware methods for evolutionary art}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {301--308}, keywords = {genetic algorithms, genetic programming}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598293}, doi = {doi:10.1145/2576768.2598293}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In the past few years the use of semantic aware crossover and mutation has become a hot topic of research within the Genetic Programming community. Unlike traditional genetic operators that perform syntactic manipulations of programs regardless of their behavior, semantic driven operators promote direct search on the underlying behavioral space. Based on previous work on semantic Genetic Programming and Genetic Morphing, we propose and implement semantic driven crossover and mutation operators for evolutionary art. The experimental results focus on assessing how these operators compare with traditional ones.}, notes = {Also known as \cite{2598293} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Macret:2014:GECCO, author = {Matthieu Macret and Philippe Pasquier}, title = {Automatic design of sound synthesizers as pure data patches using coevolutionary mixed-typed cartesian genetic programming}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {309--316}, keywords = {genetic algorithms, genetic programming, cartesian genetic programming}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598303}, doi = {doi:10.1145/2576768.2598303}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {A sound synthesizer can be defined as a program that takes a few input parameters and returns a sound. The general sound synthesis problem could then be formulated as: given a sound (or a set of sounds) what program and set of input parameters can generate that sound (set of sounds)? We propose a novel approach to tackle this problem in which we represent sound synthesisers using Pure Data (Pd), a graphic programming language for digital signal processing. We search the space of possible sound synthesisers using Coevolutionary Mixed-typed Cartesian Genetic Programming (MT-CGP), and the set of input parameters using a standard Genetic Algorithm (GA). The proposed algorithm co-evolves a population of MT-CGP graphs, representing the functional forms of synthesisers, and a population of GA chromosomes, representing their inputs parameters. A fitness function based on the Mel-frequency Cepstral Coefficients (MFCC) evaluates the distance between the target and produced sounds. Our approach is capable of suggesting novel functional forms and input parameters, suitable to approximate a given target sound (and we hope in future iterations a set of sounds). Since the resulting synthesizers are presented as Pd patches, the user can experiment, interact with, and reuse them.}, notes = {Also known as \cite{2598303} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Nallaperuma:2014:GECCO, author = {Samadhi Nallaperuma and Frank Neumann and Mohammad Reza Bonyadi and Zbigniew Michalewicz}, title = {EVOR: an online evolutionary algorithm for car racing games}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {317--324}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598298}, doi = {doi:10.1145/2576768.2598298}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper, we present evolutionary racer (EVOR) a simulated car dynamically controlled by an online evolutionary algorithm (EA). The key distinction between EVOR and earlier car racing methods is that it considers car racing as a dynamic optimisation problem and is addressed by an evolutionary algorithm. Our approach calculates a car trajectory based on a controller decision and adjusts this decision according to the suitability of its resultant trajectory with the current track status. Furthermore, it allows to integrate features such as opponent handling implicitly. Our experimental results show that EVOR outperforms current best AI controllers on a wide range of tracks.}, notes = {Also known as \cite{2598298} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Schrum:2014:GECCO, author = {Jacob Schrum and Risto Miikkulainen}, title = {Evolving multimodal behavior with modular neural networks in Ms. Pac-Man}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {325--332}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598234}, doi = {doi:10.1145/2576768.2598234}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Ms. Pac-Man is a challenging video game in which multiple modes of behaviour are required to succeed: Ms. Pac-Man must escape ghosts when they are threats, and catch them when they are edible, in addition to eating all pills in each level. Past approaches to learning behaviour in Ms. Pac-Man have treated the game as a single task to be learnt using monolithic policy representations. In contrast, this paper uses a framework called Modular Multiobjective NEAT to evolve modular neural networks. Each module defines a separate policy; evolution discovers these policies and when to use them. The number of modules can be fixed or learned using a new version of a genetic operator, called Module Mutation, which duplicates an existing module that can then evolve to take on a distinct behavioural identity. Both the fixed modular networks and Module Mutation networks outperform traditional monolithic networks. More interestingly, the best modular networks dedicate modules to critical behaviours that do not follow the customary division of the game into chasing edible and escaping threatening ghosts.}, notes = {Also known as \cite{2598234} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Christie:2014:GECCO, author = {Lee A. Christie and John A.W. McCall and David P. Lonie}, title = {Minimal walsh structure and ordinal linkage of monotonicity-invariant function classes on bit strings}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {333--340}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598240}, doi = {doi:10.1145/2576768.2598240}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Problem structure, or linkage, refers to the interaction between variables in a black-box fitness function. Discovering structure is a feature of a range of algorithms, including estimation of distribution algorithms (EDAs) and perturbation methods (PMs). The complexity of structure has traditionally been used as a broad measure of problem difficulty, as the computational complexity relates directly to the complexity of structure. The EDA literature describes necessary and unnecessary interactions in terms of the relationship between problem structure and the structure of probabilistic graphical models discovered by the EDA. In this paper we introduce a classification of problems based on monotonicity invariance. We observe that the minimal problem structures for these classes often reveal that significant proportions of detected structures are unnecessary. We perform a complete classification of all functions on 3 bits. We consider nonmonotonicity linkage discovery using perturbation methods and derive a concept of directed ordinal linkage associated to optimisation schedules. The resulting refined classification factored out by relabelling, shows a hierarchy of nine directed ordinal linkage classes for all 3-bit functions. We show that this classification allows precise analysis of computational complexity and parallelizability and conclude with a number of suggestions for future work.}, notes = {Also known as \cite{2598240} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Cox:2014:GECCO, author = {Chris R. Cox and Richard A. Watson}, title = {Solving building block problems using generative grammar}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {341--348}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598259}, doi = {doi:10.1145/2576768.2598259}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this work we demonstrate novel applications of generative grammar to evolutionary search. We introduce a class of grammar that can represent hierarchical schema structure in a problem space, and describe an algorithm that can infer an instance of the grammar from a population of sample phenotypes. Unlike conventional sequence-based grammars this grammar represents set-membership relationships, not strings, and is therefore insensitive to gene-ordering and physical linkage. We show that these methods are capable of accurately identifying problem structure from populations of above-average-fitness individuals on simple modular and hierarchically modular test problems. We then show how these grammatical models can be used to aid evolutionary problem solving by enabling facilitated variation; specifically, by producing novel combinations of schemata observed in the sample population whilst respecting the inherent constraint structure of the problem space. This provides a robust method of building-block recombination that is linkage-invariant and not restricted to low-order schemata.}, notes = {Also known as \cite{2598259} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Helmi:2014:GECCO, author = {Bentolhoda Helmi and Adel Torkaman Rahmani}, title = {Estimation of distribution algorithm using factor graph and Markov blanket canonical factorization}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {349--356}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598266}, doi = {doi:10.1145/2576768.2598266}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Finding a good model and efficiently estimating the distribution is still an open challenge in estimation of distribution algorithms (EDAs). Factorisation encoded by models in most of the EDAs are constrained. However for optimisation of many real-world problems, finding the model capable of representing complex interactions without much computational complexity overhead is the key challenge. On the other hand factor graph which is the most natural graphical model for representing additively decomposable functions is rarely employed in EDAs. In this paper we introduce Factor Graph based EDA (FGEDA) which learns factor graph as the model and estimate the probability distribution represented by the learnt factor graph using Markov blanket canonical factorisation. The class of factorization that is employed for approximation of distribution in FGEDA is expanded relative to famous EDAs. We have used matrix factorization for learning the factor graph of the problem based on the pairwise mutual information between pair of variables. Gibbs sampling and BB-wise crossover are used to generate new samples. Empirical evaluation as well as theoretical analysis of the approach show the efficiency and power of FGEDA in the optimisation of functions with complex interactions. It is showed experimentally that FGEDA outperform other well-known EDAs.}, notes = {Also known as \cite{2598266} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Luong:2014:GECCO, author = {Ngoc Hoang Luong and Han {La Poutr\'{e}} and Peter A.N. Bosman}, title = {Multi-objective gene-pool optimal mixing evolutionary algorithms}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {357--364}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598261}, doi = {doi:10.1145/2576768.2598261}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The recently introduced Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA), with a lean, but sufficient, linkage model and an efficient variation operator, has been shown to be a robust and efficient methodology for solving single objective (SO) optimisation problems with superior performance compared to classic genetic algorithms (GAs) and estimation-of-distribution algorithms (EDAs). In this paper, we bring the strengths of GOMEAs to the multi-objective (MO) optimisation realm. To this end, we modify the linkage learning procedure and the variation operator of GOMEAs to better suit the need of finding the whole Pareto-optimal front rather than a single best solution. Based on state-of-the-art studies on MOEAs, we further pinpoint and incorporate two other essential components for a scalable MO optimiser. First, the use of an elitist archive is beneficial for keeping track of non-dominated solutions when the main population size is limited. Second, clustering can be crucial if different parts of the Pareto-optimal front need to be handled differently. By combining these elements, we construct a multi-objective GOMEA (MO-GOMEA). Experimental results on various MO optimization problems confirm the capability and scalability of our MO-GOMEA that compare favourably with those of the well-known GA NSGA-II and the more recently introduced EDA mohBOA.}, notes = {Also known as \cite{2598261} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Martins:2014:GECCO, author = {Jean P. Martins and Alexandre C.B. Delbem}, title = {Multimodality and the linkage-learning difficulty of additively separable functions}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {365--372}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598281}, doi = {doi:10.1145/2576768.2598281}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Estimation of Distribution Algorithms (EDAs) have emerged from the synergy between machine-learning techniques and Genetic Algorithms (GAs). EDAs rely on probabilistic modelling for obtaining information about the underlying structure of optimisation problems and implementing effective reproduction operators. The effectiveness of EDAs depends on the capacity of the model-building to extract reliable information about the problem. In this study we analyse additively separable functions and argue that the degree of multimodality of such functions defines their linkage-learning difficulty. Besides, by using entropy-based concepts and Jensen's inequality, we show how allelic pairwise independence may appear as a consequence of an increasing multimodality. The results characterise the linkage-learning difficulty of well-known functions, like the deceptive trap, bipolar and concatenated parity.}, notes = {Also known as \cite{2598281} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Akimoto:2014:GECCO, author = {Youhei Akimoto and Anne Auger and Nikolaus Hansen}, title = {Comparison-based natural gradient optimization in high dimension}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {373--380}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598258}, doi = {doi:10.1145/2576768.2598258}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We propose a novel natural gradient based stochastic search algorithm, VD-CMA, for the optimisation of high dimensional numerical functions. The algorithm is comparison-based and hence invariant to monotonic transformations of the objective function. It adapts a multivariate normal distribution with a restricted covariance matrix with twice the dimension as degrees of freedom, representing an arbitrarily oriented long axis and additional axis-parallel scaling. We derive the different components of the algorithm and show linear internal time and space complexity. We find empirically that the algorithm adapts its covariance matrix to the inverse Hessian on convex-quadratic functions with an Hessian with one short axis and different scaling on the diagonal. We then evaluate VD-CMA on test functions and compare it to different methods. On functions covered by the internal model of VD-CMA and on the Rosenbrock function, VD-CMA outperforms CMA-ES (having quadratic internal time and space complexity) not only in internal complexity but also in number of function calls with increasing dimension.}, notes = {Also known as \cite{2598258} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Au:2014:GECCO, author = {Chun-Kit Au and Ho-Fung Leung}, title = {Halfspace sampling in evolution strategies}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {381--388}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598335}, doi = {doi:10.1145/2576768.2598335}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper presents a novel half-space sampling method in single parent elitist evolution strategies (ESs) for unimodal functions. In half space sampling, the supporting hyperplane going through a parent separates the search space into a positive halfspace and a negative halfspace. If an offspring lies in the negative halfspace, it will be reflected with respect to the parent so that it lies in the positive halfspace. We derive the convergence rates of a scale-invariant step size (1+1)-ES with halfspace sampling on spherical functions in finite and infinite dimensions. We prove that the lower bounds of convergence rates are improved by a factor of 2 when strategies sample their offspring in the optimal positive halfspace. We also implement halfspace sampling into the (1+1) CMA-ES by introducing the concept of evolution halfspaces. Evolution halfspaces accumulate the significant information of the previous successful and unsuccessful steps in order to estimate the optimal positive halfspace. The (1+1)-CMA-ES with halfspace sampling is benchmarked on the BBOB noise-free testbed and experimentally compared with the standard (1+1)-CMA-ES.}, notes = {Also known as \cite{2598335} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Glasmachers:2014:GECCO, author = {Tobias Glasmachers}, title = {Handling sharp ridges with local supremum transformations}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {389--396}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598215}, doi = {doi:10.1145/2576768.2598215}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {A particular strength of many evolution strategies is their invariance against strictly monotonic and therefore rank-preserving transformations of the objective function. Their view onto a continuous fitness landscape is therefore completely determined by the shapes of the level sets. Most modern algorithms can cope well with diverse shapes as long as these are sufficiently smooth. In contrast, the sharp angles found in level sets of ridge functions can cause premature convergence to a non-optimal point. We propose a simple and generic family of transformation of the fitness function to avoid this effect. This allows general purpose evolution strategies to solve even extremely sharp ridge problems.}, notes = {Also known as \cite{2598215} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Loshchilov:2014:GECCO, author = {Ilya Loshchilov}, title = {A computationally efficient limited memory CMA-ES for large scale optimization}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {397--404}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598294}, doi = {doi:10.1145/2576768.2598294}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We propose a computationally efficient limited memory Covariance Matrix Adaptation Evolution Strategy for large scale optimisation, which we call the LM-CMA-ES. The LM-CMA-ES is a stochastic, derivative-free algorithm for numerical optimisation of non-linear, non-convex optimisation problems in continuous domain. Inspired by the limited memory BFGS method of Liu and Nocedal (1989), the LM-CMA-ES samples candidate solutions according to a covariance matrix reproduced from m direction vectors selected during the optimisation process. The decomposition of the covariance matrix into Cholesky factors allows to reduce the time and memory complexity of the sampling to O(mn), where $n$ is the number of decision variables. When $n$ is large (e.g., n > 1000), even relatively small values of $m$ (e.g., m=20,30) are sufficient to efficiently solve fully non-separable problems and to reduce the overall run-time.}, notes = {Also known as \cite{2598294} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Andrade:2014:GECCO, author = {Carlos E. Andrade and Mauricio G.C. Resende and Howard J. Karloff and Fl\'{a}vio K. Miyazawa}, title = {Evolutionary algorithms for overlapping correlation clustering}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {405--412}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598284}, doi = {doi:10.1145/2576768.2598284}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In Overlapping Correlation Clustering (OCC), a number of objects are assigned to clusters. Two objects in the same cluster have correlated characteristics. As opposed to traditional clustering where objects are assigned to a single cluster, in OCC objects may be assigned to one or more clusters. In this paper, we present Biased Random-Key Genetic Algorithms for OCC. We present computational experiments such results outperformed the state of art methods for OCC.}, notes = {Also known as \cite{2598284} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Basseur:2014:GECCO, author = {Matthieu Basseur and Adrien Go\"{e}ffon}, title = {On the efficiency of worst improvement for climbing NK-landscapes}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {413--420}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598268}, doi = {doi:10.1145/2576768.2598268}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Climbers are often used in metaheuristics in order to intensify the search and identify local optima with respect to a neighbourhood structure. Even if they constitute a central component of modern heuristics, their design principally consists in choosing the pivoting rule, which is often reduced to two alternative strategies: first improvement or best improvement. The conception effort of most metaheuristics belongs in proposing techniques to escape from local optima, and not necessarily on how to climb toward better local optima. In this paper, we are interested in attaining good local optima with basic hill-climbing techniques. The NK model will be used to evaluate a set of climbers proposed in this paper. By focusing on the pivoting rule definition, we show that choosing the worst improving neighbour often leads to attain better local optima. Moreover, by slightly modifying the worst improvement strategy, one can design efficient climbers which outperform first and best improvement in terms of tradeoff between quality and computational effort.}, notes = {Also known as \cite{2598268} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Bonyadi:2014:GECCOa, author = {Mohammad Reza Bonyadi and Zbigniew Michalewicz and Micha\u{o} Roman Przyby\u{o}ek and Adam Wierzbicki}, title = {Socially inspired algorithms for the travelling thief problem}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {421--428}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598367}, doi = {doi:10.1145/2576768.2598367}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Many real-world problems are composed of two or more problems that are interdependent on each other. The interaction of such problems usually is quite complex and solving each problem separately cannot guarantee the optimal solution for the overall multi-component problem. In this paper we experiment with one particular 2-component problem, namely the Travelling Thief Problem (TTP). TTP is composed of the Traveling Salesman Problem (TSP) and the Knapsack Problem (KP). We investigate two heuristic methods to deal with TTP. In the first approach we decompose TTP into two sub-problems, solve them by separate modules/algorithms (that communicate with each other), and combine the solutions to obtain an overall approximated solution to TTP (this method is called CoSolver ). The second approach is a simple heuristic (called density-based heuristic, DH) method that generates a solution for the TSP component first (a version of Lin-Kernighan algorithm is used) and then, based on the fixed solution for the TSP component found, it generates a solution for the KP component (associated with the given TTP). In fact, this heuristic ignores the interdependency between sub-problems and tries to solve the sub-problems sequentially. These two methods are applied to some generated TTP instances of different sizes. Our comparisons show that CoSolver outperforms DH specially in large instances.}, notes = {Also known as \cite{2598367} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{CunhaCampos:2014:GECCO, author = {Saulo {Cunha Campos} and Jos\'{e} Elias {Claudio Arroyo}}, title = {NSGA-II with iterated greedy for a bi-objective three-stage assembly flowshop scheduling problem}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {429--436}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598324}, doi = {doi:10.1145/2576768.2598324}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper we address a three-stage assembly flowshop scheduling problem where there are m machines at the first stage, a transportation machine at the second stage and an assembly machine at the third stage. At the first stage, different parts of a product are manufactured independently on parallel production lines. At the second stage, the manufactured parts are collected and transferred to the next stage. At the third stage, the parts are assembled into final products. The objective is to schedule n jobs on the machines so that total flow time and the total tardiness of the jobs are minimised simultaneously. This problem has many applications in industry and belongs to the class of NP-Hard combinatorial optimisation problems. In order to obtain near Pareto optimal solutions, we propose an Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) coupled with Iterated Greedy (IG) strategy. IG is a simple heuristic that has shown excellent results for different flow-shop scheduling problems. A comparative study is presented between the results obtained using the standard NSGA-II, the enhanced NSGA-II with IG approach and a single-objective GRASP heuristic. Experimental results on both medium and large size of instances show the efficiency of the hybrid NSGA-II approach.}, notes = {Also known as \cite{2598324} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Chicano:2014:GECCO, author = {Francisco Chicano and Darrell Whitley and Andrew M. Sutton}, title = {Efficient identification of improving moves in a ball for pseudo-boolean problems}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {437--444}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598304}, doi = {doi:10.1145/2576768.2598304}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Hill climbing algorithms are at the core of many approaches to solve optimisation problems. Such algorithms usually require the complete enumeration of a neighbourhood of the current solution. In the case of problems defined over binary strings of length n, we define the r-ball neighborhood as the set of solutions at Hamming distance r or less from the current solution. For r ll n this neighborhood contains Theta(nr) solutions. In this paper efficient methods are introduced to locate improving moves in the r-ball neighbourhood for problems that can be written as a sum of a linear number of sub-functions depending on a bounded number of variables. NK-landscapes and MAX-kSAT are examples of these problems. If the number of subfunctions depending on any given variable is also bounded, then we prove that the method can explore the neighbourhood in constant time, despite the fact that the number of solutions in the neighbourhood is polynomial in n. We develop a hill climber based on our exploration method and we analyse its efficiency and efficacy using experiments with NKq-landscapes instances.}, notes = {Also known as \cite{2598304} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Choi:2014:GECCO, author = {HyukGeun Choi and JinHyun Kim and Byung-Ro Moon}, title = {A hybrid incremental genetic algorithm for subgraph isomorphism problem}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {445--452}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598382}, doi = {doi:10.1145/2576768.2598382}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Finding an isomorphic subgraph is a key problem in many real world applications modelled on graph. In this paper, we propose a new hybrid genetic algorithm(GA) for subgraph isomorphism problem which uses an incremental approach. We solve the problem with increasing the size of the subproblem step by step. The graph for which we search is gradually expanded from the empty structure to the entire one. We apply a hybrid GA to each subproblem, initialised with the evolved population of previous step. We present design issues for the incremental approach, and the effects of each design decision are analysed by experiment. The proposed algorithm is tested on widely used dataset. With apposite vertex reordering along with moderate population diversity, incremental approach brought a significant performance improvement. Experimental results showed that our algorithm outperformed representative previous works.}, notes = {Also known as \cite{2598382} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Iclanzan:2014:GECCO, author = {David Iclanzan and Fabio Daolio and Marco Tomassini}, title = {Data-driven local optima network characterization of QAPLIB instances}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {453--460}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598275}, doi = {doi:10.1145/2576768.2598275}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Inherent networks of potential energy surfaces proposed in physical chemistry inspired a compact network characterisation of combinatorial fitness landscapes. In these so-called Local Optima Networks (LON), the nodes correspond to the local optima and the edges quantify a measure of adjacency - transition probability between them. Methods so far used an exhaustive search for extracting LON, limiting their applicability to small problem instances only. To increase scalability, in this paper a new data-driven methodology is proposed that approximates the LON from actual runs of search methods. The method enables the extraction and study of LON corresponding to the various types of instances from the Quadratic Assignment Problem Library (QAPLIB), whose search spaces are characterised in terms of local minima connectivity. Our analysis provides a novel view of the unified testbed of QAP combinatorial landscapes used in the literature, revealing qualitative inherent properties that can be used to classify instances and estimate search difficulty.}, notes = {Also known as \cite{2598275} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Meignan:2014:GECCO, author = {David Meignan}, title = {A heuristic approach to schedule reoptimization in the context of interactive optimization}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {461--468}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598213}, doi = {doi:10.1145/2576768.2598213}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Optimisation models used in planning and scheduling systems are not exempt from inaccuracies. These optimisation systems often require an expert to assess solutions and to adjust them before taking decisions. However, adjusting a solution computed by an optimization procedure is difficult, especially because of the cascading effect. A small modification in a candidate solution may require to modify a large part of the solution. This obstacle to the adjustment of a solution can be overcome by interactive reoptimization. In this paper we analyse the impact of the cascading effect on a shift-scheduling problem and propose an efficient heuristic approach for re-optimising solutions. The proposed approach is a local-search metaheuristic that has been adapted to the reoptimisation. This approach is evaluated on a set of problem instances on which additional preferences are generated to simulate desired adjustments of a decision maker. Experimental results indicate that, even with a small perturbation, the cascading effect is manifest and cannot be efficiently tackled by applying recovery actions. Moreover, results show that the proposed reoptimisation method provides significant cost gains within a short time while keeping a level of simplicity and modularity adequate for an implementation in a decision support system.}, notes = {Also known as \cite{2598213} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Monical:2014:GECCO, author = {Cara Monical and Forrest Stonedahl}, title = {Static vs. dynamic populations in genetic algorithms for coloring a dynamic graph}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {469--476}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598233}, doi = {doi:10.1145/2576768.2598233}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We studied the performance of genetic algorithms for colouring dynamic graphs under a variety of experimental conditions, focusing on the relationship between the dynamics of the graph and that of the algorithm. Graph colouring is a well-studied NP-hard problem, while dynamic graphs are a natural way to model a diverse range of dynamic systems. Dynamic graph coloring can be applied to online scheduling in a changing environment, such as the online scheduling of conflicting tasks. As genetic algorithms (GAs) have been effective for graph coloring and are adaptable to dynamic environments, they are a promising choice for this problem. Thus, we compared the performance of three algorithms: a GA that maintained a single population adapting to the dynamic graph (DGA), a GA that restarted with a fresh population for the static graph of each time-step (SGA), and DSATUR, a well-known heuristic graph colouring algorithm re-applied at each time-step. We examined the relative performance of these algorithms for dynamic graphs of different sizes, edge densities, structures, and change rates, using different amounts of evolution between time-steps. Overall, the DGA consistently outperformed the SGA, being particularly dominant at low change rates, and under certain conditions was able to outperform DSATUR.}, notes = {Also known as \cite{2598233} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Polyakovskiy:2014:GECCO, author = {Sergey Polyakovskiy and Mohammad Reza Bonyadi and Markus Wagner and Zbigniew Michalewicz and Frank Neumann}, title = {A comprehensive benchmark set and heuristics for the traveling thief problem}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {477--484}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598249}, doi = {doi:10.1145/2576768.2598249}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Real-world optimisation problems often consist of several NP-hard optimisation problems that interact with each other. The goal of this paper is to provide a benchmark suite that promotes a research of the interaction between problems and their mutual influence. We establish a comprehensive benchmark suite for the travelling thief problem (TTP) which combines the traveling salesman problem and the knapsack problem. Our benchmark suite builds on common benchmarks for the two sub-problems which grant a basis to examine the potential hardness imposed by combining the two classical problems. Furthermore, we present some simple heuristics for TTP and their results on our benchmark suite.}, notes = {Also known as \cite{2598249} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{ShenoyKB:2014:GECCO, author = {Ajitha {Shenoy K B} and Somenath Biswas and Piyush P. Kurur}, title = {Performance of metropolis algorithm for the minimum weight code word problem}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {485--492}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598274}, doi = {doi:10.1145/2576768.2598274}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We study the performance of the Metropolis algorithm for the problem of finding a code word of weight less than or equal to M, given a generator matrix of an [n,k]-binary linear code. The algorithm uses the set Sk of all kxk invertible matrices as its search space where two elements are considered adjacent if one can be obtained from the other via an elementary row operation (i.e by adding one row to another or by swapping two rows.) We prove that the Markov chains associated with the Metropolis algorithm mix rapidly for suitable choices of the temperature parameter T. We ran the Metropolis algorithm for a number of codes and found that the algorithm performed very well in comparison to previously known experimental results.}, notes = {Also known as \cite{2598274} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Tantar:2014:GECCO, author = {Alexandru-Adrian Tantar and Emilia Tantar and Oliver Sch\"{u}tze}, title = {Asymmetric quadratic landscape approximation model}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {493--500}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598381}, doi = {doi:10.1145/2576768.2598381}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This work presents an asymmetric quadratic approximation model and an $\epsilon$-archiving algorithm. The model allows to construct, under local convexity assumptions, descriptors for local optima points in continuous functions. A descriptor can be used to extract confidence radius information. The $\epsilon$-archiving algorithm is designed to maintain and update a set of such asymmetric descriptors, spaced at some given threshold distance. An in-depth analysis is conducted on the stability and performance of the asymmetric model, comparing the results with the ones obtained by a quadratic polynomial approximation. A series of different applications are possible in areas such as dynamic and robust optimisation.}, notes = {Also known as \cite{2598381} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Tinos:2014:GECCO, author = {Renato Tin\'{o}s and Darrell Whitley and Gabriela Ochoa}, title = {Generalized asymmetric partition crossover (GAPX) for the asymmetric TSP}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {501--508}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598245}, doi = {doi:10.1145/2576768.2598245}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The Generalised Partition Crossover (GPX) constructs new solutions for the Travelling Salesman Problem (TSP) by finding recombining partitions with one entry and one exit in the graph composed by the union of two parent solutions. If there are k recombining partitions in the union graph, 2^k-2 solutions are simultaneously exploited by GPX. Generalised Asymmetric Partition Crossover (GAPX) is introduced; it finds more recombining partitions and can also find partitions for the asymmetric TSP. GAPX does this by locating partitions that cut vertices of degree 4 in the union graph and by finding partitions with multiple entry and exit points, both in O(n) time. GAPX can improve the quality of solutions generated by the Lin-Kernighan-Helsgaun heuristic and improve the state of the art for the asymmetric TSP.}, notes = {Also known as \cite{2598245} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Witt:2014:GECCO, author = {Carsten Witt}, title = {Revised analysis of the (1+1) ea for the minimum spanning tree problem}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {509--516}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598237}, doi = {doi:10.1145/2576768.2598237}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We revisit the classical analysis of the (1+1) EA for the minimum spanning tree problem in the case that nothing is known about the weights of the underlying graph. Here the original upper bound on the expected running time by Neumann and Wegener [Theor. Comput. Sci. 378(1), 32-40, 2007], which depends on the largest weight of the graph, is of no use. The best upper bound available before in this case is due to Reichel and Skutella [FOGA 2009, 21-28] and is of order O(m3 \log n), where m is the number of edges and n the number of vertices. Using an adaptive drift analysis, we show the improved bound O(m2 (sqrt{c(G)} + \log n)), where c(G) is the circumference (length of the longest cycle) of the graph. This is only by an asymptotic factor of at most sqrt{n}/\log n away from the classical lower bound. Furthermore, an alternative fitness function leading to the bound O(m2\log n) is proposed, and limitations of the adaptive drift analysis are pointed out.}, notes = {Also known as \cite{2598237} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Garcia-Limon:2014:GECCO, author = {Mauricio Garcia-Limon and Hugo Jair Escalante and Eduardo Morales and Alicia Morales-Reyes}, title = {Simultaneous generation of prototypes and features through genetic programming}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {517--524}, keywords = {genetic algorithms, genetic programming}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598356}, doi = {doi:10.1145/2576768.2598356}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Nearest-neighbour (NN) methods are highly effective and widely used pattern classification techniques. There are, however, some issues that hinder their application for large scale and noisy data sets; including, its high storage requirements, its sensitivity to noisy instances, and the fact that test cases must be compared to all of the training instances. Prototype (PG) and feature generation (FG) techniques aim at alleviating these issues to some extent; where, traditionally, both techniques have been implemented separately. This paper introduces a genetic programming approach to tackle the simultaneous generation of prototypes and features to be used for classification with a NN classifier. The proposed method learns to combine instances and attributes to produce a set of prototypes and a new feature space for each class of the classification problem via genetic programming. An heterogeneous representation is proposed together with ad-hoc genetic operators. The proposed approach overcomes some limitations of NN without degradation in its classification performance. Experimental results are reported and compared with several other techniques. The empirical assessment provides evidence of the effectiveness of the proposed approach in terms of classification accuracy and instance/feature reduction.}, notes = {Also known as \cite{2598356} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Iqbal:2014:GECCO, author = {Muhammad Iqbal and Syed S. Naqvi and Will N. Browne and Christopher Hollitt and Mengjie Zhang}, title = {Salient object detection using learning classifiersystems that compute action mappings}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {525--532}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598371}, doi = {doi:10.1145/2576768.2598371}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Learning classifier systems (LCSs) are rule-based online evolutionary machine learning techniques that solve a problem by interacting with an environment. LCSs have been successfully used in various applications such as data mining, robot control and computer vision systems. Salient object detection is the task of automatically localising the objects of interests in a scene by suppressing the background information, which facilitates various machine learning applications such as object segmentation, recognition and tracking. It is a difficult problem as natural scenes can often have objects with cluttered backgrounds (making it difficult to distinguish the object from background based on its features) or other complicating factors such as multiple objects. Existing saliency learning methods learn a single weight vector emphasising the importance of each feature/attribute for the whole image dataset, hence losing generalisation in the test phase when considering unseen images. LCS technique has the ability to learn weight sets for different types of images automatically. Hence, this paper investigates the application of LCS for learning image dependent feature fusion strategies for the task of salient object detection. Our LCS approach evolves generalised rules for a well known benchmark dataset consisting of 1000 images, of various types and difficulty levels, and outperforms a genetic algorithm based system that was previously state-of-the-art.}, notes = {Also known as \cite{2598371} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Kamath:2014:GECCO, author = {Uday Kamath and Jessica Lin and Kenneth {De Jong}}, title = {SAX-EFG: an evolutionary feature generation framework for time series classification}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {533--540}, keywords = {genetic algorithms, genetic programming}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598321}, doi = {doi:10.1145/2576768.2598321}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {A variety of real world applications fit into the broad definition of time series classification. Using traditional machine learning approaches such as treating the time series sequences as high dimensional vectors have faced the well known curse of dimensionality problem. Recently, the field of time series classification has seen success by using preprocessing steps that discretise the time series using a Symbolic Aggregate ApproXimation technique (SAX) and using recurring subsequences (motifs) as features. In this paper we explore a feature construction algorithm based on genetic programming that uses SAX-generated motifs as the building blocks for the construction of more complex features. The research shows that the constructed complex features improve the classification accuracy in a statistically significant manner for many applications.}, notes = {Also known as \cite{2598321} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Koutnik:2014:GECCO, author = {Jan Koutn\'{\i}k and Juergen Schmidhuber and Faustino Gomez}, title = {Evolving deep unsupervised convolutional networks for vision-based reinforcement learning}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {541--548}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598358}, doi = {doi:10.1145/2576768.2598358}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Dealing with high-dimensional input spaces, like visual input, is a challenging task for reinforcement learning (RL). Neuroevolution (NE), used for continuous RL problems, has to either reduce the problem dimensionality by (1) compressing the representation of the neural network controllers or (2) employing a pre-processor (compressor) that transforms the high-dimensional raw inputs into low-dimensional features. In this paper, we are able to evolve extremely small recurrent neural network (RNN) controllers for a task that previously required networks with over a million weights. The high-dimensional visual input, which the controller would normally receive, is first transformed into a compact feature vector through a deep, max-pooling convolutional neural network (MPCNN). Both the MPCNN preprocessor and the RNN controller are evolved successfully to control a car in the TORCS racing simulator using only visual input. This is the first use of deep learning in the context evolutionary RL.}, notes = {Also known as \cite{2598358} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Marzukhi:2014:GECCO, author = {Syahaneim Marzukhi and Will N. Browne and Mengjie Zhang}, title = {Three-cornered coevolution learning classifier systems for classification tasks}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {549--556}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598235}, doi = {doi:10.1145/2576768.2598235}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The Three-Cornered Coevolution concept describes a framework where artificial problems may be generated in concert with classification agents in order to provide insight into their relationships. This is unlike standard studies where humans set a problem's difficulty, which may have bias or lack understanding of the multiple interactions of a problem's characteristics, such as noise in conjunction with class imbalance. Previous studies have shown that it is feasible to generate problems with one agent in relation to a single classification agent's performance, but when to adjust the problem difficulty was manually set. This paper introduces a second classification agent to trigger the coevolutionary process within the system, where its functionality and effect on the system requires investigation. The classification agents, in this case Learning Classifier Systems, use different styles of learning techniques (e.g. supervised or reinforcement learning techniques) to learn the problems. Experiments show that the realised system is capable of autonomously generating various problems, triggering learning and providing insight into each learning system's ability by determining the problem domains where they perform relatively well - this is in contrast to humans having to determine the problem domains.}, notes = {Also known as \cite{2598235} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Nakata:2014:GECCO, author = {Masaya Nakata and Pier Luca Lanzi and Tim Kovacs and Keiki Takadama}, title = {Complete action map or best action map in accuracy-based reinforcement learning classifier systems}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {557--564}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598351}, doi = {doi:10.1145/2576768.2598351}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We study two existing Learning Classifier Systems (LCSs): XCS, which has a complete map (which covers all actions in each state), and XCSAMm, which has a best action map (which covers only the highest-return action in each state). This allows XCSAM to learn with a smaller population size limit (but larger population size) and to learn faster than XCS on well-behaved tasks. However, many tasks have difficulties like noise and class imbalances. XCS and XCSAM have not been compared on such problems before. This paper aims to discover which kind of map is more robust to these difficulties. We apply them to a classification problem (the multiplexer problem) with class imbalance, Gaussian noise or alternating noise (where we return the reward for a different action). We also compare them on real-world data from the UCI repository without adding noise. We analyse how XCSAM focuses on the best action map and introduce a novel deletion mechanism that helps to evolve classifiers towards a best action map. Results show the best action map is more robust (has higher accuracy and sometimes learns faster) in all cases except small amounts of alternating noise.}, notes = {Also known as \cite{2598351} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Nakata:2014:GECCOa, author = {Masaya Nakata and Tim Kovacs and Keiki Takadama}, title = {A modified XCS classifier system for sequence labeling}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {565--572}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598352}, doi = {doi:10.1145/2576768.2598352}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper introduces XCS-SL, an extension of XCS for sequence labelling, a form of time-series classification where every input has a class label. Specifically, we consider sequence labeling tasks where on each time step we receive an input/class pair. In sequence labeling the correct class of an input may depend on data received on previous time stamps, so a learner may need to refer to data at previous time stamps. That is, some classification rules (called classifiers' here) must include conditions on previous inputs (a kind of memory). We assume the agent does not know how many conditions on previous inputs are needed to classify the current input, and the number of conditions/memories needed may be different for each input. Hence, using a fixed number of conditions is not a good solution. A novel idea we introduce is classifiers that have a variable-length condition to refer back to data at previous times. The condition can grow and shrink to find a suitable memory size. On a benchmark problem XCS-SL can learn optimal classifiers, and on a real-world sequence labelling task, it derived high classification accuracy and discovered interesting knowledge that shows dependencies between inputs at different times.}, notes = {Also known as \cite{2598352} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Nalepa:2014:GECCO, author = {Jakub Nalepa and Michal Kawulok}, title = {A memetic algorithm to select training data for support vector machines}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {573--580}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598370}, doi = {doi:10.1145/2576768.2598370}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper we propose a new memetic algorithm (MASVM) for fast and efficient selection of a valuable training set for support vector machines (SVMs). This is a crucial step especially in case of large and noisy data sets, since the SVM training has high time and memory complexity. The majority of state-of-the-art methods exploit the data geometry analysis, both in the input and kernel space. Although evolutionary algorithms have been proved to be very efficient for this purpose, they have not been extensively studied so far. Here, we propose a new method employing an adaptive genetic algorithm enhanced by some refinement techniques. The refinements are based on using a pool of the support vectors identified so far at various steps of the algorithm. Extensive experimental study performed on the well-known benchmark, real-world and artificial data sets clearly confirms the efficacy, robustness and convergence capabilities of the proposed approach, and shows that it is competitive compared with other state-of-the-art techniques.}, notes = {Also known as \cite{2598370} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Azzouz:2014:GECCO, author = {Nessrine Azzouz and Slim Bechikh and Lamjed {Ben Said}}, title = {Steady state IBEA assisted by MLP neural networks for expensive multi-objective optimization problems}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {581--588}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598271}, doi = {doi:10.1145/2576768.2598271}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Several engineering problems involve simultaneously several objective functions where at least one of them is expensive to evaluate. This fact has yielded to a new class of Multi-Objective Problems (MOPs) called expensive MOPs. Several attempts have been conducted in the literature with the goal to minimise the number of expensive evaluations by using surrogate models stemming from the machine learning field. Usually, researchers substitute the expensive objective function evaluation by an estimation drawn from the used surrogate. In this paper, we propose a new way to tackle expensive MOPs. The main idea is to use Neural Networks (NNs) within the Indicator-Based Evolutionary Algorithm (IBEA) in order to estimate the contribution of each generated offspring in terms of hypervolume. After that, only fit individuals with respect to the estimations are exactly evaluated. Our proposed algorithm called NN-SS-IBEA (Neural Networks assisted Steady State IBEA) have been demonstrated to provide good performance with a low number of function evaluations when compared against the original IBEA and MOEA/D-RBF on a set of benchmark problems in addition to the airfoil design problem.}, notes = {Also known as \cite{2598271} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Bringmann:2014:GECCO, author = {Karl Bringmann and Tobias Friedrich and Patrick Klitzke}, title = {Two-dimensional subset selection for hypervolume and epsilon-indicator}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {589--596}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598276}, doi = {doi:10.1145/2576768.2598276}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The goal of bi-objective optimisation is to find a small set of good compromise solutions. A common problem for bi-objective evolutionary algorithms is the following subset selection problem (SSP): Given n solutions P in R-squared in the objective space, select k solutions P* from P that optimise an indicator function. In the hypervolume SSP we want to select k points P* that maximise the hypervolume indicator IHYP(P*, r) for some reference point r in R-squared. Similarly, the epsilon-indicator SSP aims at selecting k~points P* that minimise the epsilon-indicator Ieps(P*,R) for some reference set R in Reals-squared of size m (which can be R=P). We first present a new algorithm for the hypervolume SSP with runtime O(n (k + log n)). Our second main result is a new algorithm for the epsilon-indicator SSP with runtime O(n log n + m log m). Both results improve the current state of the art runtimes by a factor of (nearly) $n$ and make the problems tractable for new applications. Preliminary experiments confirm that the theoretical results translate into substantial empirical runtime improvements.}, notes = {Also known as \cite{2598276} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Dohr:2014:GECCO, author = {Martin Dohr and Bernd Eichberger}, title = {Improving many-objective optimization performance by sequencing evolutionary algorithms}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {597--604}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598326}, doi = {doi:10.1145/2576768.2598326}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Evolutionary multi objective optimisation (EMO) has been successfully applied to various real-world scenarios with usually two or three contradicting optimisation goals. However, several studies have pointed out a great deterioration of computational performance when handling more than three objectives. In order to improve the scalability of multiobjective evolutionary algorithms (MOEAs) onto higher-dimensional objective spaces, techniques using e.g. scalarising functions and preference- or indicator-based guidance have been proposed. Most of those proposals require a-priori information or a decision maker during optimisation, which increases the complexity of the algorithms. In this paper, we propose a divide and conquer method for many-objective optimization. First, we partition a problem into lower-dimensional subproblems for which standard algorithms are known to perform very well. Our key improvement is the sequential usage of MOEAs, using the results of one suboptimization as initial population for another MOEA. This technique allows modular optimisation phases and can be applied to common evolutionary algorithms. We test our enhanced method on the hard to solve multiobjective Quadratic Assignment Problem (mQAP), using a variety of established MOEAs.}, notes = {Also known as \cite{2598326} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Fieldsend:2014:GECCO, author = {Jonathan E. Fieldsend and Richard M. Everson}, title = {Efficiently identifying pareto solutions when objective values change}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {605--612}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598279}, doi = {doi:10.1145/2576768.2598279}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In many multi-objective problems the objective values assigned to a particular design can change during the course of an optimisation. This may be due to dynamic changes in the problem itself, or updates to estimated objectives in noisy problems. In these situations, designs which are non-dominated at one time step may become dominated later not just because a new and better solution has been found, but because the existing solution's performance has degraded. Likewise, a dominated solution may later be identified as non-dominated because its objectives have comparatively improved. We propose management algorithms based on recording single guardian dominators for each solution which allow rapid discovery and updating of the non-dominated subset of solutions evaluated by an optimiser. We examine the computational complexity of our proposed approach, and compare the performance of different ways of selecting the guardian dominators.}, notes = {Also known as \cite{2598279} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Huo:2014:GECCO, author = {Yu Dan Huo and Zhi Hua Cai and Wen Yin Gong and Qin Liu}, title = {The parameter optimization of kalman filter based on multi-objective memetic algorithm}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {613--620}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598242}, doi = {doi:10.1145/2576768.2598242}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Generally, there are two objectives in the optimisation of the measurement noise covariance matrix R of Kalman filter. However, most of the traditional optimisation methods of Kalman filter only focus on one objective. In this paper, we proposed a new method to optimize the parameter R based on Multi-Objective Memetic Algorithm (MOMA). Compared with traditional methods, it can optimise multiple objectives simultaneously. In this method, the decision vector is the diagonal elements of matrix R, the first objective function f1 is the mean of the residual vectors, and the second objective function f2 is the degree of mismatching between the actual value of the residual covariance with its theoretical value. In the MOMA, the global search based on NSGA-II is used to minimise the two objective functions, and the local search based on Simulated Annealing (SA) is just used to minimise the f1. The experimental results demonstrate that the Kalman filter optimised by MOMA, namely MOMA-Kalman, can get much smaller filtering error than regular Kalman filter and other adaptive filter algorithms, such as SageHusa-Kalman and Fuzzy-Kalman.}, notes = {Also known as \cite{2598242} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Iordache:2014:GECCO, author = {Raluca Iordache and Serban Iordache and Florica Moldoveanu}, title = {A framework for the study of preference incorporation in multiobjective evolutionary algorithms}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {621--628}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598380}, doi = {doi:10.1145/2576768.2598380}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We present a formal framework for the study of user preference incorporation into multiobjective evolutionary algorithms. This framework can accommodate virtually any preference model, including those that violate the independence of irrelevant alternatives. We also introduce the Preferanto notation, which permits the specification of a large variety of preference models. A number of properties and indicators are proposed for characterising preference models. We report the results of a case study experiment assessing the impact of incorporating different preference models into an NSGA-II algorithm.}, notes = {Also known as \cite{2598380} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Miyakawa:2014:GECCO, author = {Minami Miyakawa and Keiki Takadama and Hiroyuki Sato}, title = {Controlling selection area of useful infeasible solutions and their archive for directed mating in evolutionary constrained multiobjective optimization}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {629--636}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598313}, doi = {doi:10.1145/2576768.2598313}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {As an evolutionary approach to solve constrained multi-objective optimisation problems (CMOPs), recently a MOEA using the two-stage non-dominated sorting and the directed mating (TNSDM) has been proposed. In TNSDM, the directed mating uses infeasible solutions dominating feasible solutions to generate offspring. Although the directed mating contributes to improve the search performance of TNSDM in CMOPs, there are two problems. First, since the number of infeasible solutions dominating feasible solutions in the population depends on each CMOP, the effectiveness of the directed mating also depends on each CMOP. Second, infeasible solutions used in the directed mating are discarded in the selection process of parents (elites) population and cannot be used in the next generation. To overcome these problems and further improve the effectiveness of the directed mating in TNSDM, in this work we propose an improved TNSDM introducing a method to control selection area of infeasible solutions and an archiving strategy of useful infeasible solutions for the directed mating. The experimental results on m objectives k knapsacks problems shows that the improved TNSDM improves the search performance by controlling the directionality of the directed mating and increasing the number of directed mating executions in the solution search.}, notes = {Also known as \cite{2598313} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Pilat:2014:GECCO, author = {Martin Pil\'{a}t and Roman Neruda}, title = {Hypervolume-based local search in multi-objective evolutionary optimization}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {637--644}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598332}, doi = {doi:10.1145/2576768.2598332}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper describes a surrogate based multi-objective evolutionary algorithm with hyper-volume contribution-based local search. The algorithm switches between an NSGA-II phase and a local search phase. In the local search phase, a model for each of the objectives is trained and CMA-ES is used to optimize the hyper-volume contribution of each individual with respect to its two neighbors on the non-dominated front. The performance of the algorithm is evaluated using the well known ZDT and WFG benchmark suites.}, notes = {Also known as \cite{2598332} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Sato:2014:GECCO, author = {Hiroyuki Sato}, title = {Inverted PBI in MOEA/D and its impact on the search performance on multi and many-objective optimization}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {645--652}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598297}, doi = {doi:10.1145/2576768.2598297}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {MOEA/D decomposes a multi-objective optimisation problem into a number of single objective optimisation problems. Each single objective optimization problem is defined by a scalarising function using a weight vector. In MOEA/D, there are several scalarising approaches such as the weighted Tchebycheff, the weighted sum, and the PBI (penalty-based boundary intersection). However, these conventional scalarising approaches face a difficulty to approximate a widely spread Pareto front in some problems. To enhance the spread of Pareto optimal solutions in the objective space and improve the search performance of MOEA/D especially in many-objective optimisation problems, in this work we propose the inverted PBI scalarising approach which is an extension of the conventional PBI. We use many-objective knapsack problems and WFG4 problems with 2-8 objectives, and compare the search performance of NSGA-III and four MOEA/Ds using the weighted Tchebycheff, the weighted sum, the PBI and the inverted PBI. As results, we show that MOEA/D using the inverted PBI achieves higher search performance than other algorithms in problems with many-objectives and the difficulty to obtain a widely spread Pareto front in the objective space.}, notes = {Also known as \cite{2598297} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Xiao:2014:GECCO, author = {Jing Xiao and Zhou Wu and Jian-Chao Tang}, title = {Hybridization of electromagnetism with multi-objective evolutionary algorithms for RCPSP}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {653--660}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598228}, doi = {doi:10.1145/2576768.2598228}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {As one of the most challenging combinatorial optimisation problems in scheduling, the resource-constrained project scheduling problem (RCPSP) has attracted numerous scholars' interest resulting in considerable research in the past few decades. However, most of these papers focused on the single objective RCPSP; only a few papers concentrated on the multi-objective resource-constrained project scheduling problem (MORCPSP). Inspired by a procedure called electromagnetism (EM), which can help a generic population-based evolutionary search algorithm to obtain good results for single objective RCPSP, in this paper we attempt to extend EM and hybridise it with three reputable state-of-the-art multi-objective evolutionary algorithms (MOEAs) i.e. NSGA-II, SPEA2 and MOEA/D, for MORCPSP. Our two objectives are minimising makespan and total tardiness. We perform computational experiments on standard benchmark datasets. Empirical comparison and analysis of the results obtained by the hybridization versions of EM with NSGA-II, SPEA2 and MOEA/D are conducted. The results demonstrate that EM can improve the performance of NSGA-II and SPEA2.}, notes = {Also known as \cite{2598228} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Yuan:2014:GECCO, author = {Yuan Yuan and Hua Xu and Bo Wang}, title = {An improved NSGA-III procedure for evolutionary many-objective optimization}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {661--668}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598342}, doi = {doi:10.1145/2576768.2598342}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Many-objective (four or more objectives) optimisation problems pose a great challenge to the classical Pareto-dominance based multi-objective evolutionary algorithms (MOEAs), such as NSGA-II and SPEA2. This is mainly due to the fact that the selection pressure based on Pareto-dominance degrades severely with the number of objectives increasing. Very recently, a reference-point based NSGA-II, referred as NSGA-III, is suggested to deal with many-objective problems, where the maintenance of diversity among population members is aided by supplying and adaptively updating a number of well-spread reference points. However, NSGA-III still relies on Pareto-dominance to push the population towards Pareto front (PF), leaving room for the improvement of its convergence ability. In this paper, an improved NSGA-III procedure, called e-NSGA-III, is proposed, aiming to better tradeoff the convergence and diversity in many-objective optimisation. In e-NSGA-III, the non-dominated sorting scheme based on the proposed e-dominance is employed to rank solutions in the environmental selection phase, which ensures both convergence and diversity. Computational experiments have shown that e-NSGA-III is significantly better than the original NSGA-III and MOEA/D on most instances no matter in convergence and overall performance.}, notes = {Also known as \cite{2598342} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Yuan:2014:GECCOa, author = {Yuan Yuan and Hua Xu and Bo Wang}, title = {Evolutionary many-objective optimization using ensemble fitness ranking}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {669--676}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598345}, doi = {doi:10.1145/2576768.2598345}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper, a new framework, called ensemble fitness ranking (EFR), is proposed for evolutionary many-objective optimisation that allows to work with different types of fitness functions and ensemble ranking schemes. The framework aims to rank the solutions in the population more appropriately by combing the ranking results from many simple individual rankers. As to the form of EFR, it can be regarded as an extension of average and maximum ranking methods which have been shown promising for many-objective problems. The significant change is that EFR adopts more general fitness functions instead of objective functions, which would make it easier for EFR to balance the convergence and diversity in many-objective optimization. In the experimental studies, the influence of several fitness functions and ensemble ranking schemes on the performance of EFR is fist investigated. Afterwards, EFR is compared with two state-of-the-art methods (MOEA/D and NSGA-III) on well-known test problems. The computational results show that EFR significantly outperforms MOEA/D and NSGA-III on most instances, especially for those having a high number of objectives.}, notes = {Also known as \cite{2598345} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Bryson:2014:GECCO, author = {David M. Bryson and Aaron P. Wagner and Charles Ofria}, title = {There and back again: gene-processing hardware for the evolution and robotic deployment of robust navigation strategies}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {689--696}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598363}, doi = {doi:10.1145/2576768.2598363}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Navigation strategies represent some of the most intriguing examples of complex and intelligent behaviours in nature. Accordingly, they have been the focus of extensive research in animal behavior and in evolutionary robotics. However, engineering successes in harnessing the evolutionary dynamics that shape sophisticated navigation strategies remain limited. Here we describe a novel gene-processing architecture for digital organisms that enables the evolution of central-place-foraging strategies, such as those seen in honeybees and striped hyena. While previous studies have evolved navigation de novo, the resulting algorithms have been relatively fragile and difficult to translate into physical systems. In contrast, the strategies evolved in this study are highly congruous with those seen in nature: a single evolved foraging strategy incorporates periods of directed travel, fixed pattern search, cue response, and reorientation when outcomes do not match expected results. Additionally, the genetic architecture enabled rapid extraction of the underlying behavioural algorithm and transference to a robotic system, proving to be robust to issues of noise and scale that commonly plague such attempts. Accordingly, we demonstrate that the flexibility and interpretability of the new gene-processing hardware readily facilitate the creation, study, and use of naturalistic and deployable algorithms for functionally complex behaviours.}, notes = {Also known as \cite{2598363} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Huizinga:2014:GECCO, author = {Joost Huizinga and Jeff Clune and Jean-Baptiste Mouret}, title = {Evolving neural networks that are both modular and regular: HyperNEAT plus the connection cost technique}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {697--704}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598232}, doi = {doi:10.1145/2576768.2598232}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {One of humanity's grand scientific challenges is to create artificially intelligent robots that rival natural animals in intelligence and agility. A key enabler of such animal complexity is the fact that animal brains are structurally organised in that they exhibit modularity and regularity, amongst other attributes. Modularity is the localisation of function within an encapsulated unit. Regularity refers to the compressibility of the information describing a structure, and typically involves symmetries and repetition. These properties improve evolvability, but they rarely emerge in evolutionary algorithms without specific techniques to encourage them. It has been shown that (1) modularity can be evolved in neural networks by adding a cost for neural connections and, separately, (2) that the HyperNEAT algorithm produces neural networks with complex, functional regularities. In this paper we show that adding the connection cost technique to HyperNEAT produces neural networks that are significantly more modular, regular, and higher performing than HyperNEAT without a connection cost, even when compared to a variant of HyperNEAT that was specifically designed to encourage modularity. Our results represent a stepping stone towards the goal of producing artificial neural networks that share key organisational properties with the brains of natural animals.}, notes = {Also known as \cite{2598232} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Lessin:2014:GECCO, author = {Dan Lessin and Don Fussell and Risto Miikkulainen}, title = {Trading control intelligence for physical intelligence: muscle drives in evolved virtual creatures}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {705--712}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598290}, doi = {doi:10.1145/2576768.2598290}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Traditional evolved virtual creatures [1] are actuated using unevolved, uniform, invisible drives at joints between rigid segments. In contrast, this paper shows how such conventional actuators can be replaced by evolvable muscle drives that are a part of the creature's physical structure. Such a muscle-drive system replaces control intelligence with meaningful morphological complexity. For instance, the experiments in this paper show that control intelligence sufficient for locomotion or jumping can be moved almost entirely from the brain into the musculature of evolved virtual creatures. This design is important for two reasons: First, the control intelligence is made visible in the purposeful development of muscle density, orientation, attachment points, and size. Second, the complexity that needs to be evolved for the brain to control the actuators is reduced, and in some cases can be essentially eliminated, thus freeing brain power for higher-level functions. Such designs may thus make it possible to create more complex behaviour than would otherwise be achievable.}, notes = {Also known as \cite{2598290} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Risi:2014:GECCO, author = {Sebastian Risi and Kenneth O. Stanley}, title = {Guided self-organization in indirectly encoded and evolving topographic maps}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {713--720}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598369}, doi = {doi:10.1145/2576768.2598369}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {An important phenomenon seen in many areas of biological brains and recently in deep learning architectures is a process known as self-organisation. For example, in the primary visual cortex, colour and orientation maps develop based on lateral inhibitory connectivity patterns and Hebbian learning dynamics. These topographic maps, which are found in all sensory systems, are thought to be a key factor in enabling abstract cognitive representations. This paper shows for the first time that the Hypercube-based NeuroEvolution of Augmenting Topologies (HyperNEAT) method can be seeded to begin evolution with such lateral connectivity, enabling genuine self-organising dynamics. The proposed approach draws on HyperNEAT's ability to generate a pattern of weights across the connectivity of an artificial neural network (ANN) based on a function of its geometry. Validating this approach, the afferent weights of an ANN self-organize in this paper to form a genuine topographic map of the input space for a simple line orientation task. Most interestingly, this seed can then be evolved further, providing a method to guide the self-organisation of weights in a specific way, much as evolution likely guided the self-organizing trajectories of biological brains.}, notes = {Also known as \cite{2598369} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Samuelsen:2014:GECCO, author = {Eivind Samuelsen and Kyrre Glette}, title = {Some distance measures for morphological diversification in generative evolutionary robotics}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {721--728}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598325}, doi = {doi:10.1145/2576768.2598325}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Evolutionary robotics often involves optimisation in large, complex search spaces, requiring good population diversity. Recently, measures to actively increase diversity or novelty have been employed in order to get sufficient exploration of the search space either as the sole optimisation objective or in combination with some performance measurement. When evolving morphology in addition to the control system, it can be difficult to construct a measure that sufficiently captures the qualitative differences between individuals. In this paper we investigate four diversity measures, applied in a set of evolutionary robotics experiments using an indirect encoding for evolving robot morphology. In the experiments we optimise forward locomotion capabilities of symmetrical legged robots in a physics simulation. Two distance measures in Cartesian phenotype feature spaces are compared with two methods operating in the space of possible morphology graphs. These measures are used for computing a diversity objective in a multi-objective evolutionary algorithm, and compared to a control case with no diversity objective. For the given task one of the distance measures shows a clear improvement over the control case in improving the main objectives, while others display better ability to diversify, underlining the difficulty of designing good, general measures of morphological diversity.}, notes = {Also known as \cite{2598325} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Smalikho:2014:GECCO, author = {Olga Smalikho and Markus Olhofer}, title = {Growth in co-evolution of sensory system and signal processing for optimal wing control}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {729--736}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598318}, doi = {doi:10.1145/2576768.2598318}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The development of adaptive systems, which react autonomously to changes in their environment, require the coordinated generation of sensors, providing information about the environment and signal processing structures, which generate suitable reactions to changed conditions. In this work we demonstrate the applicability of a concurrent evolutionary design of the optimal sensory and controller parts of a system for the example of an adaptive wing design. The focus of the work is twofold. First on the realisation of developmental stages of the sensory and controlling systems design, defined as a growth process, and second on the comparison of the differences in structures of the systems developed through the presented evolutionary growth method and of evolved systems, having fixed set of sensory elements. We ascertained that the success of the realised growth process depends among others on the relation between the triggering methods and timing of the system enlargement and on parameter settings of the optimisation strategy after a growth phase.}, notes = {Also known as \cite{2598318} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Velez:2014:GECCO, author = {Roby Velez and Jeff Clune}, title = {Novelty search creates robots with general skills for exploration}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {737--744}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598225}, doi = {doi:10.1145/2576768.2598225}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Novelty Search, a new type of Evolutionary Algorithm, has shown much promise in the last few years. Instead of selecting for phenotypes that are closer to an objective, Novelty Search assigns rewards based on how different the phenotypes are from those already generated. A common criticism of Novelty Search is that it is effectively random or exhaustive search because it tries solutions in an unordered manner until a correct one is found. Its creators respond that over time Novelty Search accumulates information about the environment in the form of skills relevant to reaching uncharted territory, but to date no evidence for that hypothesis has been presented. In this paper we test that hypothesis by transferring robots evolved under Novelty Search to new environments (here, mazes) to see if the skills they've acquired generalise. Three lines of evidence support the claim that Novelty Search agents do indeed learn general exploration skills. First, robot controllers evolved via Novelty Search in one maze and then transferred to a new maze explore significantly more of the new environment than non-evolved (randomly generated) agents. Second, a Novelty Search process to solve the new mazes works significantly faster when seeded with the transferred controllers versus randomly-generated ones. Third, no significant difference exists when comparing two types of transferred agents: those evolved in the original maze under (1) Novelty Search vs. (2) a traditional, objective-based fitness function. The evidence gathered suggests that, like traditional Evolutionary Algorithms with objective-based fitness functions, Novelty Search is not a random or exhaustive search process, but instead is accumulating information about the environment, resulting in phenotypes possessing skills needed to explore their world.}, notes = {Also known as \cite{2598225} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Wilson:2014:GECCO, author = {Dennis Wilson and Sylvain Cussat-Blanc and Kalyan Veeramachaneni and Una-May O'Reilly and Herv\'{e} Luga}, title = {A continuous developmental model for wind farm layout optimization}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {745--752}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598383}, doi = {doi:10.1145/2576768.2598383}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We present DevoII, an improved cell-based developmental model for wind farm layout optimisation. To address the shortcomings of discretisation, DevoII's gene regulatory networks control cells that act in a continuous rather than discretized grid space. We find that DevoII is competitive, and in some cases, superior with respect to state-of-the-art global, stochastic search approaches when a suite of algorithms is evaluated on different wind scenarios. The modularity of the genetic regulatory network computational paradigm in terms of isolating its search algorithm, the regulatory network simulation and the cell simulation, allowed this improvement to largely focus upon cell simulation. This indicates a robustness property of the paradigm's design. As well, wflo highlights how developmental models can be considered more efficient than other optimisation methods because of their optimise once, use-many adaptability.}, notes = {Also known as \cite{2598383} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Colin:2014:GECCO, author = {`Sylvain Colin and Benjamin Doerr and Gaspard F\'{e}rey}, title = {Monotonic functions in EC: anything but monotone!}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {753--760}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598338}, doi = {doi:10.1145/2576768.2598338}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {To understand how evolutionary algorithms optimise the simple class of monotonic functions, Jansen (FOGA 2007) introduced the partially-ordered evolutionary algorithm (PO-EA) model and analysed its runtime. The PO-EA is a pessimistic model of the true optimisation process, hence performance guarantees for it immediately take over to the true optimisation process. Based on the observation that Jansen's model leads to a process more pessimistic than what any monotonic function would, we extend his model by parametrise the degree of pessimism. For all degrees of pessimism, and all mutation rates c/n, we give a precise runtime analysis of this process. For all degrees of pessimism lower than that of Jansen, we observe a Theta(n log n) runtime for the standard mutation probability of 1/n. However, we also observe a strange double-jump behaviour in terms of the mutation probability. For all non-zero degrees of pessimism, there is a threshold c ? R such that (i) for mutation rates c'/n with c'3/2), and (iii) for mutation rates c''/n with c''> c we have an exponential runtime. To overcome the complicated interplay of mutation and selection in the PO-EA, by define artificial algorithms which provably (via a coupling argument) have the same asymptotic runtime, but allow a much easier computation of the drift towards the optimum.}, notes = {Also known as \cite{2598338} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Cruz-Vega:2014:GECCO, author = {Israel Cruz-Vega and Mauricio Garcia-Limon and Hugo Jair Escalante}, title = {Adaptive-surrogate based on a neuro-fuzzy network and granular computing}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {761--768}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598376}, doi = {doi:10.1145/2576768.2598376}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Surrogate-based methods aim at reducing the evaluation of expensive fitness functions in optimisation processes. Several surrogate-based methods for evolutionary optimization have been proposed so far, including those based on granular computing / clustering. Granular computing provides granules as an assemblage of entities arranged together by their similarity, functional or physical adjacency, indistinguishability, coherency, or the like. Techniques like this avoid multiple and unnecessary evaluations of individuals repeatedly. In this paper, with the aim of granular computing as a method of grouping data, such information is exploited to obtain knowledge of the structure and parameters of individuals and then, design a Neuro-Fuzzy network that adapts granules' parameters, providing convergence to acceptable solutions with a reduced number of evaluations of the fitness function. We implement this adaptive surrogate in a genetic algorithm and show its performance using benchmark functions.}, notes = {Also known as \cite{2598376} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Doerr:2014:GECCO, author = {Benjamin Doerr and Carola Doerr and Timo K\"{o}tzing}, title = {Unbiased black-box complexities of jump functions: how to cross large plateaus}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {769--776}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598341}, doi = {doi:10.1145/2576768.2598341}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We analyse the unbiased black-box complexity of jump functions with large jump sizes. Among other results, we show that when the jump size is (1/2 - epsilon)n, that is, only a small constant fraction of the fitness values is visible, then the unbiased black-box complexities for arities 3 and higher are of the same order as those for the simple OneMax function. Even for the extreme jump function, in which all but the two fitness values n/2 and n are blanked out, polynomial-time mutation-based (i.e., unary unbiased) black-box optimisation algorithms exist. This is quite surprising given that for the extreme jump function almost the whole search space (all but a Theta(n-1/2) fraction) is a plateau of constant fitness. To prove these results, we introduce new tools for the analysis of unbiased black-box complexities, for example, selecting the new parent individual not by comparing the fitnesses of the competing search points, but also by taking into account the (empirical) expected fitnesses of their offspring.}, notes = {Also known as \cite{2598341} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Gao:2014:GECCO, author = {Wanru Gao and Frank Neumann}, title = {Runtime analysis for maximizing population diversity in single-objective optimization}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {777--784}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598251}, doi = {doi:10.1145/2576768.2598251}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Recently Ulrich and Thiele [14] have introduced evolutionary algorithms for the mixed multi-objective problem of maximising fitness as well as diversity in the decision space. Such an approach allows to generate a diverse set of solutions which are all of good quality. With this paper, we contribute to the theoretical understanding of evolutionary algorithms for maximizing the diversity in a population that contains several solutions of high quality. We study how evolutionary algorithms maximise the diversity of a population where each individual has to have fitness beyond a given threshold value. We present a first runtime analysis in this area and study the classical problems called \emph{\OM} and \emph{\LO}. Our results give first rigorous insights on how evolutionary algorithms can be used to produce a maximal diverse set of solutions in which all solutions have quality above a certain threshold value.}, notes = {Also known as \cite{2598251} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Goldman:2014:GECCO, author = {Brian W. Goldman and William F. Punch}, title = {Parameter-less population pyramid}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {785--792}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598350}, doi = {doi:10.1145/2576768.2598350}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Real world applications of evolutionary techniques are often hindered by the need to determine problem specific parameter settings. While some previous methods have reduced or removed the need for parameter tuning, many do so by trading efficiency for general applicability. The Parameter-less Population Pyramid (P3) is an evolutionary technique that requires no parameters and is still broadly effective. P3 strikes a balance between continuous integration of diversity and exploitative elitist operators, allowing it to solve easy problems quickly and hard problems eventually. When compared with three optimally tuned, state of the art optimisation techniques, P3 always finds the optimum at least a constant factor faster across four benchmarks (Deceptive Trap, Deceptive Step Trap, HIFF, Rastrigin). More importantly, on three randomised benchmarks (NK Landscapes, Ising Spin Glasses, MAX-SAT), P3 has a lower order of computational complexity as measured by evaluations. We also provide outlines for expected runtime analysis of P3, setting the stage for future theory based conclusions. Based on over 1 trillion evaluations, our results suggest P3 has wide applicability to a broad class of problems.}, notes = {Also known as \cite{2598350} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Kromer:2014:GECCO, author = {Pavel Kr\"{o}mer and Jan Plato\v{s}}, title = {Genetic algorithm for sampling from scale-free data and networks}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {793--800}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598391}, doi = {doi:10.1145/2576768.2598391}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {A variety of real-world data and networks can be described by a heavy-tailed probability distribution of its values, vertex degrees, or other significant properties, that follows the power law. Such a scale-free data and networks can be found in both natural phenomena such as protein interaction networks and gene regulation networks and man-made structures like the Internet, language, and various social networks. An efficient analysis of large scale data and networks is often impractical and various heuristic and metaheuristc sampling techniques are deployed to select smaller subsets of the data for analysis and visualisation. A key goal of data and network sampling is to select such a subset of the original data that would accurately represent the original data with respect to selected attributes. In this work we propose a novel genetic algorithm for scale-free data and network sampling and evaluate the algorithm in a series of computational experiments.}, notes = {Also known as \cite{2598391} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Mayer:2014:GECCO, author = {Benjamin E. Mayer and Kay Hamacher}, title = {Stochastic tunneling transformation during selection in genetic algorithm}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {801--806}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598243}, doi = {doi:10.1145/2576768.2598243}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Genetic Algorithms (GA) combine mutational and recombination operators to then select between individuals. Thereby, competition becomes the driving force to improve solutions. Now, this naive approach to biological evolution often assumes a static fitness function, e.g., co-evolutionary effects cannot easily be leveraged. Here, we introduce a fitness landscape transformation inspired by Monte-Carlo-based optimisation schemes. In the Stochastic-Tunneling (STUN) framework fitness values are non-linearly transformed under preservation of the relative ranking of optima. The base line of the STUN-transformation can be set based on different memory mechanisms -- from current to full history. This STUN-based GA-variant allows to include co-evolution and history into the GA. Based on analytic arguments we can show that the non-linearity of the transformation generates high population densities in areas of interest. We numerically simulated small, controllable, and well understood test instance: replicas of Ising-spin glasses. For these systems the STUN-GAs have shown significant improvements in terms of relative error for given computational effort. In addition, we introduce an empirical measure of selection to discuss the improved convergence behaviour.}, notes = {Also known as \cite{2598243} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Nallaperuma:2014:GECCOa, author = {Samadhi Nallaperuma and Frank Neumann and Dirk Sudholt}, title = {A fixed budget analysis of randomized search heuristics for the traveling salesperson problem}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {807--814}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598302}, doi = {doi:10.1145/2576768.2598302}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Randomised Search heuristics are frequently applied to NP-hard combinatorial optimisation problems. The runtime analysis of randomised search heuristics has contributed tremendously to their theoretical understanding. Recently, randomized search heuristics have been examined regarding their achievable progress within a fixed time budget. We follow this approach and present a first fixed budget runtime analysis for a NP-hard combinatorial optimisation problem. We consider the well-known Travelling Salesperson problem (TSP) and analyse the fitness increase that randomised search heuristics are able to achieve within a given fixed budget.}, notes = {Also known as \cite{2598302} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Picek:2014:GECCO, author = {Stjepan Picek and Domagoj Jakobovic}, title = {From fitness landscape to crossover operator choice}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {815--822}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598320}, doi = {doi:10.1145/2576768.2598320}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Genetic algorithms are applied to numerous problems that demonstrate different properties. To efficiently solve these problems, during the years a significant number of variation operators have been and still are created. It is a problem by itself how to correctly choose between those operators, i.e. how to find the most suitable operator (or a set) for a given problem. In this paper we investigate the choice of the suitable crossover operator on the basis of fitness landscape. The fitness landscape can be described with a number of properties, so a thorough analysis needs to be done to find the most useful ones. To achieve that, we experiment with 24 noise-free problems and floating point encoding. The results indicate it is possible to either select a suitable operator or at least to reduce the number of adequate operators with fitness landscape properties.}, notes = {Also known as \cite{2598320} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Ray:2014:GECCO, author = {Steven Ray and Vahl Scott Gordon and Laurent Vaucher}, title = {Evolving QWOP gaits}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {823--830}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598248}, doi = {doi:10.1145/2576768.2598248}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {QWOP is a popular Flash game in which a human player controls a sprinter in a simulated 100-meter dash. The game is notoriously difficult owing to its ragdoll physics engine, and the simultaneous movements that must be carefully coordinated to achieve forward progress. While previous researchers have evolved gaits using simulations similar to QWOP, we describe a software interface that connects directly to QWOP itself, incorporating a genetic algorithm to evolve actual QWOP gaits. Since QWOP has no API, ours detects graphical screen elements and uses them to build a fitness function. Two variable-length encoding schemes, that codify sequences of QWOP control commands that loop to form gaits, are tested. We then compare the performance of SGA, Genitor, and a Cellular Genetic Algorithm on this task. Using only the end score as the basis for fitness, the cellular algorithm is consistently able to evolve a successful scooting strategy similar to one most humans employ. The results confirm that steady-state GAs are preferred when the task is sensitive to small input variations. Although the limited feedback does not yet produce performance competitive with QWOP champions, it is the first autonomous software evolution of successful QWOP gaits.}, notes = {Also known as \cite{2598248} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Ruiz:2014:GECCO, author = {Kim-Hang Ruiz}, title = {Search for maximal snake-in-the-box using new genetic algorithm}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {831--838}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598296}, doi = {doi:10.1145/2576768.2598296}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The Snake-In-The-Box (SIB) problem is a challenging combinatorial search problem to find the longest constrained open path (k-spread snake) in n-dimensional hypercube (Qn). In addition to constructive techniques, many search algorithms such as Depth First Search (DFS), Genetic Algorithm (GA), hybrid Evolutionary Computation algorithm (EC), and Nested Monte-Carlo Search (NMCS) have been used to tackle this problem. To get better results and to speed up the process, these techniques often used a long snake as the starting point for the search (priming/seeding). This paper reviews the hypercube fundamentals, then presents a new search technique, Mitosis Genetic Algorithm (MGA), which was applied in search for the four different spread snakes (spread 2 to 5) in seven different dimensional hypercubes (Q6 to Q13). The MGA found three new record-breaking 3-spread snakes in Q10, Q11 and Q13, all the previously known optimal snakes from spread 2 to spread 5, and the best previous known maximal 3-S9 snake of length 63. It is remarkable that it found those within minutes to hours without priming, significantly shorter than days to weeks needed in the other techniques.}, notes = {Also known as \cite{2598296} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Shi:2014:GECCO, author = {Jun Shi and Ole J. Mengshoel and Dipan K. Pal}, title = {Feedback control for multi-modal optimization using genetic algorithms}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {839--846}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598231}, doi = {doi:10.1145/2576768.2598231}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Many optimisation problems are multi-modal. In certain cases, we are interested in finding multiple locally optimal solutions rather than just a single optimum as is computed by traditional genetic algorithms (GAs). Several niching techniques have been developed that seek to find multiple such local optima. These techniques, which include sharing and crowding, are clearly powerful and useful. But they do not explicitly let the user control the number of local optima being computed, which we believe to be an important capability. In this paper, we develop a method that provides, as an input parameter to niching, the desired number of local optima. Our method integrates techniques from feedback control, includes a sensor based on clustering, and uses a scaling parameter in Generalised Crowding to control the number of niches being explored. The resulting Feedback Control GA (FCGA) is tested in several experiments and found to perform well compared to previous approaches. Overall, the integration of feedback control and Generalized Crowding is shown to effectively guide the search for multiple local optima in a more controlled fashion. We believe this novel capability has the potential to impact future applications as well as other evolutionary algorithms.}, notes = {Also known as \cite{2598231} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Sinha:2014:GECCO, author = {Ankur Sinha and Pekka Malo and Peng Xu and Kalyanmoy Deb}, title = {A bilevel optimization approach to automated parameter tuning}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {847--854}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598221}, doi = {doi:10.1145/2576768.2598221}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Many of the modern optimisation algorithms contain a number of parameters that require tuning before the algorithm can be applied to a particular class of optimization problems. A proper choice of parameters may have a substantial effect on the accuracy and efficiency of the algorithm. Until recently, parameter tuning has mostly been performed using brute force strategies, such as grid search and random search. Guesses and insights about the algorithm are also used to find suitable parameters or suggest strategies to adjust them. More recent trends include the use of meta-optimisation techniques. Most of these approaches are computationally expensive and do not scale when the number of parameters increases. In this paper, we propose that the parameter tuning problem is inherently a bilevel programming problem. Based on this insight, we introduce an evolutionary bilevel algorithm for parameter tuning. A few commonly used optimisation algorithms (Differential Evolution and Nelder-Mead) have been chosen as test cases, whose parameters are tuned on a number of standard test problems. The bilevel approach is found to quickly converge towards the region of efficient parameters. The code for the proposed algorithm can be accessed from the website http://bilevel.org.}, notes = {Also known as \cite{2598221} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Vafaee:2014:GECCO, author = {Fatemeh Vafaee}, title = {Learning the structure of large-scale bayesian networks using genetic algorithm}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {855--862}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598223}, doi = {doi:10.1145/2576768.2598223}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Bayesian networks are probabilistic graphical models representing conditional dependencies among a set of random variables. Due to their concise representation of the joint probability distribution, Bayesian Networks are becoming incrementally popular models for knowledge representation and reasoning in various problem domains. However, learning the structure of the Bayesian networks is an NP-hard problem since the number of structures grows super-exponentially as the number of variables increases. This work therefore is aimed to propose a new hybrid structure learning algorithm that uses mutual dependencies to reduce the search space complexity and recruits the genetic algorithm to effectively search over the reduced space of possible structures. The proposed method is best suited for problems with medium to large number of variables and a limited dataset. It is shown that the proposed method achieves higher model's accuracy as compared to a series of popular structure learning algorithms particularly when the data size gets smaller.}, notes = {Also known as \cite{2598223} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Vafaee:2014:GECCOa, author = {Fatemeh Vafaee and Gyorgy Turan and Peter C. Nelson and Tanya Y. Berger-Wolf}, title = {Among-site rate variation: adaptation of genetic algorithm mutation rates at each single site}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {863--870}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598216}, doi = {doi:10.1145/2576768.2598216}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper is concerned with proposing an elitist genetic algorithm which makes use of a new mutation scheme aimed to tackle both explorative and exploitative responsibilities of genetic operators. The proposed mutation scheme follows an approach similar to motif representation in biology, to derive the underlying pattern of highly-fit solutions discovered so far. This pattern is then used to derive mutation rates specified for every site along the encoded solutions. The site-specific rates are amended for every individual to balance the required explorative and exploitative power. The Markov chain model of the proposed method is also derived and used to analyse its convergence properties.}, notes = {Also known as \cite{2598216} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Zaefferer:2014:GECCO, author = {Martin Zaefferer and J\"{o}rg Stork and Martina Friese and Andreas Fischbach and Boris Naujoks and Thomas Bartz-Beielstein}, title = {Efficient global optimization for combinatorial problems}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {871--878}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598282}, doi = {doi:10.1145/2576768.2598282}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Real-world optimisation problems may require time consuming and expensive measurements or simulations. Recently, the application of surrogate model-based approaches was extended from continuous to combinatorial spaces. This extension is based on the use of suitable distance measures such as Hamming or Swap Distance. In this work, such an extension is implemented for Kriging (Gaussian Process) models. Kriging provides a measure of uncertainty when determining predictions. This can be harnessed to calculate the Expected Improvement (EI) of a candidate solution. In continuous optimisation, EI is used in the Efficient Global Optimisation (EGO) approach to balance exploitation and exploration for expensive optimization problems. Employing the extended Kriging model, we show for the first time that EGO can successfully be applied to combinatorial optimization problems. We describe necessary adaptations and arising issues as well as experimental results on several test problems. All surrogate models are optimised with a Genetic Algorithm (GA). To yield a comprehensive comparison, EGO and Kriging are compared to an earlier suggested Radial Basis Function Network, a linear modelling approach, as well as model-free optimisation with random search and GA. EGO clearly outperforms the competing approaches on most of the tested problem instances.}, notes = {Also known as \cite{2598282} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Arnaldo:2014:GECCO, author = {Ignacio Arnaldo and Krzysztof Krawiec and Una-May O'Reilly}, title = {Multiple regression genetic programming}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {879--886}, keywords = {genetic algorithms, genetic programming}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598291}, doi = {doi:10.1145/2576768.2598291}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We propose a new means of executing a genetic program which improves its output quality. Our approach, called Multiple Regression Genetic Programming (MRGP) decouples and linearly combines a program's subexpressions via multiple regression on the target variable. The regression yields an alternate output: the prediction of the resulting multiple regression model. It is this output, over many fitness cases, that we assess for fitness, rather than the program's execution output. MRGP can be used to improve the fitness of a final evolved solution. On our experimental suite, MRGP consistently generated solutions fitter than the result of competent GP or multiple regression. When integrated into GP, inline MRGP, on the basis of equivalent computational budget, outperforms competent GP while also besting post-run MRGP. Thus MRGP's output method is shown to be superior to the output of program execution and it represents a practical, cost neutral, improvement to GP.}, notes = {Also known as \cite{2598291} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Cano:2014:GECCO, author = {Alberto Cano and Sebastian Ventura}, title = {GPU-parallel subtree interpreter for genetic programming}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {887--894}, keywords = {genetic algorithms, genetic programming}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598272}, doi = {doi:10.1145/2576768.2598272}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Genetic Programming (GP) is a computationally intensive technique but its nature is embarrassingly parallel. Graphic Processing Units (GPUs) are many-core architectures which have been widely employed to speed up the evaluation of GP. In recent years, many works have shown the high performance and efficiency of GPUs on evaluating both the individuals and the fitness cases in parallel. These approaches are known as population parallel and data parallel. This paper presents a parallel GP interpreter which extends these approaches and adds a new parallelisation level based on the concurrent evaluation of the individual's subtrees. A GP individual defined by a tree structure with nodes and branches comprises different depth levels in which there are independent subtrees which can be evaluated concurrently. Threads can cooperate to evaluate different subtrees and share the results via GPU's shared memory. The experimental results show the better performance of the proposal in terms of the GP operations per second (GPops/s) that the GP interpreter is capable of processing, achieving up to 21 billion GPops/s using a NVIDIA 480 GPU. However, some issues raised due to limitations of currently available hardware are to be overcome by the dynamic parallelisation capabilities of the next generation of GPUs.}, notes = {Also known as \cite{2598272} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{DeMelo:2014:GECCO, author = {Vin\'{\i}cius Veloso {De Melo}}, title = {Kaizen programming}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {895--902}, keywords = {genetic algorithms, genetic programming}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598264}, doi = {doi:10.1145/2576768.2598264}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper presents Kaizen Programming, an evolutionary tool based on the concepts of Continuous Improvement from Kaizen Japanese methodology. One may see Kaizen Programming as a new paradigm since, as opposed to classical evolutionary algorithms where individuals are complete solutions, in Kaizen Programming each expert proposes an idea to solve part of the problem, thus a solution is composed of all ideas together. Consequently, evolution becomes a collaborative approach instead of an egocentric one. An idea's quality (analog to an individual's fitness) is not how good it fits the data, but a measurement of its contribution to the solution, which improves the knowledge about the problem. Differently from evolutionary algorithms that simply perform trial-and-error search, one can determine, exactly, parts of the solution that should be removed or improved. That property results in the reduction in bloat, number of function evaluations, and computing time. Even more important, the Kaizen Programming tool, proposed to solve symbolic regression problems, builds the solutions as linear regression models - not linear in the variables, but linear in the parameters, thus all properties and characteristics of such statistical tool are valid. Experiments on benchmark functions proposed in the literature show that Kaizen Programming easily outperforms Genetic Programming and other methods, providing high quality solutions for both training and testing sets while requiring a small number of function evaluations.}, notes = {Also known as \cite{2598264} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Fitzgerald:2014:GECCO, author = {Jeannie Fitzgerald and Conor Ryan}, title = {On size, complexity and generalisation error in GP}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {903--910}, keywords = {genetic algorithms, genetic programming}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598346}, doi = {doi:10.1145/2576768.2598346}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {For some time, Genetic Programming research has lagged behind the wider Machine Learning community in the study of generalisation, where the decomposition of generalisation error into bias and variance components is well understood. However, recent Genetic Programming contributions focusing on complexity, size and bloat as they relate to over-fitting have opened up some interesting avenues of research. In this paper, we carry out a simple empirical study on five binary classification problems. The study is designed to discover what effects may be observed when program size and complexity are varied in combination, with the objective of gaining a better understanding of relationships which may exist between solution size, operator complexity and variance error. The results of the study indicate that the simplest configuration, in terms of operator complexity, consistently results in the best average performance, and in many cases, the result is significantly better. We further demonstrate that the best results are achieved when this minimum complexity set-up is combined with a less than parsimonious permissible size.}, notes = {Also known as \cite{2598346} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Harada:2014:GECCO, author = {Tomohiro Harada and Keiki Takadama}, title = {Asynchronously evolving solutions with excessively different evaluation time by reference-based evaluation}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {911--918}, keywords = {genetic algorithms, genetic programming}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598330}, doi = {doi:10.1145/2576768.2598330}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The asynchronous evolution has an advantage when evolving solutions with excessively different evaluation time since the asynchronous evolution evolves each solution independently without waiting for other evaluations, unlike the synchronous evolution requires evaluations of all solutions at the same time. As a novel asynchronous evolution approach, this paper proposes Asynchronous Reference-based Evaluation (ARE) that asynchronously selects good parents by the tournament selection using reference solution in order to evolve solutions through a crossover of the good parents. To investigate the effectiveness of ARE in the case of evolving solutions with excessively different evaluation time, this paper applies ARE to Genetic Programming (GP), and compares GP using ARE (ARE-GP) with GP using (mu+lambda) selection ((mu+lambda)-GP) as the synchronous approach in particular situation where the evaluation time of individuals differs from each other. The intensive experiments have revealed the following implications: (1) ARE-GP greatly outperforms (mu+lambda)-GP from the viewpoint of the elapsed unit time in the parallel computation environment, (2) ARE-GP can evolve individuals without decreasing the searching ability in the situation where the computing speed of each individual differs from each other and some individuals fail in their execution.}, notes = {Also known as \cite{2598330} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Helmuth:2014:GECCO, author = {Thomas Helmuth and Lee Spector}, title = {Word count as a traditional programming benchmark problem for genetic programming}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {919--926}, keywords = {genetic algorithms, genetic programming}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598230}, doi = {doi:10.1145/2576768.2598230}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The Unix utility program wc, which stands for word count, takes any number of files and prints the number of newlines, words, and characters in each of the files. We show that genetic programming can find programs that replicate the core functionality of the wc utility, and propose this problem as a traditional programming benchmark for genetic programming systems. This wc problem features key elements of programming tasks that often confront human programmers, including requirements for multiple data types, a large instruction set, control flow, and multiple outputs. Furthermore, it mimics the behavior of a real-world utility program, showing that genetic programming can automatically synthesize programs with general utility. We suggest statistical procedures that should be used to compare performances of different systems on traditional programming problems such as the wc problem, and present the results of a short experiment using the problem. Finally, we give a short analysis of evolved solution programs, showing how they make use of traditional programming concepts.}, notes = {Also known as \cite{2598230} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Hunt:2014:GECCO, author = {Rachel Hunt and Mark Johnston and Mengjie Zhang}, title = {Evolving "less-myopic" scheduling rules for dynamic job shop scheduling with genetic programming}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {927--934}, keywords = {genetic algorithms, genetic programming}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598224}, doi = {doi:10.1145/2576768.2598224}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Job Shop Scheduling (JSS) is a complex real-world problem aiming to optimise a measure of delivery speed or customer satisfaction by determining a schedule for processing jobs on machines. A major disadvantage of using a dispatching rule (DR) approach to solving JSS problems is their lack of a global perspective of the current and potential future state of the shop. We investigate a genetic programming based hyper-heuristic (GPHH) approach to develop less-myopic DRs for dynamic JSS. Results show that in the dynamic ten machine job shop, incorporating features of the state of the wider shop, and the stage of a job's journey through the shop, improves the mean performance, and decreases the standard deviation of performance of the best evolved rules.}, notes = {Also known as \cite{2598224} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Krawiec:2014:GECCO, author = {Krzysztof Krawiec and Una-May O'Reilly}, title = {Behavioral programming: a broader and more detailed take on semantic GP}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {935--942}, keywords = {genetic algorithms, genetic programming}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598288}, doi = {doi:10.1145/2576768.2598288}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In evolutionary computation, the fitness of a candidate solution conveys sparse feedback. Yet in many cases, candidate solutions can potentially yield more information. In genetic programming (GP), one can easily examine program behaviour on particular fitness cases or at intermediate execution states. However, how to exploit it to effectively guide the search remains unclear. In this study we apply machine learning algorithms to features describing the intermediate behavior of the executed program. We then drive the standard evolutionary search with additional objectives reflecting this intermediate behavior. The machine learning functions independent of task-specific knowledge and discovers potentially useful components of solutions (subprograms), which we preserve in an archive and use as building blocks when composing new candidate solutions. In an experimental assessment on a suite of benchmarks, the proposed approach proves more capable of finding optimal and/or well-performing solutions than control methods.}, notes = {Also known as \cite{2598288} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Kvren:2014:GECCO, author = {Tom\'{a}\v{s} K\v{r}en and Roman Neruda}, title = {Utilization of reductions and abstraction elimination in typed genetic programming}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {943--950}, keywords = {genetic algorithms, genetic programming}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598361}, doi = {doi:10.1145/2576768.2598361}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Lambda calculus representation of programs offers a more expressive alternative to traditional S-expressions. In this paper we discuss advantages of this representation coming from the use of reductions (beta and eta) and a way to overcome disadvantages caused by variables occurring in the programs by use of the abstraction elimination algorithm. We discuss the role of those reductions in the process of generating initial population and propose two novel crossover operations based on abstraction elimination capable of handling general form of typed lambda term while being a straight generalization of the standard crossover operation. We compare their performances using the even parity benchmark problem.}, notes = {Also known as \cite{2598361} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Langdon:2014:GECCO, author = {William B. Langdon and Marc Modat and Justyna Petke and Mark Harman}, title = {Improving 3D medical image registration CUDA software with genetic programming}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {951--958}, keywords = {genetic algorithms, genetic programming}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598244}, doi = {doi:10.1145/2576768.2598244}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Genetic Improvement (GI) is shown to optimise, in some cases by more than 35percent, a critical component of healthcare industry software across a diverse range of six nVidia graphics processing units (GPUs). GP and other search based software engineering techniques can automatically optimise the current rate limiting CUDA parallel function in the NiftyReg open source C++ project used to align or register high resolution nuclear magnetic resonance NMRI and other diagnostic NIfTI images. Future Neurosurgery techniques will require hardware acceleration, such as GPGPU, to enable real time comparison of three dimensional in theatre images with earlier patient images and reference data. With millimetre resolution brain scan measurements comprising more than ten million voxels the modified kernel can process in excess of 3 billion active voxels per second.}, notes = {Also known as \cite{2598244} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Wong:2014:GECCO, author = {Pak-Kan Wong and Leung-Yau Lo and Man-Leung Wong and Kwong-Sak Leung}, title = {Grammar-based genetic programming with dependence learning and bayesian network classifier}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {959--966}, keywords = {genetic algorithms, genetic programming}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598256}, doi = {doi:10.1145/2576768.2598256}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Grammar-Based Genetic Programming formalises constraints on the solution structure based on domain knowledge to reduce the search space and generate grammatically correct individuals. Nevertheless, building blocks in a program can often be dependent, so the effective search space can be further reduced. Approaches have been proposed to learn the dependence using probabilistic models and shown to be useful in finding the optimal solutions with complex structure. It raises questions on how to use the individuals in the population to uncover the underlying dependence. Usually, only the good individuals are selected. To model the dependence better, we introduce Grammar-Based Genetic Programming with Bayesian Network Classifier (GBGPBC) which also uses poorer individuals. With the introduction of class labels, we further propose a refinement technique on probability distribution based on class label. Our results show that GBGPBC performs well on two benchmark problems. These techniques boost the performance of our system.}, notes = {Also known as \cite{2598256} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Aulig:2014:GECCO, author = {Nikola Aulig and Markus Olhofer}, title = {Neuro-evolutionary topology optimization of structures by utilizing local state features}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {967--974}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598314}, doi = {doi:10.1145/2576768.2598314}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper we propose a novel method for the topology optimisation of mechanical structures, based on a hybrid combination of a neuro-evolution with a gradient-based optimiser. Conventional gradient-based topology optimization requires problem-specific sensitivity information, however this is not available in the general case. The proposed method substitutes the analytical gradient by an artificial neural network approximation model, whose parameters are learnt by an evolutionary algorithm. Advantageous is that the number of parameters in the evolutionary search is not directly coupled to the mesh of the discretised design, potentially enabling the optimisation of fine discretisations. Concretely, the network maps features, obtained for each element of the discretized design, to an update signal, that is used to determine a new design. A new network is learned for every iteration of the topology optimization. The proposed method is evaluated on the minimum compliance design problem, with two different sets of features. Feasible designs are obtained, showing that the neural network is able to successfully replace analytical sensitivity information. In concluding remarks, we discuss the significant improvement that is achieved when including the strain energy as feature.}, notes = {Also known as \cite{2598314} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Jansen:2014:GECCO, author = {Thomas Jansen and Christine Zarges}, title = {Evolutionary algorithms and artificial immune systems on a bi-stable dynamic optimisation problem}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {975--982}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598344}, doi = {doi:10.1145/2576768.2598344}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Dynamic optimisation is an important area of application for evolutionary algorithms and other randomised search heuristics. Theoretical investigations are currently far behind practical successes. Addressing this deficiency a bi-stable dynamic optimisation problem is introduced and the performance of standard evolutionary algorithms and artificial immune systems is assessed. Deviating from the common theoretical perspective that concentrates on the expected time to find a global optimum (again) here the `any time performance' of the algorithms is analysed, i.e., the expected function value at each step. Basis for the analysis is the recently introduced perspective of fixed budget computations. Different dynamic scenarios are considered which are characterised by the length of the stable phases. For each scenario different population sizes are examined. It is shown that the evolutionary algorithms tend to have superior performance in almost all cases.}, notes = {Also known as \cite{2598344} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Khayrattee:2014:GECCO, author = {Azhar Khayrattee and Georgios C. Anagnostopoulos}, title = {Derivative free optimization using a population-based stochastic gradient estimator}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {983--990}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598365}, doi = {doi:10.1145/2576768.2598365}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper we introduce a derivative-free optimization method that is derived from a population based stochastic gradient estimator. We first demonstrate some properties of this estimator and show how it is expected to always yield a descent direction. We analytically show that the difference between the expected function value and the optimum decreases exponentially for strongly convex functions and the expected distance between the current point and the optimum has an upper bound. Then we experimentally tune the parameters of our algorithm to get the best performance. Finally, we use the Black-Box-Optimization-Benchmarking test function suite to evaluate the performance of the algorithm. The experiments indicate that the method offer notable performance advantages especially, when applied to objective functions that are ill-conditioned and potentially multi-modal. This result, coupled with the low computational cost when compared to Quasi-Newton methods, makes it quite attractive.}, notes = {Also known as \cite{2598365} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Rodrigues:2014:GECCO, author = {Silvio Rodrigues and Pavol Bauer and Peter A.N. Bosman}, title = {A novel population-based multi-objective CMA-ES and the impact of different constraint handling techniques}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {991--998}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598329}, doi = {doi:10.1145/2576768.2598329}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES) is a well-known, state-of-the-art optimization algorithm for single-objective real-valued problems, especially in black-box settings. Although several extensions of CMA-ES to multi-objective (MO) optimization exist, no extension incorporates a key component of the most robust and general CMA-ES variant: the association of a population with each Gaussian distribution that drives optimization. To achieve this, we use a recently introduced framework for extending population-based algorithms from single- to multi-objective optimization. We compare, using six well-known benchmark problems, the performance of the newly constructed MO-CMA-ES with existing variants and with the estimation of distribution algorithm (EDA) known as iMAMaLGaM, that is also an instance of the framework, extending the single-objective EDA iAMaLGaM to MO. Results underline the advantages of being able to use populations. Because many real-world problems have constraints, we also study the use of four constraint-handling techniques. We find that CMA-ES is typically less robust to these techniques than iAMaLGaM. Moreover, whereas we could verify that a penalty method that was previously used in literature leads to fast convergence, we also find that it has a high risk of finding only nearly, but not entirely, feasible solutions. We therefore propose that other constraint-handling techniques should be preferred in general.}, notes = {Also known as \cite{2598329} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Tinos:2014:GECCOa, author = {Renato Tin\'{o}s and Darrell Whitley and Adele Howe}, title = {Use of explicit memory in the dynamic traveling salesman problem}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {999--1006}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598247}, doi = {doi:10.1145/2576768.2598247}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In the dynamic traveling salesman problem (DTSP), the weights and vertices of the graph representing the TSP are allowed to change during the optimization. This work first discusses some issues related to the use of evolutionary algorithms in the DTSP. When efficient algorithms used for the static TSP are applied with restart in the DTSP, we observe that only some edges are generally inserted in and removed from the best solutions after the changes. This result indicates a possible beneficial use of memory approaches, usually employed in cyclic dynamic environments. We propose a memory approach and a hybrid approach that combines our memory approach with the elitism-based immigrants genetic algorithm (EIGA). We compare these two algorithms to four existing algorithms and show that memory approaches can be beneficial for the DTSP with random changes.}, notes = {Also known as \cite{2598247} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{hidalgo:2014:GECCO, author = {J. Ignacio hidalgo and J. Manuel Colmenar and Jose L. Risco-Mart\'{\i}n and Carlos S\'{a}nchez-Lacruz and Juan Lanchares and Oscar Garnica and Josefa D\'{\i}az}, title = {Solving GA-hard problems with EMMRS and GPGPUs}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {1007--1014}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598219}, doi = {doi:10.1145/2576768.2598219}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Different techniques have been proposed to tackle GA-Hard problems. Some techniques work with different encodings and representations, other use reordering operators and several, such as the Evolutionary Mapping Method (EMM), apply genotype-phenotype mappings. EMM uses multiple chromosomes in a single cell for mating with another cell within a single population. Although EMM gave good results, it fails on solving some deceptive problems. In this line, EMMRS (EMM with Replacement and Shift) adds a new operator, consisting on doing a replacement and a shift of some of the bits within the chromosome. Results showed the efficiency of the proposal on deceptive problems. However, EMMRS was not tested with other kind of hard problems. In this paper we have adapted EMMRS for solving the Traveling Salesman Problem (TSP). The encodings and genetic operators for solving the TSP are quite different to those applied on deceptive problems. In addition, execution times recommended the parallelization of the GA. We implemented a GPU parallel version. We present here some preliminary results proving that Evolutionary Mapping Method with Replacement and Shift gives good results not only in terms of quality but also in terms of speedup on its GPU parallel version for some instances of the TSP problem.}, notes = {Also known as \cite{2598219} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Hrbacek:2014:GECCO, author = {Radek Hrbacek and Lukas Sekanina}, title = {Towards highly optimized cartesian genetic programming: from sequential via SIMD and thread to massive parallel implementation}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {1015--1022}, keywords = {genetic algorithms, genetic programming, cartesian genetic programming}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598343}, doi = {doi:10.1145/2576768.2598343}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Most implementations of Cartesian genetic programming (CGP) which can be found in the literature are sequential. However, solving complex design problems by means of genetic programming requires parallel implementations of search methods and fitness functions. This paper deals with the design of highly optimized implementations of CGP and their detailed evaluation in the task of evolutionary circuit design. Several sequential implementations of CGP have been analyzed and the effect of various additional optimizations has been investigated. Furthermore, the parallelism at the instruction, data, thread and process level has been applied in order to take advantage of modern processor architectures and computer clusters. Combinational adders and multipliers have been chosen to give a performance comparison with state of the art methods.}, notes = {Also known as \cite{2598343} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Jaros:2014:GECCO, author = {Jiri Jaros and Radek Tyrala}, title = {GPU-accelerated evolutionary design of the complete exchange communication on wormhole networks}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {1023--1030}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598315}, doi = {doi:10.1145/2576768.2598315}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The communication overhead is one of the main challenges in the exascale era, where millions of compute cores are expected to collaborate on solving complex jobs. However, many algorithms will not scale since they require complex global communication and synchronisation. In order to perform the communication as fast as possible, contentions, blocking and deadlock must be avoided. Recently, we have developed an evolutionary tool producing fast and safe communication schedules reaching the lower bound of the theoretical time complexity. Unfortunately, the execution time associated with the evolution process raises up to tens of hours, even when being run on a multi-core processor. In this paper, we propose a revised implementation accelerated by a single Graphic Processing Unit (GPU) delivering speed-up of 5 compared to a quad-core CPU. Subsequently, we introduce an extended version employing up to 8 GPUs in a shared memory environment offering a speed-up of almost 30. This significantly extends the range of interconnection topologies we can cover.}, notes = {Also known as \cite{2598315} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Ludwig:2014:GECCOa, author = {Simone A. Ludwig}, title = {MapReduce-based optimization of overlay networks using particle swarm optimization}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {1031--1038}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598269}, doi = {doi:10.1145/2576768.2598269}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {An overlay network is a virtual network that is built on top of the real network such as the Internet. Cloud computing, peer-to-peer networks, and client-server applications are examples of overlay networks since their nodes run on top of the Internet. The major needs of overlay networks are content distribution and caching, file sharing, improved routing, multicast and streaming, ordered message delivery, and enhanced security and privacy. The focus of this paper is the optimization of overlay networks using a Particle Swarm Optimization (PSO) approach. However, since the ever growing need for more infrastructure causes the number of network nodes to grow significantly, the parallelization of the PSO approach becomes a necessity. In this paper, the MapReduce concept, proposed by Google, is adopted for the PSO approach in order to be able to optimize large-scale networks. MapReduce is easy to implement since it is based on the divide and conquer method, and implementation frameworks such has Hadoop allow for scalability and fault tolerance. Experiments of the MapReduce based PSO algorithm are performed to investigate the solution quality and scalability of the approach.}, notes = {Also known as \cite{2598269} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Luque:2014:GECCO, author = {Gabriel Luque and Enrique Alba}, title = {Enhancing parallel cooperative trajectory based metaheuristics with path relinking}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {1039--1046}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598337}, doi = {doi:10.1145/2576768.2598337}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper proposes a novel algorithm combining path relinking with a set of cooperating trajectory based parallel algorithms to yield a new metaheuristic of enhanced search features. Algorithms based on the exploration of the neighborhood of a single solution, like simulated annealing (SA), have offered accurate results for a large number of real-world problems in the past. Because of their trajectory based nature, some advanced models such as the cooperative one are competitive in academic problems, but still show many limitations in addressing large scale instances. In addition, the field of parallel models for trajectory methods has not deeply been studied yet (at least in comparison with parallel population based models). In this work, we propose a new hybrid algorithm which improves cooperative single solution techniques by using path relinking, allowing both to reduce the global execution time and to improve the efficacy of the method. We test here this new model using a large benchmark of instances of two well-known NP-hard problems: MAXSAT and QAP, with competitive results.}, notes = {Also known as \cite{2598337} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Mambrini:2014:GECCO, author = {Andrea Mambrini and Dirk Sudholt}, title = {Design and analysis of adaptive migration intervals in parallel evolutionary algorithms}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {1047--1054}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598347}, doi = {doi:10.1145/2576768.2598347}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The migration interval is one of the fundamental parameters governing the dynamic behaviour of island models. Yet, there is little understanding on how this parameter affects performance, and how to optimally set it given a problem in hand. We propose schemes for adapting the migration interval according to whether fitness improvements have been found. As long as no improvement is found, the migration interval is increased to minimise communication. Once the best fitness has improved, the migration interval is decreased to spread new best solutions more quickly. We provide a method for analysing the expected running time and the communication effort, defined as the expected number of migrants sent. Example applications of this method to common example functions show that our adaptive schemes are able to compete with, or even outperform, the optimal fixed choice of the migration interval, with regard to running time and communication effort.}, notes = {Also known as \cite{2598347} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Probst:2014:GECCO, author = {Malte Probst and Franz Rothlauf and J\"{o}rn Grahl}, title = {An implicitly parallel EDA based on restricted boltzmann machines}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {1055--1062}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598273}, doi = {doi:10.1145/2576768.2598273}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We present a parallel version of RBM-EDA. RBM-EDA is an Estimation of Distribution Algorithm (EDA) that models dependencies between decision variables using a Restricted Boltzmann Machine (RBM). In contrast to other EDAs, RBM-EDA mainly uses matrix-matrix multiplications for model estimation and sampling. Hence, for implementation, standard libraries for linear algebra can be used. This allows an easy parallelisation. The probabilistic model of the parallel version and the version on a single core are identical. We explore the speedups gained from running RBM-EDA on a Graphics Processing Unit. For problems of bounded difficulty like deceptive traps, parallel RBM-EDA is faster by several orders of magnitude (up to 750 times) in comparison to a single-threaded implementation on a CPU. As the speedup grows linearly with problem size, parallel RBM-EDA may be particularly useful for large problems.}, notes = {Also known as \cite{2598273} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Bartoli:2014:GECCO, author = {Alberto Bartoli and Andrea {De Lorenzo} and Eric Medvet and Fabiano Tarlao}, title = {Playing regex golf with genetic programming}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {1063--1070}, keywords = {genetic algorithms, genetic programming}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598333}, doi = {doi:10.1145/2576768.2598333}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Regex golf has recently emerged as a specific kind of code golf, i.e., unstructured and informal programming competitions aimed at writing the shortest code solving a particular problem. A problem in regex golf consists in writing the shortest regular expression which matches all the strings in a given list and does not match any of the strings in another given list. The regular expression is expected to follow the syntax of a specified programming language, e.g., Javascript or PHP. In this paper, we propose a regex golf player internally based on Genetic Programming. We generate a population of candidate regular expressions represented as trees and evolve such population based on a multi-objective fitness which minimises the errors and the length of the regular expression. We assess experimentally our player on a popular regex golf challenge consisting of 16 problems and compare our results against those of a recently proposed algorithm---the only one we are aware of.Our player obtains scores which improve over the baseline and are highly competitive also with respect to human players. The time for generating a solution is usually in the order of tens minutes, which is arguably comparable to the time required by human players.}, notes = {Also known as \cite{2598333} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Bucur:2014:GECCO, author = {Doina Bucur and Giovanni Iacca and Giovanni Squillero and Alberto Tonda}, title = {The tradeoffs between data delivery ratio and energy costs in wireless sensor networks: a multi-objectiveevolutionary framework for protocol analysis}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {1071--1078}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598384}, doi = {doi:10.1145/2576768.2598384}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Wireless sensor network (WSN) routing protocols, e.g., the Collection Tree Protocol (CTP), are designed to adapt in an ad-hoc fashion to the quality of the environment. WSNs thus have high internal dynamics and complex global behaviour. Classical techniques for performance evaluation (such as testing or verification) fail to uncover the cases of extreme behavior which are most interesting to designers. We contribute a practical framework for performance evaluation of WSN protocols. The framework is based on multi-objective optimisation, coupled with protocol simulation and evaluation of performance factors. For evaluation, we consider the two crucial functional and non-functional performance factors of a WSN, respectively: the ratio of data delivery from the network (DDR), and the total energy expenditure of the network (COST). We are able to discover network topological configurations over which CTP has unexpectedly low DDR and/or high COST performance, and expose full Pareto fronts which show what the possible performance tradeoffs for CTP are in terms of these two performance factors. Eventually, Pareto fronts allow us to bound the state space of the WSN, a fact which provides essential knowledge to WSN protocol designers.}, notes = {Also known as \cite{2598384} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Cheney:2014:GECCO, author = {Nicholas Cheney and Ethan Ritz and Hod Lipson}, title = {Automated vibrational design and natural frequency tuning of multi-material structures}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {1079--1086}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598362}, doi = {doi:10.1145/2576768.2598362}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Natural frequency tuning is a vital engineering problem. Every structure has natural frequencies, where vibrational loading at nearby frequencies excite the structure. This causes the structure to resonate, oscillating until energy is dissipated through friction or structural failure. Examples of fragility and distress from vibrational loading include civil structures during earthquakes or aircraft rotor blades. Tuning the structure's natural frequencies away from these vibrations increases the structure's robustness. Conversely, tuning towards the frequencies caused by vibrations can channel power into energy harvesting systems. Despite its importance, natural frequency tuning is often performed ad-hoc, by attaching external vibrational absorbers to a structure. This is usually adequate only for the lowest (fundamental) resonant frequencies, yet remains standard practice due to the unintuitive and difficult nature of the problem. Given Evolutionary Algorithms' (EA's) ability to solve these types of problems, we propose to approach this problem with the EA CPPN-NEAT to evolve multi-material structures which resonate at multiple desired natural frequencies without external damping. The EA assigns the material type of each voxel within the discretised space of the object's existing topology, preserving the object's shape and using only its material composition to shape its frequency response.}, notes = {Also known as \cite{2598362} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Curran:2014:GECCO, author = {William Curran and Adrian Agogino and Kagan Tumer}, title = {Hierarchical simulation for complex domains: air traffic flow management}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {1087--1094}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598385}, doi = {doi:10.1145/2576768.2598385}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {A key element in the continuing growth of air traffic is the increased use of automation. The Next Generation (Next-Gen) Air Traffic System will include automated decision support systems and satellite navigation that will let pilots know the precise locations of other aircraft around them. This Next-Gen suggestion system can assist pilots in making good decisions when they have to direct the aircraft themselves. However, effective automation is critical in achieving the capacity and safety goals of the Next-Gen Air Traffic System. In this paper we show that evolutionary algorithms can be used to achieve this effective automation. However, it is not feasible to use a standard evolutionary algorithm learning approach in such a detailed simulation. Therefore, we apply a hierarchical simulation approach to an air traffic congestion problem where agents must reach a destination while avoiding separation violations. Due to the dynamic nature of this problem, agents need to learn fast. Therefore, we apply low fidelity simulation for agents learning their destination, and a high fidelity simulation employing the Next-Gen technology for learning separation assurance. The hierarchical simulation approach increases convergence rate, leads to a better performing solution, and lowers computational complexity by up to 50 times.}, notes = {Also known as \cite{2598385} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{DeFalco:2014:GECCO, author = {Ivanoe {De Falco} and Antonio {Della Cioppa} and Domenico Maisto and Umberto Scafuri and Ernesto Tarantino}, title = {Using an adaptive invasion-based model for fast range image registration}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {1095--1102}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598340}, doi = {doi:10.1145/2576768.2598340}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper presents an adaptive model for automatically pair-wise registering range images. Given two images and set one as the model, the aim is to find the best possible spatial transformation of the second image causing 3D reconstruction of the original object. Registration is effected here by using a distributed Differential Evolution algorithm characterised by a migration model inspired by the phenomenon known as biological invasion, and by applying a parallel Grid Closest Point algorithm. The distributed algorithm is endowed with two adaptive updating schemes to set the mutation and the crossover parameters, whereas the subpopulation size is assumed to be set in advance and kept fixed throughout the evolution process. The adaptive procedure is tied to the migration and is guided by a performance measure between two consecutive migrations. Experimental results achieved by our approach show the capability of this adaptive method of picking up efficient transformations of images and are compared with those of a recently proposed evolutionary algorithm. This efficiency is evaluated in terms of both quality and robustness of the reconstructed 3D image, and of computational cost.}, notes = {Also known as \cite{2598340} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Eicholtz:2014:GECCO, author = {Matthew Eicholtz and Levent Burak Kara and Jason Lohn}, title = {Recognizing planar kinematic mechanisms from a single image using evolutionary computation}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {1103--1110}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598354}, doi = {doi:10.1145/2576768.2598354}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper, a method is presented that automatically recognises kinematic mechanisms from textbook images using an evolutionary algorithm to complement computer vision techniques for object detection. Specifically, a nondominated sorting genetic algorithm (NSGA-II) is used to optimise the number and position of mechanical joints in an image and corresponding joint connections (i.e. rigid bodies) such that Pareto front solutions maximise image consistency and mechanical feasibility. A well-known object detector is used as an example method for locating joints, and local image features between pairwise detected joints are used to predict likely connections. The performance of the algorithm using these specific vision techniques is compared to a parametrised detection scheme in order to decouple the efficacy of the object detector from the evolutionary algorithm. Experiments were performed to validate this approach on selected images from a custom dataset, and the results demonstrate reasonable success in both accuracy and speed.}, notes = {Also known as \cite{2598354} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Gholami:2014:GECCO, author = {Mohammad M.O. Gholami and Brian J. Ross}, title = {Passive solar building design using genetic programming}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {1111--1118}, keywords = {genetic algorithms, genetic programming}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598211}, doi = {doi:10.1145/2576768.2598211}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Passive solar building design considers the effect that sunlight has on energy usage. The goal is to reduce the need for artificial cooling and heating devices, thereby saving energy costs. A number of competing design objectives can arise. Window heat gain during winter requires large windows. These same windows, however, reduce energy efficiency during nights and summers. Other model requirements add further complications, which creates a challenging optimisation problem. We use genetic programming for passive solar building design. The EnergyPlus system is used to evaluate energy consumption. It considers factors ranging from model construction (shape, windows, materials) to location particulars (latitude/longitude, weather, time of day/year). We use a strongly typed design language to build 3D models, and multi-objective fitness to evaluate the multiple design objectives. Experimental results showed that balancing window heat gain and total energy use is challenging, although our multi-objective strategy could find interesting compromises. Many factors (roof shape, material selection) were consistently optimised by evolution. We also found that geographic aspects of the location play a critical role in the final building design.}, notes = {Also known as \cite{2598211} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Gupta:2014:GECCO, author = {Shikha Gupta and Sheetal Taneja and Naveen Kumar}, title = {Quantum inspired genetic algorithm for community structure detection in social networks}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {1119--1126}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598277}, doi = {doi:10.1145/2576768.2598277}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Community detection is a key problem in social network analysis. We propose a two-phase algorithm for detecting community structure in social networks. First phase employs a local-search method to group together nodes that have a high chance of falling in a single community. The second phase is bi-partitioning strategy that optimises network modularity and deploys a variant of quantum-inspired genetic algorithm. The proposed algorithm does not require any knowledge of the number of communities beforehand and works well for both directed and undirected networks. Experiments on synthetic and real-life networks show that the method is able to successfully reveal community structure with high modularity.}, notes = {Also known as \cite{2598277} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Hinckley:2014:GECCO, author = {David W. Hinckley,Jr. and Karol Zieba and Darren L. Hitt and Margaret J. Eppstein}, title = {Evolved spacecraft trajectories for low earth orbit}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {1127--1134}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598246}, doi = {doi:10.1145/2576768.2598246}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper we use Differential Evolution (DE), with best evolved results refined using a Nelder-Mead optimisation, to solve complex problems in orbital mechanics relevant to low Earth orbits (LEO). A class of so-called 'Lambert Problems' is examined. We evolve impulsive initial velocity vectors giving rise to intercept trajectories that take a spacecraft from given initial positions to specified target positions. We seek to minimise final positional error subject to time-of-flight and/or energy (fuel) constraints. We first validate that the method can recover known analytical solutions obtainable with the assumption of Keplerian motion. We then apply the method to more complex and realistic non-Keplerian problems incorporating trajectory perturbations arising in LEO due to the Earth's oblateness and rarefied atmospheric drag. The viable trajectories obtained for these difficult problems suggest the robustness of our computational approach for real-world orbital trajectory design in LEO situations where no analytical solution exists.}, notes = {Also known as \cite{2598246} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Hudson:2014:GECCO, author = {Jonathan Hudson and Majid Ghaderi and J\"{o}rg Denzinger}, title = {Dynamic multi-dimensional PSO with indirect encoding for proportional fair constrained resource allocation}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {1135--1142}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598387}, doi = {doi:10.1145/2576768.2598387}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Dynamic particle swarm optimization (PSO) problems are generally characterised by the exhaustively examined issues of the changing location of optima, the changing fitness of optima, and measurement noise/errors. However, the challenging issue of continuously changing problem dimensionality has not been similarly examined. Given that in anytime dynamic resource allocation it is necessary to maintain a high quality solution, we argue that, rather than restarting the PSO algorithm, a more appropriate approach is to design an algorithm that robustly handles changing problem dimensionality. Specifically, we propose an indirect particle encoding scheme specifically designed for a dynamic multi-dimensional PSO algorithm for proportional fair constrained resource allocation. This PSO algorithm is implemented for the proportional fair allocation of power and users to channels within a simulation of an Orthogonal Frequency-Division Multiple Access (OFDMA) wireless network with mobile users switching cells as they traverse the simulation environment. The proposed PSO algorithm is evaluated using simulations, which demonstrate the ability of the proposed indirect encoding scheme to maximise the overall proportional fair optimisation goal, without unfairly penalising the individual components of the solution related to newly introduced problem dimensions.}, notes = {Also known as \cite{2598387} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Levy:2014:GECCO, author = {Erez Levy and Omid E. David and Nathan S. Netanyahu}, title = {Genetic algorithms and deep learning for automatic painter classification}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {1143--1150}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598287}, doi = {doi:10.1145/2576768.2598287}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper we describe the problem of painter classification, and propose a novel hybrid approach incorporating genetic algorithms (GA) and deep restricted Boltzmann machines (RBM). Given a painting, we extract features using both generic image processing (IP) functions (e.g., fractal dimension, Fourier spectra coefficients, texture coefficients, etc.) and unsupervised deep learning (using deep RBMs). We subsequently compare several supervised learning techniques for classification using the extracted features as input. The results show that the weighted nearest neighbour (WNN) method, for which the weights are evolved using GA, outperforms both a support vector machine (SVM) classifier and a standard nearest neighbor classifier, achieving over 90percent classification accuracy for the 3-painter problem (an improvement of over 10percent relatively to previous results due to standard feature extraction only).}, notes = {Also known as \cite{2598287} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Praxedes:2014:GECCO, author = {Eric da Silva Praxedes and Adriano Soares Koshiyama and Elita Selmara Abreu and Douglas Mota Dias and Marley Maria Bernardes Rebuzzi Vellasco and Marco Aur\'{e}lio Cavalcanti Pacheco}, title = {Lithology discrimination using seismic elastic attributes: a genetic fuzzy classifier approach}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {1151--1158}, keywords = {genetic algorithms, genetic programming}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598319}, doi = {doi:10.1145/2576768.2598319}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {One of the most important issues in oil \& gas industry is the lithological identification. Lithology is the macroscopic description of the physical characteristics of a rock. This work proposes a new methodology for lithological discrimination, using GPF-CLASS model (Genetic Programming for Fuzzy Classification) a Genetic Fuzzy System based on Multi-Gene Genetic Programming. The main advantage of our approach is the possibility to identify, through seismic patterns, the rock types in new regions without requiring opening wells. Thus, we seek for a reliable model that provides two flexibilities for the experts: evaluate the membership degree of a seismic pattern to the several rock types and the chance to analyse at linguistic level the model output. Therefore, the final tool must afford knowledge discovery and support to the decision maker. Also, we evaluate other 7 classification models (from statistics and computational intelligence), using a database from a well located in Brazilian coast. The results demonstrate the potentialities of GPF-CLASS model when comparing to other classifiers.}, notes = {Also known as \cite{2598319} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Purshouse:2014:GECCO, author = {Robin C. Purshouse and Abdallah K. Ally and Alan Brennan and Daniel Moyo and Paul Norman}, title = {Evolutionary parameter estimation for a theory of planned behaviour microsimulation of alcohol consumption dynamics in an English birth cohort 2003 to 2010}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {1159--1166}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598239}, doi = {doi:10.1145/2576768.2598239}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper presents a new real-world application of evolutionary computation: identifying parameterisations of a theory-driven model that can reproduce alcohol consumption dynamics observed in a population over time. Population alcohol consumption is a complex system, with multiple interactions between economic and social factors and drinking behaviours, the nature and importance of which are not well-understood. Prediction of time trends in consumption is therefore difficult, but essential for robust estimation of future changes in health-related consequences of drinking and for appraising the impact of interventions aimed at changing alcohol use in society. The paper describes a microsimulation approach in which an attitude-behaviour model, Theory of Planned Behaviour, is used to describe the frequency of drinking by individuals. Consumption dynamics in the simulation are driven by changes in the social roles of individuals over time (parenthood, partnership, and paid labour). An evolutionary optimiser is used to identify parameterisations of the Theory that can describe the observed changes in drinking frequency. Niching is incorporated to enable multiple possible parameterisations to be identified, each of which can accurately recreate history but potentially encode quite different future trends. The approach is demonstrated using evidence from the 1979-1985 birth cohort in England between 2003 and 2010.}, notes = {Also known as \cite{2598239} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Rafique:2014:GECCO, author = {M. Zubair Rafique and Ping Chen and Christophe Huygens and Wouter Joosen}, title = {Evolutionary algorithms for classification of malware families through different network behaviors}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {1167--1174}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598238}, doi = {doi:10.1145/2576768.2598238}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The staggering increase of malware families and their diversity poses a significant threat and creates a compelling need for automatic classification techniques. In this paper, we first analyze the role of network behaviour as a powerful technique to automatically classify malware families and their polymorphic variants. Afterwards, we present a framework to efficiently classify malware families by modelling their different network behaviours (such as HTTP, SMTP, UDP, and TCP). We propose protocol-aware and state-space modeling schemes to extract features from malware network behaviours. We analyze the applicability of various evolutionary and non-evolutionary algorithms for our malware family classification framework. To evaluate our framework, we collected a real-world dataset of $6,000$ unique and active malware samples belonging to 20 different malware families. We provide a detailed analysis of network behaviours exhibited by these prevalent malware families. The results of our experiments shows that evolutionary algorithms, like sUpervised Classifier System (UCS), can effectively classify malware families through different network behaviours in real-time. To the best of our knowledge, the current work is the first malware classification framework based on evolutionary classifier that uses different network behaviours.}, notes = {Also known as \cite{2598238} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Rahat:2014:GECCO, author = {Alma As-Aad Mohammad Rahat and Richard M. Everson and Jonathan E. Fieldsend}, title = {Multi-objective routing optimisation for battery-powered wireless sensor mesh networks}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {1175--1182}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598311}, doi = {doi:10.1145/2576768.2598311}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Mesh network topologies are becoming increasingly popular in battery powered wireless sensor networks, primarily due to the extension of network range and resilience against routing failures. However, multi-hop mesh networks suffer from higher energy costs, and the routing strategy directly affects the lifetime of nodes with limited energy sources. Hence while planning routes there are trade-offs to be considered between individual and system-wide battery lifetimes. We present a novel multi-objective routing optimisation approach using evolutionary algorithms to approximate the optimal trade-off between minimum lifetime and the average lifetime of nodes in the network. In order to accomplish this combinatorial optimisation rapidly and thus permit dynamic optimisation for self-healing networks, our approach uses novel $k$-shortest paths based search space pruning in conjunction with a new edge metric, which associates the energy cost at a pair of nodes with the link between them. We demonstrate our solution on a real network, deployed in the Victoria \& Albert Museum, London. We show that this approach provides better trade-off solutions in comparison to the minimum energy option, and how a combination of solutions over the lifetime of the network can enhance the overall minimum lifetime.}, notes = {Also known as \cite{2598311} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Sato:2014:GECCOa, author = {Yuji Sato and Yusuke Oku and Masanori Fukuda}, title = {Applying GA with local search by taking hamming distances into consideration to credit erasure processing problems}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {1183--1190}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598229}, doi = {doi:10.1145/2576768.2598229}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Credit erasure processing refers to the process of cancelling corresponding items from a list of accounts receivable by checking against a detailed statement when receiving accounts receivable and other payments. In credit erasure processing, it is sometimes necessary to perform the laborious task of searching for credited items based solely on billing data and payments received. In this paper, we define credit erasure processing as a large-scale subset sum problem, and propose a solution based on a genetic algorithm (GA). In particular, we propose improving the search precision by incorporating a local search method that takes Hamming distances into consideration. To this end, we compare the standard GA with the result of adding a simple local search to the standard GA. This is done using a set of data where the numbers of digits in the billed quantities are roughly the same, another set of data where the numbers of digits are more varied, and a set of real data. As a result, we show that the proposed method works effectively when the numbers of digits in the billed quantities are roughly the same or when the fitness score is has a somewhat higher value.}, notes = {Also known as \cite{2598229} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Sholomon:2014:GECCO, author = {Dror Sholomon and Omid E. David and Nathan S. Netanyahu}, title = {Genetic algorithm-based solver for very large multiple jigsaw puzzles of unknown dimensions and piece orientation}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {1191--1198}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598289}, doi = {doi:10.1145/2576768.2598289}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper we propose the first genetic algorithm (GA)-based solver for jigsaw puzzles of unknown puzzle dimensions and unknown piece location and orientation. Our solver uses a novel crossover technique, and sets a new state-of-the-art in terms of the puzzle sizes solved and the accuracy obtained. The results are significantly improved, even when compared to previous solvers assuming known puzzle dimensions. Moreover, the solver successfully contends with a mixed bag of multiple puzzle pieces, assembling simultaneously all puzzles.}, notes = {Also known as \cite{2598289} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Shukla:2014:GECCO, author = {Pradyumn Kumar Shukla and Michael P. Cipold and Claus Bachmann and Hartmut Schmeck}, title = {On homogenization of coal in longitudinal blending beds}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {1199--1206}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598316}, doi = {doi:10.1145/2576768.2598316}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Coal blending processes mainly use static and non-reactive blending methods like the well-known Chevron stacking. Although real-time quality measurement techniques such as online X-ray fluorescence measurements are available, the possibility to explore a dynamic adaptation of the blending process to the current quality data obtained using these techniques has not been explored. A dynamic adaptation helps to mix the coal from different mines in an optimal way and deliver a homogeneous product. The paper formulates homogenisation of coal in longitudinal blending beds as a bi-objective problem of minimising the variance of the cross-sectional quality and minimising the height variance of the coal heap in the blending bed. We propose a cone based evolutionary algorithm to explore different trade-off regions of the Pareto front. A pronounced knee region on the Pareto front is found and is investigated in detail using a knee search algorithm. There are many interesting problem insights that are gained by examining the solutions found in different regions. In addition, all the knee solutions outperform the traditional Chevron stacking method.}, notes = {Also known as \cite{2598316} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Stolfi:2014:GECCO, author = {Daniel H. Stolfi and Enrique Alba}, title = {Eco-friendly reduction of travel times in european smart cities}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {1207--1214}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598317}, doi = {doi:10.1145/2576768.2598317}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This article proposes an innovative solution for reducing polluting gas emissions from road traffic in modern cities. It is based on our new Red Swarm architecture which is composed of a series of intelligent spots with WiFi connections that can suggest a customised route to drivers. We have tested our proposal in four different case studies corresponding to actual European smart cities. To this end, we first import the city information from OpenStreetMap into the SUMO road traffic micro-simulator, propose a Red Swarm architecture based on intelligent spots located at traffic lights, and then optimise the resulting system in terms of travel times and gas emissions by using an evolutionary algorithm. Our results show that an important quantitative reduction in gas emissions as well as in travel times can be achieved when vehicles are rerouted according to our Red Swarm indications. This represents a promising result for the low cost implementation of an idea that could engage the interest of both citizens and municipal authorities.}, notes = {Also known as \cite{2598317} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Yliniemi:2014:GECCO, author = {Logan Yliniemi and Adrian K. Agogino and Kagan Tumer}, title = {Evolutionary agent-based simulation of the introduction of new technologies in air traffic management}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {1215--1222}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598388}, doi = {doi:10.1145/2576768.2598388}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Accurate simulation of the effects of integrating new technologies into a complex system is critical to the modernisation of our antiquated air traffic system, where there exist many layers of interacting procedures, controls, and automation all designed to cooperate with human operators. Additions of even simple new technologies may result in unexpected emergent behaviour due to complex human/machine interactions. One approach is to create high-fidelity human models coming from the field of human factors that can simulate a rich set of behaviours. However, such models are difficult to produce, especially to show unexpected emergent behavior coming from many human operators interacting simultaneously within a complex system. Instead of engineering complex human models, we directly model the emergent behavior by evolving goal directed agents, representing human users. Using evolution we can predict how the agent representing the human user reacts given his/her goals. In this paradigm, each autonomous agent in a system pursues individual goals, and the behavior of the system emerges from the interactions, foreseen or unforeseen, between the agents/actors. We show that this method reflects the integration of new technologies in a historical case, and apply the same methodology for a possible future technology.}, notes = {Also known as \cite{2598388} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Zaefferer:2014:GECCOa, author = {Martin Zaefferer and Beate Breiderhoff and Boris Naujoks and Martina Friese and J\"{o}rg Stork and Andreas Fischbach and Oliver Flasch and Thomas Bartz-Beielstein}, title = {Tuning multi-objective optimization algorithms for cyclone dust separators}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {1223--1230}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598260}, doi = {doi:10.1145/2576768.2598260}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Cyclone separators are filtration devices frequently used in industry, e.g., to filter particles from flue gas. Optimising the cyclone geometry is a demanding task. Accurate simulations of cyclone separators are based on time consuming computational fluid dynamics simulations. Thus, the need for exploiting cheap information from analytical, approximative models is evident. Here, we employ two multi-objective optimisation algorithms on such cheap, models to analyze their optimisation performance on this problem. Under various limitations, we tune both algorithms with Sequential Parameter Optimisation (SPO) to achieve best possible results in shortest time. The resulting optimal settings are validated with different seeds, as well as with a different approximative model for collection efficiency. Their optimal performance is compared against a model based approach, where multi-objective SPO is directly employed to optimize the Cyclone model, rather than tuning the optimisation algorithms. It is shown that SPO finds improved parameter settings of the concerned algorithms and performs excellently when directly used as an optimiser.}, notes = {Also known as \cite{2598260} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Ali:2014:GECCO, author = {Shaukat Ali and Muhammad Zohaib Iqbal and Andrea Arcuri}, title = {Improved heuristics for solving OCL constraints using search algorithms}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {1231--1238}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598308}, doi = {doi:10.1145/2576768.2598308}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The Object Constraint Language (OCL) is a standard language for specifying constraints on Unified Modelling Language (UML) models. The specified constraints can be used for various purposes including verification, and model-based testing (e.g., test data generation). Efficiently solving OCL constraints is one of the key requirements for the practical use of OCL. In this paper, we propose an improvement in existing heuristics to solve OCL constraints using search algorithms. We evaluate our improved heuristics using two empirical studies with three search algorithms: Alternating Variable Method (AVM), (1+1) Evolutionary Algorithm (EA), and a Genetic Algorithm (GA). We also used Random Search (RS) as a comparison baseline. The first empirical study was conducted using carefully designed artificial problems (constraints) to assess each individual heuristics. The second empirical study is based on an industrial case study provided by Cisco about model-based testing of Video Conferencing Systems. The results of both empirical evaluations reveal that the effectiveness of the search algorithms, measured in terms of time to solve the OCL constraints to generate data, is significantly improved when using the novel heuristics presented in this paper. In particular, our experiments show that (1+1) EA with the novel heuristics has the highest success rate among all the analysed algorithms, as it requires the least number of iterations to solve constraints.}, notes = {Also known as \cite{2598308} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Efstathiou:2014:GECCO, author = {Dionysios Efstathiou and Peter McBurney and Steffen Zschaler and Johann Bourcier}, title = {Surrogate-assisted optimisation of composite applications in mobile ad hoc networks}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {1239--1246}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598307}, doi = {doi:10.1145/2576768.2598307}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Infrastructure-less mobile ad-hoc networks enable the development of collaborative pervasive applications. Within such dynamic networks, collaboration between devices can be realised through service-orientation by abstracting device resources as services. Recently, a framework for QoS-aware service composition has been introduced which takes into account a spectrum of orchestration patterns, and enables compositions of a better QoS than traditional centralised orchestration approaches. In this paper, we focus on the automated exploration of trade-off compositions within the search space defined by this flexible composition model. For the studied problem, the evaluation of the fitness functions guiding the search process is computationally expensive because it either involves a high-fidelity simulation or actually requires calling the composite service. To overcome this limitation, we have developed efficient surrogate models for estimating the QoS metrics of a candidate solution during the search. Our experimental results show that the use of surrogates can produce solutions with good convergence and diversity properties at a much lower computational effort.}, notes = {Also known as \cite{2598307} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Li:2014:GECCOb, author = {Lingbo Li and Mark Harman and Emmanuel Letier and Yuanyuan Zhang}, title = {Robust next release problem: handling uncertainty during optimization}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {1247--1254}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598334}, doi = {doi:10.1145/2576768.2598334}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Uncertainty is inevitable in real world requirement engineering. It has a significant impact on the feasibility of proposed solutions and thus brings risks to the software release plan. This paper proposes a multi-objective optimisation technique, augmented with Monte-Carlo Simulation, that optimises requirement choices for the three objectives of cost, revenue, and uncertainty. The paper reports the results of an empirical study over four data sets derived from a single real world data set. The results show that the robust optimal solutions obtained by our approach are conservative compared to their corresponding optimal solutions produced by traditional Multi-Objective Next Release Problem. We obtain a robustness improvement of at least 18percent at a small cost (a maximum 0.0285 shift in the 2D Pareto-front in the unit space). Surprisingly we found that, though a requirement's cost is correlated with inclusion on the Pareto-front, a requirement's expected revenue is not.}, notes = {Also known as \cite{2598334} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Lopez-Herrejon:2014:GECCO, author = {Roberto Erick Lopez-Herrejon and Javier Ferrer and Francisco Chicano and Evelyn Nicole Haslinger and Alexander Egyed and Enrique Alba}, title = {A parallel evolutionary algorithm for prioritized pairwise testing of software product lines}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {1255--1262}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598305}, doi = {doi:10.1145/2576768.2598305}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Software Product Lines (SPLs) are families of related software systems, which provide different feature combinations. Different SPL testing approaches have been proposed. However, despite the extensive and successful use of evolutionary computation techniques for software testing, their application to SPL testing remains largely unexplored. In this paper we present the Parallel Prioritised product line Genetic Solver (PPGS), a parallel genetic algorithm for the generation of prioritized pairwise testing suites for SPLs. We perform an extensive and comprehensive analysis of PPGS with 235 feature models from a wide range of number of features and products, using 3 different priority assignment schemes and 5 product prioritisation selection strategies. We also compare PPGS with the greedy algorithm prioritized-ICPL. Our study reveals that overall PPGS obtains smaller covering arrays with an acceptable performance difference with prioritized-ICPL.}, notes = {Also known as \cite{2598305} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Mkaouer:2014:GECCO, author = {Mohamed Wiem Mkaouer and Marouane Kessentini and Slim Bechikh and Kalyanmoy Deb and Mel {\'{O} Cinn\'{e}ide}}, title = {High dimensional search-based software engineering: finding tradeoffs among 15 objectives for automating software refactoring using NSGA-III}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {1263--1270}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598366}, doi = {doi:10.1145/2576768.2598366}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {There is a growing need for scalable search-based software engineering approaches that address software engineering problems where a large number of objectives are to be optimised. Software refactoring is one of these problems where a refactoring sequence is sought that optimises several software metrics. Most of the existing refactoring work uses a large set of quality metrics to evaluate the software design after applying refactoring operations, but current search-based software engineering approaches are limited to using a maximum of five metrics. We propose for the first time a scalable search-based software engineering approach based on a newly proposed evolutionary optimisation method NSGA-III where there are 15 different objectives to be optimized. In our approach, automated refactoring solutions are evaluated using a set of 15 distinct quality metrics. We evaluated this approach on seven large open source systems and found that, on average, more than 92percent of code smells were corrected. Statistical analysis of our experiments over 31 runs shows that NSGA-III performed significantly better than two other many-objective techniques (IBEA and MOEA/D), a multi-objective algorithm (NSGA-II) and two mono-objective approaches, hence demonstrating that our NSGA-III approach represents the new state of the art in fully-automated refactoring.}, notes = {Also known as \cite{2598366} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Omar:2014:GECCO, author = {Elmahdi Omar and Sudipto Ghosh and Darrell Whitley}, title = {Comparing search techniques for finding subtle higher order mutants}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {1271--1278}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598286}, doi = {doi:10.1145/2576768.2598286}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Subtle Higher Order Mutants (HOMs) are those HOMs that cannot be killed by existing test suites that kill all First Order Mutants (FOMs) for the program under test. Subtle HOMs simulate complex, real faults, whose behaviour cannot be simulated using FOMs. However, due to the coupling effect, subtle HOMs are rare in the exponentially large space of candidate HOMs and they can be costly to find even for small programs. In this paper we propose new search techniques for finding subtle HOMs and extend our prior work with new heuristics and search strategies. We compare the effectiveness of six search techniques applied to Java and AspectJ programs. Our study shows that more subtle HOMs were found when the new heuristics and search strategies were used. The programming language (Java or AspectJ) did not affect the effectiveness of any search technique.}, notes = {Also known as \cite{2598286} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Poulding:2014:GECCO, author = {Simon Poulding and Robert Feldt}, title = {Generating structured test data with specific properties using nested Monte-Carlo search}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {1279--1286}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598339}, doi = {doi:10.1145/2576768.2598339}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Software acting on complex data structures can be challenging to test: it is difficult to generate diverse test data that satisfies structural constraints while simultaneously exhibiting properties, such as a particular size, that the test engineer believes will be effective in detecting faults. In our previous work we introduced GodelTest, a framework for generating such data structures using non-deterministic programs, and combined it with Differential Evolution to optimise the generation process. Monte-Carlo Tree Search (MCTS) is a search technique that has shown great success in playing games that can be represented as a sequence of decisions. In this paper we apply Nested Monte-Carlo Search, a single-player variant of MCTS, to the sequence of decisions made by the generating programs used by GodelTest, and show that this combination can efficiently generate random data structures which exhibit the specific properties that the test engineer requires. We compare the results to Boltzmann sampling, an analytical approach to generating random combinatorial data structures.}, notes = {Also known as \cite{2598339} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Ramirez:2014:GECCO, author = {Aurora Ram\'{\i}rez and Jos\'{e} Ra\'{u}l Romero and Sebasti\'{a}n Ventura}, title = {On the performance of multiple objective evolutionary algorithms for software architecture discovery}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {1287--1294}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598310}, doi = {doi:10.1145/2576768.2598310}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {During the design of complex systems, software architects have to deal with a tangle of abstract artefacts, measures and ideas to discover the most fitting underlying architecture. A common way to structure these systems is in terms of their interacting software components, whose composition and connections need to be properly adjusted. Its abstract and highly combinatorial nature increases the complexity of the problem. In this scenario, Search-based Software Engineering (SBSE) may serve to support this decision making process from initial analysis models, since the discovery of component-based architectures can be formulated as a challenging multiple optimisation problem, where different metrics and configurations can be applied depending on the design requirements and its specific domain. Many-objective optimisation evolutionary algorithms can provide an interesting alternative to classical multi-objective approaches. This paper presents a comparative study of five different algorithms, including an empirical analysis of their behaviour in terms of quality and variety of the returned solutions. Results are also discussed considering those aspects of concern to the expert in the decision making process, like the number and type of architectures found. The analysis of many-objectives algorithms constitutes an important challenge, since some of them have never been explored before in SBSE.}, notes = {Also known as \cite{2598310} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Yue:2014:GECCO, author = {Tao Yue and Shaukat Ali}, title = {Applying search algorithms for optimizing stakeholders familiarity and balancing workload in requirements assignment}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {1295--1302}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598309}, doi = {doi:10.1145/2576768.2598309}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {During the early phase of project development lifecycle of large scale cyber-physical systems, a large number of requirements are needed to be assigned to different stakeholders from different organisations or different departments of the same organisation for reviewing, clarifying and checking their conformance to industry standards and government or other regulations. These requirements have different characteristics such as various extents of importance to the organization, complexity, and dependencies between each other, thereby requiring different effort (workload) to review and clarify. While working with our industrial partners in the domain of cyber-physical systems, we discovered an optimisation problem, where an optimal solution is required for assigning requirements to different stakeholders by maximising their familiarities to the assigned requirements while balancing the overall workload of each stakeholder. We propose a fitness function which was investigated with four search algorithms: (1+1) Evolutionary Algorithm (EA), Genetic Algorithm, and Alternating Variable Method, whereas Random Search is used as a comparison base line. We empirically evaluated their performance for finding an optimal solution using a large-scale industrial case study and 120 artificial problems with varying complexity. Results show that (1+1) EA gives the best results together with our proposed fitness function as compared to the other three algorithms.}, notes = {Also known as \cite{2598309} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Adriaensen:2014:GECCO, author = {Steven Adriaensen and Tim Brys and Ann Now\'{e}}, title = {Fair-share ILS: a simple state-of-the-art iterated local search hyperheuristic}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {1303--1310}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598285}, doi = {doi:10.1145/2576768.2598285}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this work we present a simple state-of-the-art selection hyperheuristic called Fair-Share Iterated Local Search (FS-ILS). FS-ILS is an iterated local search method using a conservative restart condition. Each iteration, a perturbation heuristic is selected proportionally to the acceptance rate of its previously proposed candidate solutions (after iterative improvement) by a domain-independent variant of the Metropolis condition. FS-ILS was developed in prior work using a semi-automated design approach. That work focused on how the method was found, rather than the method itself. As a result, it lacked a detailed explanation and analysis of the method, which will be the main contribution of this work. In our experiments we analyze FS-ILS's parameter sensitivity, accidental complexity and compare it to the contestants of the CHeSC (2011) competition.}, notes = {Also known as \cite{2598285} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Basgalupp:2014:GECCO, author = {M\'{a}rcio Porto Basgalupp and Rodrigo Coelho Barros and Tiago Barabasz}, title = {A grammatical evolution based hyper-heuristic for the automatic design of split criteria}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {1311--1318}, keywords = {genetic algorithms, genetic programming, grammatical evolution}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598327}, doi = {doi:10.1145/2576768.2598327}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Top-down induction of decision trees (TDIDT) is a powerful method for data classification. A major issue in TDIDT is the decision on which attribute should be selected for dividing the nodes in subsets, creating the tree. For performing such a task, decision trees make use of a split criterion, which is usually an information-theory based measure. Apparently, there is no free-lunch regarding decision-tree split criteria, as is the case of most things in machine learning. Each application may benefit from a distinct split criterion, and the problem we pose here is how to identify the suitable split criterion for each possible application that may emerge. We propose in this paper a grammatical evolution algorithm for automatically generating split criteria through a context-free grammar. We name our new approach ESC-GE (Evolutionary Split Criteria with Grammatical Evolution). It is empirically evaluated on public gene expression datasets, and we compare its performance with state-of-the-art split criteria, namely the information gain and gain ratio. Results show that ESC-GE outperforms the baseline criteria in the domain of gene expression data, indicating its effectiveness for automatically designing tailor-made split criteria.}, notes = {Also known as \cite{2598327} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Karafotias:2014:GECCO, author = {Giorgos Karafotias and Agoston Endre Eiben and Mark Hoogendoorn}, title = {Generic parameter control with reinforcement learning}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {1319--1326}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598360}, doi = {doi:10.1145/2576768.2598360}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Parameter control in Evolutionary Computing stands for an approach to parameter setting that changes the parameters of an Evolutionary Algorithm (EA) on-the-fly during the run. In this paper we address the issue of a generic and parameter-independent controller that can be readily plugged into an existing EA and offer performance improvements by varying the EA parameters during the problem solution process. Our approach is based on a careful study of Reinforcement Learning (RL) theory and the use of existing RL techniques. We present experiments using various state-of-the-art EAs solving different difficult problems. Results show that our RL control method has very good potential in improving the quality of the solution found without requiring additional resources or time and with minimal effort from the designer of the application.}, notes = {Also known as \cite{2598360} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{SoriaAlcaraz:2014:GECCO, author = {Jorge A. {Soria Alcaraz} and Gabriela Ochoa and Martin Carpio and Hector Puga}, title = {Evolvability metrics in adaptive operator selection}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {1327--1334}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598220}, doi = {doi:10.1145/2576768.2598220}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Evolvability metrics gauge the potential for fitness of an individual rather than fitness itself. They measure the local characteristics of the fitness landscape surrounding a solution. In adaptive operator selection the goal is to dynamically select from a given pool the operator to apply next during the search process. An important component of these adaptive schemes is credit assignment, whereby operators are rewarded according to their observed performance. This article brings the notion of evolvability to adaptive operator selection, by proposing an autonomous search algorithm that rewards operators according to their potential for fitness rather than their immediate fitness improvement. The approach is tested within an evolutionary algorithm framework featuring several mutation operators on binary strings. Three benchmark problems of increasing difficulty, Onemax, Royal Staircase and Multiple Knapsack are considered. Experiments reveal that evolvability metrics significantly improve the performance of adaptive operator selection, when compared against standard fitness improvement metrics.The main contribution is to effectively use fitness landscape metrics to guide a self-configuring algorithm.}, notes = {Also known as \cite{2598220} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Tanabe:2014:GECCO, author = {Ryoji Tanabe and Alex S. Fukunaga}, title = {On the pathological behavior of adaptive differential evolution on hybrid objective functions}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {1335--1342}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598322}, doi = {doi:10.1145/2576768.2598322}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Most state-of-the-art Differential Evolution (DE) algorithms are adaptive DEs with online parameter adaptation. We investigate the behaviour of adaptive DE on a class of hybrid functions, where independent groups of variables are associated with different component objective functions. An experimental evaluation of 3 state-of-the-art adaptive DEs (JADE, SHADE, jDE) shows that hybrid functions are 'adaptive-DE-hard'. That is, adaptive DEs have significant failure rates on these new functions. In-depth analysis of the adaptive behaviour of the DEs reveals that their parameter adaptation mechanisms behave in a pathological manner on this class of problems, resulting in over-adaptation for one of the components of the hybrids and poor overall performance. Thus, this class of deceptive benchmarks pose a significant challenge for DE.}, notes = {Also known as \cite{2598322} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Ugolotti:2014:GECCO, author = {Roberto Ugolotti and Stefano Cagnoni}, title = {Analysis of evolutionary algorithms using multi-objective parameter tuning}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {1343--1350}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598226}, doi = {doi:10.1145/2576768.2598226}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Evolutionary Algorithms (EAs) and other metaheuristics are greatly affected by the choice of their parameters, not only as regards the precision of the solutions found, but also for repeatability, robustness, speed of convergence, and other properties. Most of these performance criteria are often conflicting with one another. In our work, we see the problem of EAs' parameter selection and tuning as a multi-objective optimisation problem, in which the criteria to be optimised are precision and speed of convergence. We propose EMOPaT (Evolutionary Multi-Objective Parameter Tuning), a method that uses a well-known multi-objective optimization algorithm (NSGA-II) to find a front of non-dominated parameter sets which produce good results according to these two metrics. By doing so, we can provide three kinds of results: (i) a method that is able to adapt parameters to a single function, (ii) a comparison between Differential Evolution (DE) and Particle Swarm Optimisation (PSO) that takes into consideration both precision and speed, and (iii) an insight into how parameters of DE and PSO affect the performance of these EAs on different benchmark functions.}, notes = {Also known as \cite{2598226} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Zhang:2014:GECCOa, author = {Tiantian Zhang and Michael Georgiopoulos and Georgios C. Anagnostopoulos}, title = {Online model racing based on extreme performance}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {1351--1358}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598336}, doi = {doi:10.1145/2576768.2598336}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Racing algorithms are often used for offline model selection, where models are compared in terms of their average performance over a collection of problems. In this paper, we present a new racing algorithm variant, Max-Race, which makes decisions based on the maximum performance of models. It is an online algorithm, whose goal is to optimally allocate computational resources in a portfolio of evolutionary algorithms, while solving a particular problem instance. It employs a hypothesis test based on extreme value theory in order to decide, which component algorithms to retire, while avoiding unnecessary computations. Experimental results confirm that Max-Race is able to identify the best individual with high precision and low computational overhead. When used as a scheme to select the best from a portfolio of algorithms, the results compare favourably to the ones of other popular algorithm portfolio approaches.}, notes = {Also known as \cite{2598336} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Dang:2014:GECCO, author = {Duc-Cuong Dang and Per Kristian Lehre}, title = {Evolution under partial information}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {1359--1366}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598375}, doi = {doi:10.1145/2576768.2598375}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Complete and accurate information about the quality of candidate solutions is not always available in real-world optimisation. It is often prohibitively expensive to evaluate candidate solution on more than a few test cases, or the evaluation mechanism itself is unreliable. While evolutionary algorithms are popular methods in optimisation, the theoretical understanding is lacking for the case of partial information. This paper initiates runtime analysis of evolutionary algorithms where only partial information about fitness is available. Two scenarios are investigated. In partial evaluation of solutions, only a small amount of information about the problem is revealed in each fitness evaluation. We formulate a model that makes this scenario concrete for pseudo-Boolean optimisation. In partial evaluation of populations, only a few individuals in the population are evaluated, and the fitness values of the other individuals are missing or incorrect. For both scenarios, we prove that given a set of specific conditions, non-elitist evolutionary algorithms can optimise many functions in expected polynomial time even when vanishingly little information available. The conditions imply a small enough mutation rate and a large enough population size. The latter emphasises the importance of populations in evolution.}, notes = {Also known as \cite{2598375} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Dang:2014:GECCOa, author = {Duc-Cuong Dang and Per Kristian Lehre}, title = {Refined upper bounds on the expected runtime of non-elitist populations from fitness-levels}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {1367--1374}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598374}, doi = {doi:10.1145/2576768.2598374}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Recently, an easy-to-use fitness-level technique was introduced to prove upper bounds on the expected runtime of randomised search heuristics with non-elitist populations and unary variation operators. Following this work, we present a new and much more detailed analysis of the population dynamics, leading to a significantly improved fitness-level technique. In addition to improving the technique, the proof has been simplified. From the new fitness-level technique, the upper bound on the runtime in terms of generations can be improved from linear to logarithmic in the population size. Increasing the population size therefore has a smaller impact on the runtime than previously thought. To illustrate this improvement, we show that the current bounds on the runtime of EAs with non-elitist populations on many example functions can be significantly reduced. Furthermore, the new fitness-level technique makes the relationship between the selective pressure and the runtime of the algorithm explicit. Surprisingly, a very weak selective pressure is sufficient to optimise many functions in expected polynomial time. This observation has important consequences of which some are explored in a companion paper.}, notes = {Also known as \cite{2598374} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Doerr:2014:GECCOa, author = {Benjamin Doerr and Carola Doerr}, title = {The impact of random initialization on the runtime of randomized search heuristics}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {1375--1382}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598359}, doi = {doi:10.1145/2576768.2598359}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {It has often been observed that the expected runtime of an evolutionary algorithm with random initialisation does not deviate much from the expected runtime when starting in an initial solution of average fitness. Having this information a priori would greatly simplify the runtime analysis for the algorithm using random initialisation. We prove such a result for the optimisation of the OneMax test function via the two randomised search heuristics Randomized Local Search (RLS) and the (1+1) Evolutionary Algorithm. For both algorithms, we show that the expected runtime from a random initial solution deviates at most by a constant number of iterations from the expected runtime when starting with a solution having exactly n/2 ones. For RLS we can precisely compute that this constant is about -1/2. This leads to an extremely precise bound for the expected runtime. The expected number of fitness evaluations until an optimal search point is found, is about n Hn/2 - 1/2, where Hn/2 denotes the (n/2)th harmonic number when n is even, and Hn/2:= (Hn/2 + Hn/2+1)/2 when n is odd. The main technique to obtain these results is a coupling of the optimisation process starting from different fitness levels. We believe this technique to be interesting also much beyond the specific results mentioned above; e.g., for the study of other optimization problems.}, notes = {Also known as \cite{2598359} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Giessen:2014:GECCO, author = {Christian Gie\ssen and Timo K\"{o}tzing}, title = {Robustness of populations in stochastic environments}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {1383--1390}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598227}, doi = {doi:10.1145/2576768.2598227}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We consider stochastic versions of OneMax and LeadingOnes and analyse the performance of evolutionary algorithms with and without populations on these problems. It is known that the (1+1) EA on OneMax performs well in the presence of very small noise, but poorly for higher noise levels. We extend these results to LeadingOnes and to many different noise models, showing how the application of drift theory can significantly simplify and generalise previous analyses. Most surprisingly, even small populations (of size Theta(log n)) can make evolutionary algorithms perform well for high noise levels, well outside the abilities of the (1+1) EA! Larger population sizes are even more beneficial; we consider both parent and offspring populations. In this sense, populations are robust in these stochastic settings.}, notes = {Also known as \cite{2598227} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Kotzing:2014:GECCO, author = {Timo K\"{o}tzing}, title = {Concentration of first hitting times under additive drift}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {1391--1398}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598364}, doi = {doi:10.1145/2576768.2598364}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Recent advances in drift analysis have given us better and better tools for understanding random processes, including the run time of randomised search heuristics. In the setting of multiplicative drift we do not only have excellent bounds on the expected run time, but also more general results showing the concentration of the run time. In this paper we investigate the setting of additive drift under the assumption of strong concentration of the step size of the process. Under sufficiently strong drift towards the goal we show a strong concentration of the hitting time. In contrast to this, we show that in the presence of small drift a Gamblers-Ruin-like behaviour of the process overrides the influence of the drift. Finally, in the presence of sufficiently strong negative drift the hitting time is super-polynomial with high probability; this corresponds to the so-called negative drift theorem, for which we give new variants.}, notes = {Also known as \cite{2598364} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Lissovoi:2014:GECCO, author = {Andrei Lissovoi and Carsten Witt}, title = {MMAS vs. population-based EA on a family of dynamic fitness functions}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {1399--1406}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598301}, doi = {doi:10.1145/2576768.2598301}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We study the behaviour of a population-based EA and the Max-Min Ant System (MMAS) on a family of deterministically-changing fitness functions, where, in order to find the global optimum, the algorithms have to find specific local optima within each of a series of phases. In particular, we prove that a (2+1) EA with genotype diversity is able to find the global optimum of the Maze function, previously considered by Kotzing and Molter (PPSN 2012, 113--122), in polynomial time. This is then generalised to a hierarchy result stating that for every mu, a (mu+1) EA with genotype diversity is able to track a Maze function extended over a finite alphabet of mu symbols, whereas population size mu-1 is not sufficient. Furthermore, we show that MMAS does not require additional modifications to track the optimum of the finite-alphabet Maze functions, and, using a novel drift statement to simplify the analysis, reduce the required phase length of the Maze function.}, notes = {Also known as \cite{2598301} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Lockett:2014:GECCO, author = {Alan J. Lockett}, title = {Model-optimal optimization by solving bellman equations}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {1407--1414}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598306}, doi = {doi:10.1145/2576768.2598306}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper analytically identifies the points chosen by the best black-box optimisation methods, measured by assigning values to the history of points examined by stochastic optimisation methods applied to randomly chosen but static (single) objective functions. Optimisers that make these 'best' choices are called {\it model-optimal}. Model-optimal optimisers may not exist for a given model of static objectives but can always be approximated. If the search domain and the fitness range are both compact, or if other more abstract conditions are satisfied, it can be proved that model-optimal optimisers exist and at least one of them is deterministic. Model-optimality is studied by treating the average performance as a Bellman equation. The overall performance of an optimiser can be assessed based on the sequence of points selected for evaluation. Each choice introduces an error, and the overall performance is in every case just the sum of these errors. It might be possible to extend these results to certain stochastic or dynamic objectives, but they probably do not apply in adaptive or coevolutionary environments. The properties of model-optimality may make it possible to assess the tightness of existing bounds on average performance, and it may be possible to approximate model-optimal optimisation decisions directly.}, notes = {Also known as \cite{2598306} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Shukla:2014:GECCOa, author = {Pradyumn Kumar Shukla and Nadja Doll and Hartmut Schmeck}, title = {A theoretical analysis of volume based Pareto front approximations}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {1415--1422}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598348}, doi = {doi:10.1145/2576768.2598348}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Many multi-objective algorithms use volume based quality indicators to approximate the Pareto front. Amongst these, the hyper-volume is the most widely used. The distribution of solution sets of finite size mu that maximise the hypervolume have been investigated theoretically. But nearly all results are limited to the bi-objective case. In this paper, many of these results are extended to higher dimensions and a theoretical analysis and characterisation of optimal $\mu$-distributions is done. We investigate monotonic Pareto curves that are embedded in three and higher dimensions that keep the property of the bi-objective case that only few points are determining the hyper volume contribution of a point. For finite mu, we consider the influence of the choice of the reference point and determine sufficient conditions that assure the extreme points of the Pareto curves to be included in an optimal mu- distribution. We state conditions about the slope of the front that makes it impossible to include the extremes. Furthermore, we prove more specific results for three dimensional linear Pareto fronts. It is shown that the equispaced property of an optimal distribution for a line in two dimensions does not hold in higher dimensions. We additionally investigate hypervolume in general dimensions and problems with cone domination structures.}, notes = {Also known as \cite{2598348} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Song:2014:GECCO, author = {Bo Song and Victor O.K. Li}, title = {Gaussian mixture model of evolutionary algorithms}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {1423--1430}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598252}, doi = {doi:10.1145/2576768.2598252}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper proposes a novel finite Gaussian mixture model to study the population dynamics of evolutionary algorithms on continuous optimisation problems. While previous research taking on a dynamical system view has established the transition equation between the density functions of consecutive populations, the equation usually does not have closed-form solutions and can only be applied to very few optimisation problems. In this paper, we address this issue by approximating both the population density function of each generation and the objective function by finite Gaussian mixtures. We show that by making such approximations the transition equation can be solved exactly and key statistics, such as the expected mean and the variance of fitness values of the population, can be calculated easily. We also prove that by choosing appropriate values of the parameters, the $L^1$-norm error between our model and the actual population density function can be made arbitrarily small, up until a predefined generation. We present experimental results to show that our model is useful in simulating and examining the dynamics of evolutionary algorithms.}, notes = {Also known as \cite{2598252} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Sutton:2014:GECCO, author = {Andrew M. Sutton}, title = {Superpolynomial lower bounds for the (1+1) EA on some easy combinatorial problems}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {1431--1438}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598278}, doi = {doi:10.1145/2576768.2598278}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The (1+1) EA is a simple evolutionary algorithm that is known to be efficient on linear functions and on some combinatorial optimisation problems. In this paper, we rigorously study its behaviour on two easy combinatorial problems: finding the 2-colouring of a class of bipartite graphs, and constructing satisfying assignments for a class of satisfiable 2-CNF Boolean formulae. We prove that it is inefficient on both problems in the sense that the number of iterations the algorithm needs to minimise the cost functions is super-polynomial with high probability. Our motivation is to better understand the influence of problem instance structure on the runtime character of a simple evolutionary algorithm. We are interested in what kind of structural features give rise to so-called metastable states at which, with probability 1 - o(1), the (1+1) EA becomes trapped and subsequently has difficulty leaving. Finally, we show how to modify the (1+1) EA slightly in order to obtain a polynomial-time performance guarantee on both problems.}, notes = {Also known as \cite{2598278} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @inproceedings{Wei:2014:GECCO, author = {Kuai Wei and Michael J. Dinneen}, title = {Runtime analysis to compare best-improvement and first-improvement in memetic algorithms}, booktitle = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = {2014}, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, isbn13 = {978-1-4503-2662-9}, pages = {1439--1446}, keywords = {}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2576768.2598386}, doi = {doi:10.1145/2576768.2598386}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In recent years, the advantage afforded by using multiple local searches in a Memetic Algorithm (MA) to solve one problem (a single fitness function), has been verified in many successful experiments. These experiments also give the observation that the local search operator that gives the best results in an MA on the same fitness function for solving a NP-hard problem is instance specific. This paper will provide a theoretical evidence for this observation. In this paper, we will formalise the (1+1) Restart Memetic Algorithms applying two different local searches, the first-improvement and the best-improvement, respectively. We will then run them on a single fitness function to solve the Clique Problem. We then show that there are two families of graphs such that, for the first family of graphs, MAs with one local search drastically outperform MAs with the other local search, and vice versa for the second family of graphs. Our study explains why using multiple local searches can outperform using a single local search in Memetic Algorithms.}, notes = {Also known as \cite{2598386} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, } @proceedings(Igel:2014:GECCO, title = {GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation}, year = 2014, editor = {Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges}, address = {Vancouver, BC, Canada}, publisher_address = {New York, NY, USA}, month = {12-16 July}, organisation = {SIGEVO}, keywords = {genetic algorithms, genetic programming, ant colony optimization and swarm intelligence, artificial immune systems, artificial life, robotics, and evolvable hardware, biological and biomedical applications, digital entertainment technologies and arts, estimation of distribution algorithms, evolution strategies and evolutionary programming, evolutionary combinatorial optimization and metaheuristics, evolutionary machine learning, evolutionary multiobjective optimization, generative and developmental systems, integrative genetic and evolutionary computation, parallel evolutionary systems, real world applications, search based software engineering, self-* search, theory}, ISBN13 = {978-1-4503-2662-9}, url = {http://dl.acm.org/citation.cfm?id=2001576}, notes = {GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)}, )