%processed by gecco2014_toc.awk $Revision: 1.47 $ ARGC=5 Fri Aug 15 11:17:12 BST 2014 %1 gecco2014comp_toc.txt %2 gecco2014comp_editors.txt %3 gecco2014comp.bib %4 gecco2014comp.bib %WBL 1 Aug 2017 ensure passes bibclean v3.02 @inproceedings{Bengio:2014:GECCOcomp, author = {Yoshua Bengio}, title = {Deep learning and cultural evolution}, booktitle = {GECCO 2014 Keynotes and invited talk}, year = {2014}, editor = {Dirk Arnold}, isbn13 = {978-1-4503-2881-4}, note = {Invited talk}, pages = {1--2}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598395}, doi = {doi:10.1145/2598394.2598395}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We propose a theory and its first experimental tests, relating difficulty of learning in deep architectures to culture and language. The theory is articulated around the following hypotheses: learning in an individual human brain is hampered by the presence of effective local minima, particularly when it comes to learning higher-level abstractions, which are represented by the composition of many levels of representation, i.e., by deep architectures; a human brain can learn such high-level abstractions if guided by the signals produced by other humans, which act as hints for intermediate and higher-level abstractions; language and the recombination and optimisation of mental concepts provide an efficient evolutionary recombination operator for this purpose. The theory is grounded in experimental observations of the difficulties of training deep artificial neural networks and an empirical test of the hypothesis regarding the need for guidance of intermediate concepts is demonstrated. This is done through a learning task on which all the tested machine learning algorithms failed, unless provided with hints about intermediate-level abstractions.}, notes = {Also known as \cite{2598395} Distributed at GECCO-2014.}, } @inproceedings{Floreano:2014:GECCOcomp, author = {Dario Floreano}, title = {Bridging natural and artificial evolution}, booktitle = {GECCO 2014 Keynotes and invited talk}, year = {2014}, editor = {Dirk Arnold}, isbn13 = {978-1-4503-2881-4}, note = {Invited talk}, pages = {3--4}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598396}, doi = {doi:10.1145/2598394.2598396}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this talk I will show how artificial evolution can be used to address biological questions and explain phenomena for which there is no fossil record or no experimental evidence, such evolution of behaviour, altruism, and communication. I will give examples related to insects and plants. Central to this endeavour is how selection mechanisms are applied and interpreted. I will also show how selection pressure can be lifted in artificial evolution and lead to open-ended evolution in dynamic and changing environments.}, notes = {Also known as \cite{2598396} Distributed at GECCO-2014.}, } @inproceedings{Gulwani:2014:GECCOcomp, author = {Sumit Gulwani}, title = {Applications of program synthesis to end-user programming and intelligent tutoring systems}, booktitle = {GECCO 2014 Keynotes and invited talk}, year = {2014}, editor = {Dirk Arnold}, isbn13 = {978-1-4503-2881-4}, note = {Invited talk}, pages = {5--6}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598397}, doi = {doi:10.1145/2598394.2598397}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Computing devices have become widely available to billions of end users, yet a handful of experts have the needed expertise to program these devices. Automated program synthesis has the potential to revolutionise this landscape, when targeted for the right set of problems and when allowing the right interaction model. The first part of this talk discusses techniques for programming using examples and natural language. These techniques have been applied to various end-user programming domains including data manipulation and smartphone scripting. The second part of this talk presents surprising applications of program synthesis technology to automating various repetitive tasks in Education including problem, solution, and feedback generation for various subject domains such as math and programming. These results advance the state-of-the-art in intelligent tutoring, and can play a significant role in enabling personalised and interactive education in both standard classrooms and MOOCs.}, notes = {Also known as \cite{2598397} Distributed at GECCO-2014.}, } @inproceedings{Jia:2014:GECCOcomp, author = {Ya-Hui Jia and Wei-Neng Chen and Xiao-Min Hu}, title = {A PSO approach for software project planning}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {ant colony optimization and swarm intelligence: Poster}, pages = {7--8}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598422}, doi = {doi:10.1145/2598394.2598422}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Search-based software project management is a hot research point in software engineering. Based on the event-based scheduler (EBS) we have proposed in previous work [1], this paper intends to further propose a two-phase particle swarm optimisation approach which uses a set-based representation for task scheduling and an integer representation for workload assignment scheduling to improve planning performance. Experimental results on 83 instances demonstrate the effectiveness of the proposed approach.}, notes = {Also known as \cite{2598422} Distributed at GECCO-2014.}, } @inproceedings{Langosz:2014:GECCOcomp, author = {Malte Langosz and Kai Alexander {von Szadkowski} and Frank Kirchner}, title = {Introducing particle swarm optimization into a genetic algorithm to evolve robot controllers}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {genetic algorithms, genetic programming, ant colony optimization and swarm intelligence: Poster}, pages = {9--10}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598474}, doi = {doi:10.1145/2598394.2598474}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper presents Swarm-Assisted Behaviour Graph Evolution (SABRE), a genetic algorithm which combines elements from genetic programming and neuroevolution to develop Behaviour Graphs (BGs). SABRE evolves graph structure and parameters in parallel, with particle swarm optimisation (PSO) being used for the latter. The algorithm's performance was evaluated on a set of black-box function approximation problems, one of which represents part of a robot controller. We found that SABRE performed significantly better in approximating the mathematically complex test functions than the reference algorithms genetic programming (GP) and NEAT.}, notes = {Also known as \cite{2598474} Distributed at GECCO-2014.}, } @inproceedings{Liu:2014:GECCOcomp, author = {Xing Liu and Lin Shang}, title = {Fitness proportionate selection based binary particle swarm optimization}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {ant colony optimization and swarm intelligence: Poster}, pages = {11--12}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598417}, doi = {doi:10.1145/2598394.2598417}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Particle Swarm Optimisation(PSO) has shown its advantages not only in dealing with continuous optimisation problems, but also in dealing with discrete optimization problems. Binary Particle Swarm Optimisation(BPSO), the discrete version of PSO, has been widely applied to many areas. Although there are some variations aiming to improve BPSO's performance, none of them has been proved to be a promising alternative. In this paper, we propose a novel binary particle swarm optimization called Fitness Proportionate Selection Based Binary Particle Swarm Optimization(FPSBPSO). We test FPSBPSO's performance in function optimization problems and multidimension knapsack problems. Experimental results show that FPSBPSO can find better optima than BPSO and a variation of BPSO.}, notes = {Also known as \cite{2598417} Distributed at GECCO-2014.}, } @inproceedings{Monson:2014:GECCOcomp, author = {Christopher K. Monson and Kevin D. Seppi}, title = {Under-informed momentum in PSO}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {ant colony optimization and swarm intelligence: Poster}, pages = {13--14}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598490}, doi = {doi:10.1145/2598394.2598490}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Particle Swarm Optimization is fundamentally a stochastic algorithm, where each particle takes into account noisy information from its own history as well as that of its neighbourhood. Though basic information-theoretic principles would suggest that less noise indicates greater certainty, the momentum term is simultaneously the least directly-informed and the most deterministically applied. This dichotomy suggests that the typically confident treatment of momentum is misplaced, and that swarm performance can benefit from better-motivated processes that obviate momentum entirely.}, notes = {Also known as \cite{2598490} Distributed at GECCO-2014.}, } @inproceedings{Peng:2014:GECCOcomp, author = {Meng-Qi Peng and Yue-Jiao Gong and Jing-Jing Li and Ying-Biao Lin}, title = {Multi-swarm particle swarm optimization with multiple learning strategies}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {ant colony optimization and swarm intelligence: Poster}, pages = {15--16}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598418}, doi = {doi:10.1145/2598394.2598418}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Inspired by the division of labour and migration behaviour in nature, this paper proposes a novel particle swarm optimisation algorithm with multiple learning strategies (PSO-MLS). In the algorithm, particles are divided into three sub-swarms randomly while three learning strategies with different motivations are applied to each sub-swarm respectively. The Traditional Learning Strategy (TLS) inherits the basic operations of PSO to guarantee the stability. Then a Periodically Stochastic Learning Strategy (PSLS) employs a random learning vector to increase the diversity so as to enhance the global search ability. A Random Mutation Learning Strategy (RMLS) adopts mutation to enable particles to jump out of local optima when trapped. Besides, information migration is applied within the intercommunication of sub-swarms. After a certain number of generations, sub-swarms would aggregate to continue search, aiming at global convergence. Through these learning strategies and swarm aggregation, PSO-MLS possesses both good exploration and exploitation abilities. PSO-MLS was tested on a set of benchmarks and the result shows its superiority to gain higher accuracy for unimodal functions and better solution quality for multimodal functions when compared to some PSO variants.}, notes = {Also known as \cite{2598418} Distributed at GECCO-2014.}, } @inproceedings{Sioud:2014:GECCOcomp, author = {Aymen Sioud and Caroline Gagn\'{e} and Marc Gravel}, title = {An ant colony optimization for solving a hybrid flexible flowshop}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {ant colony optimization and swarm intelligence: Poster}, pages = {17--18}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598402}, doi = {doi:10.1145/2598394.2598402}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper, we propose an ant colony optimisation (ACO) to solve a realistic variant of flowshop problem. The considered scheduling problem is a hybrid flexible flowshop problem with sequence-dependent setup times under the objective of minimising the makespan. The proposed approach uses concept from multi-objective evolutionary algorithms and look-ahead information to enhance solutions quality. We also introduce new constructive heuristic used in the ACO local improvement. Numerical experiments were performed to compare the performance of the ACO on different benchmarks from the literature. The results indicate that the ACO is very competitive and enhances solutions of the known reference sets.}, notes = {Also known as \cite{2598402} Distributed at GECCO-2014.}, } @inproceedings{Tan:2014:GECCOcomp, author = {Qiuhang Tan and Hejun Wu and Biao Hu and Xingcheng Liu}, title = {An improved artificial bee colony algorithm for clustering}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {ant colony optimization and swarm intelligence: Poster}, pages = {19--20}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598464}, doi = {doi:10.1145/2598394.2598464}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Artificial Bee Colony (ABC) algorithm, which was initially proposed for numerical function optimisation, has been increasingly used for clustering. However, when it is directly applied to clustering, the performance of ABC is lower than expected. This paper proposes an improved ABC algorithm for clustering, denoted as EABC. EABC uses a key initialisation method to accommodate the special solution space of clustering. Experimental results show that the evaluation of clustering is significantly improved and the latency of clustering is sharply reduced. Furthermore, EABC outperforms two ABC variants in clustering benchmark data sets.}, notes = {Also known as \cite{2598464} Distributed at GECCO-2014.}, } @inproceedings{Capodieci:2014:GECCOcomp, author = {Nicola Capodieci and Emma Hart and Giacomo Cabri}, title = {Artificial immune systems in the context of autonomic computing: integrating design paradigms}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {artificial immune systems: Poster}, pages = {21--22}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598502}, doi = {doi:10.1145/2598394.2598502}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We describe a design paradigm for developing autonomic computing systems based on an analogy with the natural immune system, and show how current approaches to designing autonomic systems could be enriched by considering alternative design processes based on cognitive immune networks.}, notes = {Also known as \cite{2598502} Distributed at GECCO-2014.}, } @inproceedings{Deng:2014:GECCOcomp, author = {Yiqi Deng and Peter J. Bentley}, title = {Adapting to dynamically changing noise during learning of heart sounds: an AIS-based approach using systemic computation}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {artificial immune systems: Poster}, pages = {23--24}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598461}, doi = {doi:10.1145/2598394.2598461}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Real world machine learning, where data is sampled continuously, may in theory be classifiable into distinct and unchanging categories but in practice the classification becomes non-trivial because the nature of the background noise continuously changes. Applying distinct and unchanging categories for data ignores the fact that for some applications where the categories of data may remain constant, the background noise constantly changes, and thus the ability for a supervised learning method to work is limited. In this work, we propose a novel method based on an Artificial Immune System (AIS) and implemented on a systemic computer, which is designed to adapt itself over continuous arrival of data to cope with changing patterns of noise without requirement for feedback, as a result of its own experience.}, notes = {Also known as \cite{2598461} Distributed at GECCO-2014.}, } @inproceedings{Agwang:2014:GECCOcomp, author = {Faith Agwang and Will {van Heerden} and Geoff Nitschke}, title = {Lifetimes of migration}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {artificial life, robotics, and evolvable hardware: Poster}, pages = {25--26}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598450}, doi = {doi:10.1145/2598394.2598450}, publisher = {ACM}, publisher_address = {New York, NY, USA}, notes = {Also known as \cite{2598450} Distributed at GECCO-2014.}, } @inproceedings{Blair:2014:GECCOcomp, author = {Alan Blair}, title = {Incremental evolution of HERCL programs for robust control}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {genetic algorithms, genetic programming, artificial life, robotics, and evolvable hardware: Poster}, pages = {27--28}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598424}, doi = {doi:10.1145/2598394.2598424}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We explore the evolution of programs for control tasks using the recently introduced Hierarchical Evolutionary Re-Combination Language (HERCL) which has been designed as an austere and general-purpose language, with a view toward modular evolutionary computation, combining elements from Linear GP with stack-based operations from FORTH. We show that HERCL programs can be evolved to robustly perform a benchmark double pole balancing task from a specified range of initial conditions, with the poles remaining balanced for up to an hour of simulated time.}, notes = {Also known as \cite{2598424} Distributed at GECCO-2014.}, } @inproceedings{Delecluse:2014:GECCOcomp, author = {Martin Delecluse and St\'{e}phane Sanchez and Sylvain Cussat-Blanc and Nicolas Schneider and Jean-Baptiste Welcomme}, title = {High-level behavior regulation for multi-robot systems}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {artificial life, robotics, and evolvable hardware: Poster}, pages = {29--30}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598454}, doi = {doi:10.1145/2598394.2598454}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We propose a new collaborative guidance platform for a team of robots that should protect a fixed ground target from one or several threats. The team of robots performs high-level behaviours. These are hand-coded since they consist in driving the robots to some given position. However, deciding when and how to use these behaviours is much more challenging. Scripting high-level interception strategies is a complex problem and applicable to few specific application contexts. We propose to use a gene regulatory network to regulate high-level behaviours and to enable the emergence of efficient and robust interception strategies.}, notes = {Also known as \cite{2598454} Distributed at GECCO-2014.}, } @inproceedings{Hamann:2014:GECCOcomp, author = {Heiko Hamann}, title = {Evolving prediction machines: collective behaviors based on minimal surprisal}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {artificial life, robotics, and evolvable hardware: Poster}, pages = {31--32}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598507}, doi = {doi:10.1145/2598394.2598507}, publisher = {ACM}, publisher_address = {New York, NY, USA}, notes = {Also known as \cite{2598507} Distributed at GECCO-2014.}, } @inproceedings{Krawec:2014:GECCOcomp, author = {Walter O. Krawec}, title = {Minimal variable quantum decision makers for robotic control}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {artificial life, robotics, and evolvable hardware: Poster}, pages = {33--34}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598409}, doi = {doi:10.1145/2598394.2598409}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this report we describe our research involving the construction of quantum-based robotic controllers. By careful use of quantum interference as a computational resource and by using only a linear number of elementary unitary transformations, we are able to construct systems which seem to provide a computational advantage even when simulated on a classical computer.}, notes = {Also known as \cite{2598409} Distributed at GECCO-2014.}, } @inproceedings{Rajagopalan:2014:GECCOcomp, author = {Padmini Rajagopalan and Aditya Rawal and Kay E. Holekamp and Risto Miikkulainen}, title = {General intelligence through prolonged evolution of densely connected neural networks}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {artificial life, robotics, and evolvable hardware: Poster}, pages = {35--36}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598434}, doi = {doi:10.1145/2598394.2598434}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Different species of animals have vast differences in how general their learning abilities and behaviours are. This paper analyses the effect of network connection density and prolonged evolution on general intelligence. Using the NEAT algorithm for neuroevolution, network structures with different connectivities were evaluated in recognising digits and their mirror images. These experiments show that general intelligence, i.e. recognition of previously unseen examples, increases with increase in connectivity. General intelligence also increases with the number of generations in prolonged evolution, even when performance no longer improves in the known examples. This outcome suggests that general intelligence depends on specific anatomical and environmental factors.}, notes = {Also known as \cite{2598434} Distributed at GECCO-2014.}, } @inproceedings{Ahmed:2014:GECCOcomp, author = {Soha Ahmed and Mengjie Zhang and Lifeng Peng}, title = {Prediction of detectable peptides in MS data using genetic programming}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {genetic algorithms, genetic programming, biological and biomedical applications: Poster}, pages = {37--38}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598421}, doi = {doi:10.1145/2598394.2598421}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The use of mass spectrometry to verify and quantify biomarkers requires the identification of the peptides that can be detectable. In this paper, we propose the use of genetic programming (GP) to measure the detection probability of the peptides. The new GP method is tested and verified on two different yeast data sets with increasing complexity and shows improved performance over other state-of-art classification and feature selection algorithms.}, notes = {Also known as \cite{2598421} Distributed at GECCO-2014.}, } @inproceedings{Bevilacqua:2014:GECCOcomp, author = {Vitoantonio Bevilacqua and Paolo Pannarale}, title = {A semantic expert system for the evolutionary design of synthetic gene networks}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {biological and biomedical applications: Poster}, pages = {39--40}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598504}, doi = {doi:10.1145/2598394.2598504}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This work tries to cope with the standardisation issue by the adoption of model exchange standards like CellML, BioBrick standard biological parts and standard signal carriers for modelling purpose. The BioBricks are easily assembled [1] standard DNA sequences coding for well-defined structures and functions and represent an effort to introduce the engineering principles of abstraction and standardisation in synthetic biology. Web applications as GenoCAD [2] are available and implements an algorithm of syntax check of the circuits designed [3], while some other tools for automatic design and optimisation of genetic circuits have appeared [4] and are also specific for BioBrick systems [5]. Our generated models are made of Standard Virtual Parts modular components. Model complexity includes more interaction dynamics than previous works. The inherent software complexity has been handled by a rational use of ontologies and rule engine. The database of parts and interactions is automatically created from publicly available whole system models. We implemented a genetic algorithm searching the space of possible genetic circuits for an optimal circuit meeting user defined input-output dynamics. The tools performing structural optimisation usually use stochastic strategies, while those optimising the parameters or matching the components for a given structure can take advantage of both stochastic and deterministic strategies. In most cases it is however necessary human intervention, for example to set the value of certain kinetic parameters. To our best knowledge no tool exists which does not show a couple of these limitations, then our tool is the only capable of using a library of parts, dynamically generated from other system models available from public databases [6]. The tool automatically infers the chemical and genetic interactions occurring between entities of the repository models and applies them in the target model if opportune. The repository models have to be modelled by a specific CellML standard, the Standard Virtual Parts (SVP) [7] formalism and the components have to be annotated with OWL for unique identifiers. The output is a sequence of readily composable biological components, deposited in the registry of parts, and a complete CellML kinetic model of the system. Accordingly, a model can be generated and simulated from a sequence of BioBrick, without any human intervention. Actual tools present a moderated degree of accuracy in the prediction of the behaviour, principally due to the lack of consideration of many cellular factors. Despite the advances in molecular construction, modelling and fine-tuning the behaviour of synthetic circuits remains extremely challenging [8]. We tried to cope with this issue of scalability by means of ontologies coupled with a rule engine [9]. Model complexity includes more interaction dynamics than previous works, including gene regulation, interaction between small molecules and proteins but also protein-protein and post-transcriptional regulation. The domain was described by using Ontology Web Language (OWL) ontologies in conjunction with CellML [10], while complex logic was added by Jess rules [11]. The system has been successfully tested on a single test case and looks towards the creation of a web platform [12].}, notes = {Also known as \cite{2598504} Distributed at GECCO-2014.}, } @inproceedings{Garcia-Bernardo:2014:GECCOcomp, author = {Javier Garcia-Bernardo and Margaret J. Eppstein}, title = {Evolving small GRNs with a top-down approach}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {biological and biomedical applications: Poster}, pages = {41--42}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598443}, doi = {doi:10.1145/2598394.2598443}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Designing genetic regulatory networks (GRNs) to achieve a desired cellular function is one of the main goals of synthetic biology. However, determining minimal GRNs that produce desired time-series behaviours is non-trivial. In this paper, we propose a 'top-down' approach, wherein we start with relatively dense GRNs and then use differential evolution (DE) to evolve interaction coefficients. When the target dynamical behaviour is found embedded in a dense GRN, we narrow the focus of the search and begin aggressively pruning out excess interactions at the end of each generation. We first show that the method can quickly rediscover known small GRNs for a toggle switch and an oscillatory circuit. Next we include these GRNs as non-evolvable subnetworks in the subsequent evolution of more complex, modular GRNs. By incorporating aggressive pruning and a penalty term, the DE was able to find minimal or nearly minimal GRNs in all test problems.}, notes = {Also known as \cite{2598443} Distributed at GECCO-2014.}, } @inproceedings{Li:2014:GECCOcomp, author = {Hongjian Li and Kwong-Sak Leung and Chun Ho Chan and Hei Lun Cheung and Man-Hon Wong}, title = {iSYN: de novo drug design with click chemistry support}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {biological and biomedical applications: Poster}, pages = {43--44}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598398}, doi = {doi:10.1145/2598394.2598398}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We present iSyn, an evolutionary algorithm that automatically designs de novo ligands with high predicted binding affinity and drug-like properties. It attempts to optimise candidate ligands in accordance with click chemistry and thus ensures chemical synthesisability. In addition to the existing genetic operators of mutation and crossover inherited from AutoGrow 3.0, our iSyn introduces four novel genetic operators to 'cut' ligands in order to prevent them from becoming too large in molecular size, hence preserving drug-like properties. Moreover, iSyn interfaces with our fast docking engine idock, greatly reducing the execution time. We hope iSyn can supplement medicinal chemists' efforts. iSyn was applied to optimising candidate ligands against two important drug targets, TbREL1 and HIV-1 RT, and managed to produce chemically valid ligands with high predicted binding affinities and drug-like properties. In the example of TbREL1, the predicted free energy of the best generated ligand decreased from -9.878 kcal/mol to -13.985 kcal/mol after 3 generations. In the example of HIV-1 RT, the predicted free energy of the best generated ligand decreased from -5.427 kcal/mol to -12.488 kcal/mol after 2 generations, meanwhile the molecular mass dropped from 602.818 Da to 461.736 Da, so that the compound could be properly absorbed by human body. iSyn is written in C++ and Python, and is free and open source, available at http://istar.cse.cuhk.edu.hk/iSyn.tgz. It has been tested successfully on Linux and Windows. In the near future we plan to implement a web-based user interface to facilitate its usage and to promote large-scale de novo drug design.}, notes = {Also known as \cite{2598398} Distributed at GECCO-2014.}, } @inproceedings{Ferreira:2014:GECCOcomp, author = {Lucas Ferreira and Leonardo Pereira and Claudio Toledo}, title = {A multi-population genetic algorithm for procedural generation of levels for platform games}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {digital entertainment technologies and arts: Poster}, pages = {45--46}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598489}, doi = {doi:10.1145/2598394.2598489}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper presents a multi-population genetic algorithm for procedural generation of levels for platform games such as Super Mario Bros (SMB). The algorithm evolves four aspects of the game during its generations: terrain, enemies, coins and blocks. Each aspect has its own codification, population and fitness function. At the end of the evolution, the best four aspects are combined to construct the level. The method has as input a vector of parameters to configure the characteristics of each aspect. Experiments were made to evaluate the capability of the method in generating interesting levels. Results showed the method can be controlled to generate different types of levels.}, notes = {Also known as \cite{2598489} Distributed at GECCO-2014.}, } @inproceedings{Ferreira:2014:GECCOcompa, author = {Lucas Ferreira and Leonardo Pereira and Claudio Toledo and Rodrigo Pereira}, title = {Evolutionary approaches to evolve AI scripts for a RTS game}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {digital entertainment technologies and arts: Poster}, pages = {47--48}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598486}, doi = {doi:10.1145/2598394.2598486}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper evaluates three different Evolutionary Algorithms (EAs) for generating non-player characters (NPCs) via Artificial Intelligence scripts for a Real Time Strategy (RTS) game. The first approach executes only a Genetic Algorithm (GA), while the second and third ones combine GA with a Dynamic Scripting approach. The Bos Wars game is used for testing these EAs, which are able to create and to evolve strategies of this RTS game coded as scripts in LUA language. In order to do this, the EA communicates with Bos Wars' engine by sending scripts, playing matches and capturing statistical data to evaluate its individuals.}, notes = {Also known as \cite{2598486} Distributed at GECCO-2014.}, } @inproceedings{Garcia-Ortega:2014:GECCOcomp, author = {Rub\'{e}n H\'{e}ctor Garc\'{\i}a-Ortega and Pablo Garc\'{\i}a-S\'{a}nchez and Antonio Miguel Mora and Juan Juli\'{a}n Merelo}, title = {A methodology for designing emergent literary backstories on non-player characters using genetic algorithms}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {digital entertainment technologies and arts: Poster}, pages = {49--50}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598482}, doi = {doi:10.1145/2598394.2598482}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The creation of fictional stories is a very complex task that usually implies a creative process where the author has to combine characters, conflicts and backstories to create an engaging narrative. This work presents a general methodology that uses individual based models to generate cohesive and coherent backstories where desired archetypes (universally accepted literary symbols) emerge and their life stories are a by-product of the simulation. This methodology includes the modelling and parametrisation of the agents, the environment where they will live and the desired literary setting. The use of a genetic algorithm (GA) is proposed to establish the parameter configuration that will lead to backstories that best fit the setting. Information extracted from a simulation can then be used to create the backstories. To demonstrate the adequacy of the methodology, we perform an implementation using a specific multi-agent system and evaluate the results.}, notes = {Also known as \cite{2598482} Distributed at GECCO-2014.}, } @inproceedings{Sanselone:2014:GECCOcomp, author = {Maxime Sanselone and St\'{e}phane Sanchez and C\'{e}dric Sanza and David Panzoli and Yves Duthen}, title = {Control of non player characters in a medical learning game with Monte Carlo tree search}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {digital entertainment technologies and arts: Poster}, pages = {51--52}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598473}, doi = {doi:10.1145/2598394.2598473}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper, we apply the Monte Carlo Tree Search (MCTS) method for controlling at once several virtual characters in a 3D multi-player learning game. The MCTS is used as a search algorithm to explore a search space where every potential solution reflects a specific state of the game environment. Functions representing the interaction abilities of each character are provided to the algorithm to leap from one state to another. We show that the MCTS algorithm successfully manages to plan the actions for several virtual characters in a synchronised fashion, from the initial state to one or more desirable end states. Besides, we demonstrate the ability of this algorithm to fulfil two specific requirements of a learning game AI : guiding the non player characters to follow a predefined plan while coping with the unpredictability of the human players actions.}, notes = {Also known as \cite{2598473} Distributed at GECCO-2014.}, } @inproceedings{Chuang:2014:GECCOcomp, author = {Chung-Yao Chuang and Stephen F. Smith}, title = {Estimation of distribution algorithms based on n-gramstatistics for sequencing and optimization}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {estimation of distribution algorithms: Poster}, pages = {53--54}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598399}, doi = {doi:10.1145/2598394.2598399}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper presents our work on Estimation of Distribution Algorithms (EDAs) that address sequencing problems, i.e., the task of finding the best ordering of a set of items or an optimal schedule to perform a given set of operations. Specifically, we focus on using probabilistic models based on $n$-gram statistics. These models have been used extensively in modelling the statistical properties of sequences. We start with an EDA that uses a bi-gram model, then extend this scheme to higher-order models. However, directly replacing the 2gram model with a higher-order model results in premature convergence. We give an explanation on this situation, along with some empirical support. We then introduce a technique for combining multiple models of different orders, which allows for smooth transition from lower-order models to higher-order ones. Furthermore, this technique can also be used to incorporate other heuristics as well as prior knowledge about the problem into the search process. Promising preliminary results on solving Travelling Salesman Problems (TSPs) are presented.}, notes = {Also known as \cite{2598399} Distributed at GECCO-2014.}, } @inproceedings{Sharifi:2014:GECCOcomp, author = {Hadi Sharifi and Amin Nikanjam and Hossein Karshenas and Negar Najimi}, title = {Complexity of model learning in EDAs: multi-structure problems}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {estimation of distribution algorithms: Poster}, pages = {55--56}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598479}, doi = {doi:10.1145/2598394.2598479}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Many of the real-world problems can be decomposed into a number of sub-problems for which the solutions can be found easier. However, proper decomposition of large problems remains a challenging issue, especially in optimisation, where we need to find the optimal solutions more efficiently. Estimation of distribution algorithms (EDAs) are a class of evolutionary optimization algorithms that try to capture the interactions between problem variables when learning a probabilistic model from the population of candidate solutions. In this paper, we propose a type of synthesised problems, specially designed to challenge this specific ability of EDAs. They are based on the principal idea that each candidate solution to a problem may be simultaneously interpreted by two or more different structures where only one is true, resulting in the best solution to that problem. Of course, some of these structures may be more likely according to the statistics collected from the population of candidate solutions, but may not necessarily lead to the best solution. The experimental results show that the proposed benchmarks are indeed difficult for EDAs even when they use expressive models such as Bayesian networks to capture the interactions in the problem.}, notes = {Also known as \cite{2598479} Distributed at GECCO-2014.}, } @inproceedings{Wang:2014:GECCOcomp, author = {Bo Wang and Hua Xu and Yuan Yuan}, title = {A two-level hierarchical EDA using conjugate priori}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {estimation of distribution algorithms: Poster}, pages = {57--58}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598470}, doi = {doi:10.1145/2598394.2598470}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Estimation of distribution algorithms (EDAs) are stochastic optimisation methods that guide the search by building and sampling probabilistic models. Inspired by Bayesian inference, we proposed a two-level hierarchical model based on beta distribution. Beta distribution is the conjugate priori for binomial distribution. Besides, we introduced a learning rate function into the framework to control the model update. To demonstrate the effectiveness and applicability of our proposed algorithm, experiments are carried out on the 01-knapsack problems. Experimental results show that the proposed algorithm outperforms cGA, PBIL and QEA.}, notes = {Also known as \cite{2598470} Distributed at GECCO-2014.}, } @inproceedings{Krawec:2014:GECCOcompa, author = {Walter O. Krawec}, title = {An algorithm for evolving multiple quantum operators for arbitrary quantum computational problems}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {evolution strategies and evolutionary programming: Poster}, pages = {59--60}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598408}, doi = {doi:10.1145/2598394.2598408}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We design and analyse a real-coded genetic algorithm for the use in evolving collections of quantum unitary operators (not circuits) which act on pure or mixed states over arbitrary quantum systems while interacting with fixed, problem specific operators (e.g., oracle calls) and intermediate partial measurements. Our algorithm is general enough so as to allow its application to multiple, very different, areas of quantum computation research.}, notes = {Also known as \cite{2598408} Distributed at GECCO-2014.}, } @inproceedings{Liu:2014:GECCOcompa, author = {Jialin Liu and David L. St-Pierre and Olivier Teytaud}, title = {A mathematically derived number of resamplings for noisy optimization}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {evolution strategies and evolutionary programming: Poster}, pages = {61--62}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598458}, doi = {doi:10.1145/2598394.2598458}, publisher = {ACM}, publisher_address = {New York, NY, USA}, notes = {Also known as \cite{2598458} Distributed at GECCO-2014.}, } @inproceedings{SerranoRubio:2014:GECCOcomp, author = {Juan Pablo {Serrano Rubio} and Arturo {Hern\'{a}ndez Aguirre} and Rafael {Herrera Guzm\'{a}n}}, title = {SEA: an evolutionary algorithm based on spherical inversions}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {evolution strategies and evolutionary programming: Poster}, pages = {63--64}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598492}, doi = {doi:10.1145/2598394.2598492}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper introduces the Spherical Evolutionary Algorithm (SEA) for global continuous optimisation. Two new geometric search operators are included in the design of the SEA. The operators are named: Inversion Search Operator (ISO) and Reflection Search Operator (RSO). This paper describes the general implementation of SEA and its performance is analysed through a benchmark of 10 functions.}, notes = {Also known as \cite{2598492} Distributed at GECCO-2014.}, } @inproceedings{Cheng:2014:GECCOcomp, author = {Peng Cheng and Jeng-Shyang Pan}, title = {Use EMO to protect sensitive knowledge in association rule mining by adding items}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {evolutionary combinatorial optimization and metaheuristics: Poster}, pages = {65--66}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598465}, doi = {doi:10.1145/2598394.2598465}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {When data is released or shared among different organisations, some sensitive or confidential information may be subject to be exposed by using data mining tools. Thus, a question arises: how can we protect sensitive knowledge while allowing other parties to extract the knowledge behind the shared data. In this paper, we address the problem of privacy preserving in association rule mining from the perspective of multi-objective optimisation. A sensitive rule can be hidden by adding items into the dataset to make the support of the antecedent part of the sensitive rule increase and accordingly the confidence of the sensitive rule decrease. The evolutionary multi-objective optimization (EMO) algorithm is used to find suitable transactions (or tuples) to be modified so as the side effects to be minimised. Experiments on real datasets demonstrated the effectiveness of the proposed method.}, notes = {Also known as \cite{2598465} Distributed at GECCO-2014.}, } @inproceedings{Chivilikhin:2014:GECCOcomp, author = {Daniil Chivilikhin and Vladimir Ulyantsev}, title = {Inferring automata-based programs from specification with mutation-based ant colony optimization}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {evolutionary combinatorial optimization and metaheuristics: Poster}, pages = {67--68}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598446}, doi = {doi:10.1145/2598394.2598446}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper we address the problem of constructing correct-by-design programs with the use of the automata-based programming paradigm. A recent algorithm for learning finite-state machines (FSMs) MuACOsm is applied to the problem of inferring extended finite-state machine (EFSM) models from behaviour examples (test scenarios) and temporal properties, and is shown to outperform the genetic algorithm (GA) used earlier.}, notes = {Also known as \cite{2598446} Distributed at GECCO-2014.}, } @inproceedings{Maravilha:2014:GECCOcomp, author = {Andr\'{e} L. Maravilha and Jaime A. Ram\'{\i}rez and Felipe Campelo}, title = {Combinatorial optimization with differential evolution: a set-based approach}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {evolutionary combinatorial optimization and metaheuristics: Poster}, pages = {69--70}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598463}, doi = {doi:10.1145/2598394.2598463}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This work presents a differential evolution algorithm for combinatorial optimisation, in which a set-based representation and operators define subproblems that are used to explore the search space. The proposed method is tested on the capacitated centred clustering problem.}, notes = {Also known as \cite{2598463} Distributed at GECCO-2014.}, } @inproceedings{Marshall:2014:GECCOcomp, author = {Richard J. Marshall and Mark Johnston and Mengjie Zhang}, title = {Hyper-heuristics, grammatical evolution and the capacitated vehicle routing problem}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {genetic algorithms, genetic programming, grammatical evolution, evolutionary combinatorial optimization and metaheuristics: Poster}, pages = {71--72}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598407}, doi = {doi:10.1145/2598394.2598407}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {A common problem when applying heuristics is that they often perform well on some problem instances, but poorly on others. We develop a hyper-heuristic approach, using Grammatical Evolution (GE), to generate heuristics for the Vehicle Routing Problem (VRP). Through a series of experiments we develop an approach that leads to solutions of acceptable quality to Vehicle Routing Problem instances with only limited prior knowledge of the problem to be solved.}, notes = {Also known as \cite{2598407} Distributed at GECCO-2014.}, } @inproceedings{Martins:2014:GECCOcomp, author = {Jean P. Martins and Humberto Longo and Alexandre C.B. Delbem}, title = {On the effectiveness of genetic algorithms for the multidimensional knapsack problem}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {evolutionary combinatorial optimization and metaheuristics: Poster}, pages = {73--74}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598477}, doi = {doi:10.1145/2598394.2598477}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In the Multidimensional Knapsack Problem (MKP) there are items easily identifiable as highly (lowly) profitable and likely to be chosen (not chosen) to compose high-quality solutions. For all the other items, the Knapsack Core~(KC), the decision is harder. By focusing the search on the KC effective algorithms have been developed. However, the true KC is not available and most algorithms can only rely on items' efficiencies. Chu & Beasley Genetic Algorithm (CBGA), for example, uses efficiencies in a repair-operator which bias the search towards the KC. This paper shows that, as the search progresses, efficiencies lose their descriptive power and, consequently, CBGA's effectiveness decreases. As a result, CBGA rapidly finds its best solutions and stagnates. In order to circumvent this stagnation, extra information about the KC should be used to implement specific operators. Since there is a correlation between marginal probabilities in a population and efficiencies, we show that KCs can be estimated from the population during the search. By solving the estimated KCs with CPLEX, improvements were possible in many instances, evidencing CBGA's weakness to solve KCs and indicating a promising way to improve GAs for the MKP through the use of KC estimates.}, notes = {Also known as \cite{2598477} Distributed at GECCO-2014.}, } @inproceedings{Amelio:2014:GECCOcomp, author = {Alessia Amelio and Clara Pizzuti}, title = {Uncovering communities in multidimensional networks with multiobjective genetic algorithms}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {evolutionary machine learning: Poster}, pages = {75--76}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598453}, doi = {doi:10.1145/2598394.2598453}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {A framework for community discovery in multidimensional networks based on an evolutionary approach is proposed. Each network is clustered by running a multiobjective genetic algorithm that tries to maximise the modularity function of the current network and, at the same time, to minimise the difference between the current community structure and that obtained on the already considered dimensions. Experiments on synthetic datasets show the capability of the approach in discovering latent shared group organisation of individuals.}, notes = {Also known as \cite{2598453} Distributed at GECCO-2014.}, } @inproceedings{Luckehe:2014:GECCOcomp, author = {Daniel L\"{u}ckehe and Oliver Kramer}, title = {A variable kernel function for hybrid unsupervised kernel regression}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {evolutionary machine learning: Poster}, pages = {77--78}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598459}, doi = {doi:10.1145/2598394.2598459}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Dimensionality reduction is an important problem class in machine learning and data mining, as the dimensionality of data sets is steadily increasing. This work is a contribution in the line of research on iterative unsupervised kernel regression (UKR), a class of methods for dimensionality reduction that employ regression methods to find low-dimensional representations of high-dimensional patterns. We introduce a hybrid optimisation approach of iteratively constructing a solution and performing gradient descent in the data space reconstruction error (DSRE). Further, we introduce a variable kernel function that increases the flexibility of UKR learning. The variable kernel function increases the model capacity, but introduces new parameters that have to be tuned.}, notes = {Also known as \cite{2598459} Distributed at GECCO-2014.}, } @inproceedings{MoradiKordmahalleh:2014:GECCOcomp, author = {Mina {Moradi Kordmahalleh} and Mohammad {Gorji Sefidmazgi} and Abdollah Homaifar and Dukka B. KC and Anthony Guiseppi-Elie}, title = {Time-series forecasting with evolvable partially connected artificial neural network}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {evolutionary machine learning: Poster}, pages = {79--80}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598435}, doi = {doi:10.1145/2598394.2598435}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In nonlinear and chaotic time series prediction, constructing the mathematical model of the system dynamics is not an easy task. Partially connected Artificial Neural Network with Evolvable Topology (PANNET) is a new paradigm for prediction of chaotic time series without access to the dynamics and essential memory depth of the system. Evolvable topology of the PANNET provides flexibility in recognition of systems in contrast to fixed layered topology of the traditional ANNs. This evolvable topology guides the relationship between observation nodes and hidden nodes, where hidden nodes are extra nodes that play the role of memory or internal states of the system. In the proposed variable-length Genetic Algorithm (GA), internal neurons can be connected arbitrarily to any type of nodes. Besides, number of neurons, inputs and outputs for each neuron, origin and weight of each connection evolve in order to find the best configuration of the network.}, notes = {Also known as \cite{2598435} Distributed at GECCO-2014.}, } @inproceedings{Vargas:2014:GECCOcomp, author = {Danilo Vasconcellos Vargas and Hirotaka Takano and Junichi Murata}, title = {Novelty-organizing classifiers applied to classification and reinforcement learning: towards flexible algorithms}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {evolutionary machine learning: Poster}, pages = {81--82}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598429}, doi = {doi:10.1145/2598394.2598429}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {It is widely known that reinforcement learning is a more general problem than supervised learning. In fact, supervised learning can be seen as a class of reinforcement learning problems. However, only a couple of papers tested reinforcement learning algorithms in supervised learning problems. Here we propose a new and simpler way to abstract supervised learning for any reinforcement learning algorithm. Moreover, a new algorithm called Novelty-Organising Classifiers is developed based on a Novelty Map population that focuses more on the novelty of the inputs than their frequency. A comparison of the proposed method with Self-Organising Classifiers and BioHel on some datasets is presented. Even though BioHel is specialised in solving supervised learning problems, the results showed only a trade-off between the algorithms. Lastly, results on a maze problem validate the flexibility of the proposed algorithm beyond supervised learning problems. Thus, Novelty-Organizing Classifiers is capable of solving many supervised learning problems as well as a maze problem without changing any parameter at all. Considering the fact that no adaptation of parameters was executed, the proposed algorithm's basis seems interestingly flexible.}, notes = {Also known as \cite{2598429} Distributed at GECCO-2014.}, } @inproceedings{Xue:2014:GECCOcomp, author = {Bing Xue and Wenlong Fu and Mengjie Zhang}, title = {Differential evolution (DE) for multi-objective feature selection in classification}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {evolutionary machine learning: Poster}, pages = {83--84}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598493}, doi = {doi:10.1145/2598394.2598493}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Feature selection has two main conflicting objectives, which are to minimise the number of features and maximise the classification accuracy. Evolutionary computation techniques are particularly suitable for solving mult-objective tasks. Based on differential evolution (DE), this paper develops a multi-objective feature selection algorithm (DEMOFS). DEMOFS is examined and compared with two traditional feature selection algorithms and a DE based single objective feature selection algorithm. DEFS aims to minimise the classification error rate of the selected features. Experiments on nine benchmark datasets show that DEMOFS can successfully obtain a set of non-dominated feature subsets, which include a smaller number of features and maintain or improve the classification performance over using all features. In almost all cases, DEMOFS outperforms DEFS and the two traditional feature selection methods in terms of both the number of features and the classification performance.}, notes = {Also known as \cite{2598493} Distributed at GECCO-2014.}, } @inproceedings{Chaabani:2014:GECCOcomp, author = {Abir Chaabani and Slim Bechikh and Lamjed {Ben Said}}, title = {An indicator-based chemical reaction optimization algorithm for multi-objective search}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {evolutionary multiobjective optimization: Poster}, pages = {85--86}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598468}, doi = {doi:10.1145/2598394.2598468}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper, we propose an Indicator-based Chemical Reaction Optimisation (ICRO) algorithm for multiobjective optimisation. There are two main motivations behind this work. On the one hand, CRO is a new recently proposed metaheuristic which demonstrated very good performance in solving several mono-objective problems. On the other hand, the idea of performing selection in Multi-Objective Evolutionary Algorithms (MOEAs) based on the optimisation of a quality metric has shown a big promise in tackling Multi-Objective Problems (MOPs). The statistical analysis of the obtained results shows that ICRO provides competitive and better results than several other MOEAs.}, notes = {Also known as \cite{2598468} Distributed at GECCO-2014.}, } @inproceedings{Guo:2014:GECCOcomp, author = {Wentao Guo and Xinjie Yu}, title = {Non-dominated sorting differential evolution with improved directional convergence and spread for multiobjective optimization}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {evolutionary multiobjective optimization: Poster}, pages = {87--88}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598457}, doi = {doi:10.1145/2598394.2598457}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {A non-dominated sorting differential evolution algorithm with improved directional convergence and spread (NSDE-IDCS) is developed. Taking advantage of differential evolution, searching direction for a dominated solution is determined by its nearest non-dominated neighbour, while searching direction for a non-dominated solution is determined by other two non-dominated solutions. A simplex local search operator with an adaptive search probability is embedded to further exploit the neighbourhood of non-dominated solutions.}, notes = {Also known as \cite{2598457} Distributed at GECCO-2014.}, } @inproceedings{Ishibuchi:2014:GECCOcomp, author = {Hisao Ishibuchi and Hiroyuki Masuda and Yusuke Nojima}, title = {Meta-level multi-objective formulations of set optimization for multi-objective optimization problems: multi-reference point approach to hypervolume maximization}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {evolutionary multiobjective optimization: Poster}, pages = {89--90}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598484}, doi = {doi:10.1145/2598394.2598484}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Hypervolume has been frequently used as an indicator to evaluate a solution set in indicator-based evolutionary algorithms (IBEAs). One important issue in such an IBEA is the choice of a reference point. A different solution set is often obtained from a different reference point since the hypervolume calculation depends on the location of the reference point. In this paper, we propose an idea of using this dependency to formulate a meta-level multi-objective set optimisation problem. Hypervolume maximisation for a different reference point is used as a different objective.}, notes = {Also known as \cite{2598484} Distributed at GECCO-2014.}, } @inproceedings{Narukawa:2014:GECCOcomp, author = {Kaname Narukawa and Yuki Tanigaki and Hisao Ishibuchi}, title = {Evolutionary many-objective optimization using preference on hyperplane}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {evolutionary multiobjective optimization: Poster}, pages = {91--92}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598420}, doi = {doi:10.1145/2598394.2598420}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper proposes to represent the preference of a decision maker by Gaussian functions on a hyperplane. The preference is used to evaluate non-dominated solutions as a second criterion instead of the crowding distance in NSGA-II. High performance of our proposal is demonstrated for many-objective DTLZ problems.}, notes = {Also known as \cite{2598420} Distributed at GECCO-2014.}, } @inproceedings{Qi:2014:GECCOcomp, author = {Yutao Qi and Xiaoliang Ma and Minglei Yin and Fang Liu and JingXuan Wei}, title = {MOEA/D with a delaunay triangulation based weight adjustment}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {evolutionary multiobjective optimization: Poster}, pages = {93--94}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598416}, doi = {doi:10.1145/2598394.2598416}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {MOEA/D decomposes a multi-objective optimisation problem (MOP) into a set of scalar sub-problems with evenly spread weight vectors. Recent studies have shown that the fixed weight vectors used in MOEA/D might not be able to cover the whole Pareto front (PF) very well. Due to this, we developed an adaptive weight adjustment method in our previous work by removing subproblems from the crowded parts of the PF and adding new ones into the sparse parts. Although it performs well, we found that the sparse measurement of a subproblem which is determined by the m-nearest (m is the dimensional of the object space) neighbours of its solution can be more appropriately defined. In this work, the neighbourhood relationship between subproblems is defined by using Delaunay triangulation (DT) of the points in the population.}, notes = {Also known as \cite{2598416} Distributed at GECCO-2014.}, } @inproceedings{Shukla:2014:GECCOcomp, author = {Pradyumn Kumar Shukla and Marlon A. Braun and Hartmut Schmeck}, title = {On the interrelationships between knees and aggregate objective functions}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {evolutionary multiobjective optimization: Poster}, pages = {95--96}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598483}, doi = {doi:10.1145/2598394.2598483}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Optimising several objectives that are often at odds with each other provides difficult challenges that are not encountered if having only one goal at hand. One intuitive way to solve a multi-objective problem is to aggregate the objectives and reformulate it as an optimisation problem having just a single goal. This goal can be a designer specific aggregation of the objectives or a characterisation of knees, trade-offs, utilities, stronger optimality concepts or preferences. This paper examines the theoretical relationships between two knee concepts and aggregate objective functions methods. The changes in the fitness landscape by using different aggregations is also discussed.}, notes = {Also known as \cite{2598483} Distributed at GECCO-2014.}, } @inproceedings{Zhong:2014:GECCOcomp, author = {Fugui Zhong and Bo Yuan and Bin Li}, title = {Hybridization of NSGA-II with greedy re-assignment for variation tolerant logic mapping on nano-scale crossbar architectures}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {evolutionary multiobjective optimization: Poster}, pages = {97--98}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598430}, doi = {doi:10.1145/2598394.2598430}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {There exit high variations among nano-devices in nano-electronic systems, owing to the extremely small size and the bottom-up self-assembly nanofabrication process. Therefore, it is important to develop logical function mapping techniques with the consideration of variation tolerance. In this paper, the variation tolerant logical mapping (VTLM) problem is treated as a multi-objective optimisation problem (MOP), a hybridisation of Non-dominated Sorting Genetic Algorithm II (NSGA-II) with a problem-specific local search is presented to solve the problem. The experiment results show that with the assistance of the problem-specific local search, the presented algorithm is effective, and can find better solutions than that without the local search.}, notes = {Also known as \cite{2598430} Distributed at GECCO-2014.}, } @inproceedings{Cole:2014:GECCOcomp, author = {Ben Cole and Michael Muthukrishna}, title = {Nu-life: spontaneous dynamic hierarchical organization in a non-uniform "life-like" cellular automata}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {generative and developmental systems: Poster}, pages = {99--100}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598444}, doi = {doi:10.1145/2598394.2598444}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We present a novel 2D cellular automaton with rules that are a non-uniform generalisation of a Moore-neighbourhood, outer-totalistic, two-state (life-like) cellular automaton. The system is purely deterministic and exhibits interesting multi-scale emergent behaviour, including the spontaneous formation of mobile particles and other self-organising structures. In particular, smaller-scale structures can be shown to combine with other structures to form inhomogeneous higher-order constructions, and to do so at multiple orders of magnitude. The system has features in common with reaction-diffusion models. We propose that this system has properties that make it useful as a model of an artificial chemistry with the potential for supporting open-ended evolutionary growth. We call it Nu-life.}, notes = {Also known as \cite{2598444} Distributed at GECCO-2014.}, } @inproceedings{Disset:2014:GECCOcomp, author = {Jean Disset and Sylvain Cussat-Blanc and Yves Duthen}, title = {Toward organogenesis of artificial creatures}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {generative and developmental systems: Poster}, pages = {101--102}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598441}, doi = {doi:10.1145/2598394.2598441}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper presents a new model for the development of artificial creatures from a single cell. The model aims at providing a more biologically plausible abstraction of the morphogenesis and the specialisation process, which the organogenesis follows. It is built upon three main elements: a cellular physics simulation, a simplified cell cycle using an evolved artificial gene regulatory network and a cell specialization mechanism quantifying the ability to perform different functions. As a proof-of-concept, we present a first experiment where the morphology of a multicellular organism is guided by cell weaknesses and efficiency at performing different functions under environmental stress.}, notes = {Also known as \cite{2598441} Distributed at GECCO-2014.}, } @inproceedings{Verbancsics:2014:GECCOcomp, author = {Phillip Verbancsics and Joshua Harguess}, title = {Deep learning through generative and developmental system}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {generative and developmental systems: Poster}, pages = {103--104}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598401}, doi = {doi:10.1145/2598394.2598401}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Deep learning through supervised and unsupervised learning has demonstrated human competitive performance on some visual tasks; however, evolution played an important role in the development of biological visual systems. Thus evolutionary approaches, specifically the Hypercube-based NeuroEvolution of Augmenting Topologies, are applied to deep learning tasks in this paper. Results indicate HyperNEAT alone struggles in image classification, but trains effective feature extractors for other machine learning approaches.}, notes = {Also known as \cite{2598401} Distributed at GECCO-2014.}, } @inproceedings{Cagara:2014:GECCOcomp, author = {Daniel Cagara and Ana L.C. Bazzan and Bj\"{o}rn Scheuermann}, title = {Getting you faster to work: a genetic algorithm approach to the traffic assignment problem}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {genetic algorithms: Poster}, pages = {105--106}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598419}, doi = {doi:10.1145/2598394.2598419}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Traffic assignment is a complex optimisation problem. In case the road network has many links (thus a high number of alternative routes) and multiple origin-destination pairs, most existing solutions approximate the so-called user equilibrium (a variant of Nash equilibrium). Furthermore, the quality of these solutions (mostly, iterative algorithms) come at the expense of computational performance. In this study, we introduce a methodology to evaluate an approximation of an optimal traffic assignment from the global network's perspective based on genetic algorithms. This approach has been investigated in terms of both network performance (travel time) and convergence speed.}, notes = {Also known as \cite{2598419} Distributed at GECCO-2014.}, } @inproceedings{Hasegawa:2014:GECCOcomp, author = {Taku Hasegawa and Kaname Matsumura and Kaiki Tsuchie and Naoki Mori and Keinosuke Matsumoto}, title = {Novel virtual fitness evaluation framework for fitness landscape learning evolutionary computation}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {genetic algorithms: Poster}, pages = {107--108}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598496}, doi = {doi:10.1145/2598394.2598496}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Introducing the machine learning technique into evolutionary computation (EC) is one of the most important issues to expand EC design. In this paper, we proposed a novel method that combines the genetic algorithm and support vector machine to achieve the imaginary evolution without real fitness evaluations.}, notes = {Also known as \cite{2598496} Distributed at GECCO-2014.}, } @inproceedings{Higgins:2014:GECCOcomp, author = {Conor Higgins and Conor Ryan and Aine Kearns and Mikael Fernstrom}, title = {The creation and facilitation of speech and language therapy sessions for individuals with aphasia}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {genetic algorithms: Poster}, pages = {109--110}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598485}, doi = {doi:10.1145/2598394.2598485}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Aphasia is the degradation of one's ability to comprehend or convey language, usually due to brain damage caused by strokes or some external force. Sufferers can regain some or all of their prior abilities, but only with significant speech and language therapy (SLT) sessions. SLT sessions are resource-intensive, as they often require skilled therapists to adapt the therapy for the individual patients. We present Ogma, a novel approach to the automatic creation of SLT sessions. Ogma is comprised of a proprietary mobile front-end application that the patients interact with, and an offline GA that designs patient-specific sessions based on a patient's progress. Key to this is the ability to accurately capture the difficulty of the generated sessions; this paper presents the results of experiments where SLT practitioners perform beta testing on Ogma, to ascertain its ability to consistently produce useful sessions of appropriate difficulty.}, notes = {Also known as \cite{2598485} Distributed at GECCO-2014.}, } @inproceedings{Huang:2014:GECCOcomp, author = {Xiao-Ma Huang and Yue-Jiao Gong and Jing-Jing Li and Xiao-Min Hu}, title = {A novel genetic algorithm based on partitioning for large-scale network design problems}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {genetic algorithms: Poster}, pages = {111--112}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598431}, doi = {doi:10.1145/2598394.2598431}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Network design is a broad class of essential engineering and science problems. The target of network design is to construct a graph that satisfies some restrictions. Many network design problems (NDPs) are known as NP-hard and become more challenging as networks grow fast in size. In this paper, we propose a novel genetic algorithm based on partitioning, termed PGA, which divides large-scale NDPs into low dimensional sub-problems and achieves global optimal solution by coordination of sub-problems. Experiments with PGA applied to the degree-constrained minimum spanning tree problem have shown the effectiveness of PGA for large-scale NDPs.}, notes = {Also known as \cite{2598431} Distributed at GECCO-2014.}, } @inproceedings{Inoue:2014:GECCOcomp, author = {Kazuyuki Inoue and Naoki Mori and Keinosuke Matsumoto}, title = {A novel genetic algorithm based on the life cycle of dictyostelium}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {genetic algorithms: Poster}, pages = {113--114}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598469}, doi = {doi:10.1145/2598394.2598469}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We have proposed the novel evolutionary computation called 'Dictyostelium based Genetic Algorithm' (DGA), which adopts the concept of life cycle of slime molds and can take a balance between exploitation and exploration. In this research, we propose an extension of DGA with an index evaluating population diversity. To analyse the abilities of DGAs, the computational experiments are carried out taking several combinatorial optimisation problems as examples. We show that the performance of DGAs is superior to that of Simple GA in all examples.}, notes = {Also known as \cite{2598469} Distributed at GECCO-2014.}, } @inproceedings{Kuber:2014:GECCOcomp, author = {Karthik Kuber and Stuart W. Card and Kishan G. Mehrotra and Chilukuri K. Mohan}, title = {Ancestral networks in evolutionary algorithms}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {genetic algorithms: Poster}, pages = {115--116}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598481}, doi = {doi:10.1145/2598394.2598481}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The behaviours of populations in evolutionary algorithms can be understood in terms of the dynamics of network models whose nodes represent individuals in the population. This paper explores 'ancestral networks' in which connections indicate the proximity of the nearest common ancestor of two nodes. Preliminary experimental results show that the formation of large components in such an ancestral network model can be used to identify potential convergence, and to determine when randomly reseeding part of a population can prove beneficial.}, notes = {Also known as \cite{2598481} Distributed at GECCO-2014.}, } @inproceedings{Lam:2014:GECCOcomp, author = {Ho Tat Lam and Kwok Yip Szeto}, title = {Search for the most reliable network of fixed connectivity using genetic algorithm}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {genetic algorithms: Poster}, pages = {117--118}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598412}, doi = {doi:10.1145/2598394.2598412}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Reliability is one of the important measures of how well a system meets its design objective, and mathematically is the probability that a system will perform satisfactorily for a given period of time. When the system is described by a network of N components (nodes) and L connections (links), the reliability of the system becomes a network design problem that is an NP-hard combinatorial optimisation problem. In this paper, genetic algorithm is applied to find the most reliable connected network with the same connectivity, (i.e. with given N and L). The accuracy and efficiency of genetic algorithm in the search of the most reliable network(s) of same connectivity is verified by exhaustive search. Our results not only demonstrate the efficiency of our algorithm for optimization problem for graphs, but also suggest that the most reliable network will have high symmetry.}, notes = {Also known as \cite{2598412} Distributed at GECCO-2014.}, } @inproceedings{Merelo:2014:GECCOcomp, author = {Juan Juli\'{a}n Merelo and Pedro Castillo and Antonio Mora and Anna I. Esparcia-Alc\'{a}zar and V\'{\i}ctor M. {Rivas Santos}}, title = {Assessing different architectures for evolutionary algorithms in javascript}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {genetic algorithms: Poster}, pages = {119--120}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598460}, doi = {doi:10.1145/2598394.2598460}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {JavaScript (JS) is nowadays the only language that can be used to develop web-based client-server applications in both tiers, client and server. This makes it an interesting choice for developing distributed evolutionary computation experiments, but the best way from algorithmic and practical point of views is not clear, so we will compare different distributed EC architectures in JavaScript using NodEO, an open source JS framework released by us.}, notes = {Also known as \cite{2598460} Distributed at GECCO-2014.}, } @inproceedings{Orkisz:2014:GECCOcomp, author = {Janusz Orkisz and Maciej Glowacki}, title = {On dedicated evolutionary algorithms for large non-linear constrained optimization problems}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {genetic algorithms: Poster}, pages = {121--122}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598436}, doi = {doi:10.1145/2598394.2598436}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper considers advances in development of dedicated Evolutionary Algorithms (EA) for efficiently solving large, non-linear, constrained optimisation problems. The EA are precisely understood here as decimal-coded Genetic Algorithms consisting of three operators: selection, crossover and mutation, followed by several newly developed calculation speed-up techniques based on simple concepts. These techniques include: solution smoothing and balancing, a--posteriori solution error analysis and related techniques, non-standard use of distributed and parallel calculations, and adaptive step-by-step mesh refinement. Efficiency of the techniques proposed here has been evaluated using several benchmark problems e.g. residual stresses analysis in chosen elastic-plastic bodies under cyclic loadings. These preliminary tests indicate significant acceleration of the large optimisation processes involved. The final objective of our research is development of an algorithm efficient enough for solving real, large engineering problems.}, notes = {Also known as \cite{2598436} Distributed at GECCO-2014.}, } @inproceedings{Yang:2014:GECCOcomp, author = {Ming Yang and Jing Guan and Zhihua Cai and Changhe Li}, title = {A dimensional-level adaptive differential evolutionary algorithm for continuous optimization}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {genetic algorithms: Poster}, pages = {123--124}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598400}, doi = {doi:10.1145/2598394.2598400}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In differential evolution (DE), the optimal value of the control parameters are problem-dependent. Many improved DE algorithms have been proposed with the aim of improving the exploration ability by adaptively adjusting the values of F. In those algorithms, although the value of F is adaptive at the individual level or at the population level, the value is the same for all dimensions of each individual. Individuals are close to the global optimum at some dimensions, but they may be far away from the global optimum at other dimensions. This indicated that different values of F may be needed for different dimensions. This paper proposed an adaptive scheme for the parameter F at the dimensional level. The scheme was incorporated into the jDE algorithm and tested on a set of 25 scalable benchmark functions. The results showed that the scheme improved the performance of the jDE algorithm, particularly in comparisons with several other peer algorithms on high-dimensional functions.}, notes = {Also known as \cite{2598400} Distributed at GECCO-2014.}, } @inproceedings{Zhang:2014:GECCOcomp, author = {Xin-yuan Zhang and Yue-jiao Gong and Jing-Jing Li and Ying Lin}, title = {Evolutionary computation for lifetime maximization of wireless sensor networks in complex 3D environments}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {genetic algorithms: Poster}, pages = {125--126}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598415}, doi = {doi:10.1145/2598394.2598415}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Scheduling the operating mode of nodes is an effective way to maximise the lifetime of wireless sensor networks (WSN). For a WSN with randomly and densely deployed sensors, we could maximise the lifetime of WSN through finding the maximum number of disjoint complete cover sets. Most of the related work focuses on 2D ideal plane. However, deploying sensors on the 3D surface is more practical in real world scenarios. We propose a novel genetic algorithm with redundant sensor auto-adjustment, termed RSAGA. In order to adapt the original GA into this application, we employ some effective mechanisms along with the basic crossover, mutation, and selection operation. The proposed operator of redundant sensor auto-adjustment schedules the redundant sensors in complete cover sets into incomplete cover sets so as to improve the coverage of the latters. A rearrangement operation specially designed for the critical sensors is embedded in the mutation operator to fine-tune the node arrangement of critical fields. Moreover, we modify the traditional cost function by increasing the penalty of incomplete cover sets for improving the convergence rate of finding feasible solutions. Simulation has been conducted to evaluate the performance of RSAGA. The experimental results show that the proposed RSAGA possesses very promising performance in terms of solution quality and robustness.}, notes = {Also known as \cite{2598415} Distributed at GECCO-2014.}, } @inproceedings{Azad:2014:GECCOcomp, author = {R. Muhammad Atif Azad and David Medernach and Conor Ryan}, title = {Efficient interleaved sampling of training data in genetic programming}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {genetic algorithms, genetic programming: Poster}, pages = {127--128}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598480}, doi = {doi:10.1145/2598394.2598480}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The ability to generalise beyond the training set is important for Genetic Programming (GP). Interleaved Sampling is a recently proposed approach to improve generalisation in GP. In this technique, GP alternates between using the entire data set and only a single data point. Initial results showed that the technique not only produces solutions that generalise well, but that it so happens at a reduced computational expense as half the number of generations only evaluate a single data point. This paper further investigates the merit of interleaving the use of training set with two alternatives approaches. These are: the use of random search instead of a single data point, and simply minimising the tree size. Both of these alternatives are computationally even cheaper than the original setup as they simply do not invoke the fitness function half the time. We test the utility of these new methods on four, well cited, and high dimensional problems from the symbolic regression domain. The results show that the new approaches continue to produce general solutions despite taking only half the fitness evaluations. Size minimisation also prevents bloat while producing competitive results on both training and test data sets. The tree sizes with size ionisation are substantially smaller than the rest of the setups, which further brings down the training costs.}, notes = {Also known as \cite{2598480} Distributed at GECCO-2014.}, } @inproceedings{Barresi:2014:GECCOcomp, author = {Kevin M. Barresi}, title = {Evolved nonlinear predictor functions for lossless image compression}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {genetic algorithms, genetic programming: Poster}, pages = {129--130}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598503}, doi = {doi:10.1145/2598394.2598503}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Due to the increased quantity of digital data, especially in the form of digital images, the need for effective image compression techniques is greater than ever. The JPEG lossless mode relies on predictive coding, in which accurate predictive models are critical. This study presents an efficient method of generating predictor models for input images via genetic programming. It is shown to always produce error images with entropy equal to or lower than those produced by the JPEG lossless mode. This method is demonstrated to have practical use as a real-time asymmetric image compression algorithm due to its ability to quickly and reliably derive prediction models.}, notes = {Also known as \cite{2598503} Distributed at GECCO-2014.}, } @inproceedings{Chennupati:2014:GECCOcomp, author = {Gopinath Chennupati and Conor Ryan and R. Muhammad Atif Azad}, title = {Predict the success or failure of an evolutionary algorithm run}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {genetic algorithms, genetic programming, grammatical evolution: Poster}, pages = {131--132}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598471}, doi = {doi:10.1145/2598394.2598471}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The quality of candidate solutions in evolutionary computation (EC) depend on multiple independent runs and a large number of them fail to guarantee optimal result. These runs consume more or less equal or sometimes higher amount of computational resources on par the runs that produce desirable results. This research work addresses these two issues (run quality, execution time), Run Prediction Model (RPM), in which undesirable quality evolutionary runs are identified to discontinue from their execution. An Ant Colony Optimisation (ACO) based classifier that learns to discover a prediction model from the early generations of an EC run. We consider Grammatical Evolution (GE) as our EC technique to apply RPM that is evaluated on four symbolic regression problems. We establish that the RPM applied GE produces a significant improvement in the success rate while reducing the execution time.}, notes = {Also known as \cite{2598471} Distributed at GECCO-2014.}, } @inproceedings{Cohen:2014:GECCOcomp, author = {Adam T.S. Cohen and Tony White}, title = {CityBreeder: city design with evolutionary computation}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {genetic algorithms, genetic programming: Poster}, pages = {133--134}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598495}, doi = {doi:10.1145/2598394.2598495}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The process of creating city designs is complex and time-consuming. This paper presents CityBreeder, a system which uses Evolutionary Computation to enable the rapid, user-guided development of city designs based on the blending of multiple existing designs.}, notes = {Also known as \cite{2598495} Distributed at GECCO-2014.}, } @inproceedings{Sotto:2014:GECCOcomp, author = {Leo Francoso Dal Piccol Sotto and Vinicius Veloso {de Melo}}, title = {Comparison of linear genetic programming variants for symbolic regression}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {genetic algorithms, genetic programming: Poster}, pages = {135--136}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598472}, doi = {doi:10.1145/2598394.2598472}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper, we compare a basic linear genetic programming (LGP) algorithm against several LGP variants, proposed by us, on two sets of symbolic regression benchmarks. We evaluated the influence of methods to control bloat, investigated these techniques focused in growth of effective code, and examined an operator to consider two successful individuals as modules to be integrated into a new individual. Results suggest that methods that deal with program size, percentage of effective code, and subfunctions, can improve the quality of the final solutions.}, notes = {Also known as \cite{2598472} Distributed at GECCO-2014.}, } @inproceedings{Gaudesi:2014:GECCOcomp, author = {Marco Gaudesi and Giovanni Squillero and Alberto Tonda}, title = {Universal information distance for genetic programming}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {genetic algorithms, genetic programming: Poster}, pages = {137--138}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598440}, doi = {doi:10.1145/2598394.2598440}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper presents a genotype-level distance metric for Genetic Programming (GP) based on the symmetric difference concept: first, the information contained in individuals is expressed as a set of symbols (the content of each node, its position inside the tree, and recurring parent-child structures); then, the difference between two individuals is computed considering the number of elements belonging to one, but not both, of their symbol sets.}, notes = {Also known as \cite{2598440} Distributed at GECCO-2014.}, } @inproceedings{Karim:2014:GECCOcomp, author = {Muhammad Rezaul Karim and Conor Ryan}, title = {On improving grammatical evolution performance in symbolic regression with attribute grammar}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {genetic algorithms, genetic programming, grammatical evolution: Poster}, pages = {139--140}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598488}, doi = {doi:10.1145/2598394.2598488}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper shows how attribute grammar (AG) can be used with Grammatical Evolution (GE) to avoid invalidators in the symbolic regression solutions generated by GE. In this paper, we also show how interval arithmetic can be implemented with AG to avoid selection of certain arithmetic operators or transcendental functions, whenever necessary to avoid infinite output bounds in the solutions. Results and analysis demonstrate that with the proposed extensions, GE shows significantly less overfitting than standard GE and Koza's GP, on the tested symbolic regression problems.}, notes = {Also known as \cite{2598488} Distributed at GECCO-2014.}, } @inproceedings{LaCava:2014:GECCOcomp, author = {William {La Cava} and Lee Spector and Kourosh Danai and Matthew Lackner}, title = {Evolving differential equations with developmental linear genetic programming and epigenetic hill climbing}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {genetic algorithms, genetic programming: Poster}, pages = {141--142}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598491}, doi = {doi:10.1145/2598394.2598491}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper describes a method of solving the symbolic regression problem using developmental linear genetic programming (DLGP) with an epigenetic hill climber (EHC). We propose the EHC for optimising the epigenetic properties of the genotype. The epigenetic characteristics are then inherited through coevolution with the population. Results reveal that the EHC improves performance through maintenance of smaller expressed program sizes. For some problems it produces more successful runs while remaining essentially cost-neutral with respect to number of fitness evaluations.}, notes = {Also known as \cite{2598491} Distributed at GECCO-2014.}, } @inproceedings{Mambrini:2014:GECCOcomp, author = {Andrea Mambrini and Luca Manzoni}, title = {A comparison between geometric semantic GP and cartesian GP for boolean functions learning}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {genetic algorithms, genetic programming: Poster}, pages = {143--144}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598475}, doi = {doi:10.1145/2598394.2598475}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Geometric Semantic Genetic Programming (GSGP) is a recently defined form of Genetic Programming (GP) that has shown promising results on single output Boolean problems when compared with standard tree-based GP. In this paper we compare GSGP with Cartesian GP (CGP) on comprehensive set of Boolean benchmarks, consisting of both single and multiple outputs Boolean problems. The results obtained show that GSGP outperforms also CGP, confirming the efficacy of GSGP in solving Boolean problems.}, notes = {Also known as \cite{2598475} Distributed at GECCO-2014.}, } @inproceedings{Medland:2014:GECCOcomp, author = {Michael Richard Medland and Kyle Robert Harrison and Beatrice Ombuki-Berman}, title = {Incorporating expert knowledge in object-oriented genetic programming}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {genetic algorithms, genetic programming: Poster}, pages = {145--146}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598494}, doi = {doi:10.1145/2598394.2598494}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Genetic programming (GP) has proved to be successful at generating programs which solve a wide variety of problems. Object-oriented GP (OOGP) extends traditional GP by allowing the simultaneous evolution of multiple program trees, and thus multiple functions. OOGP has been shown to be capable of evolving more complex structures than traditional GP. However, OOGP does not facilitate the incorporation of expert knowledge within the resulting evolved type. This paper proposes an alternative OOGP methodology which does incorporate expert knowledge by the use of a user-supplied partially-implemented type definition, i.e. an abstract class.}, notes = {Also known as \cite{2598494} Distributed at GECCO-2014.}, } @inproceedings{Spector:2014:GECCOcomp, author = {Lee Spector and Thomas Helmuth}, title = {Effective simplification of evolved push programs using a simple, stochastic hill-climber}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {genetic algorithms, genetic programming: Poster}, pages = {147--148}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598414}, doi = {doi:10.1145/2598394.2598414}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Genetic programming systems often produce programs that include unnecessary code. This is undesirable for several reasons, including the burdens that overly-large programs put on end-users for program interpretation and maintenance. The problem is exacerbated by recently developed techniques, such as genetic programming with geometric semantic crossover, that tend to produce enormous programs. Methods for automatically simplifying evolved programs are therefore of interest, but automatic simplification is non-trivial in the context of traditional program representations with unconstrained function sets. Here we show how evolved programs expressed in the stack-based Push programming language can be automatically and reliably simplified using a simple, stochastic hill-climber. We demonstrate and quantitatively characterise this simplification process on programs evolved to solve four non-trivial genetic programming problems with qualitatively different function sets.}, notes = {Also known as \cite{2598414} Distributed at GECCO-2014.}, } @inproceedings{Strachan:2014:GECCOcomp, author = {Guilherme Cesario Strachan and Adriano Soares Koshiyama and Douglas Mota Dias and Marley Maria Bernardes Rebuzzi Vellasco and Marco Aurelio Cavalcanti Pacheco}, title = {Towards a quantum-inspired multi-gene linear genetic programming model}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {genetic algorithms, genetic programming: Poster}, pages = {149--150}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598476}, doi = {doi:10.1145/2598394.2598476}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper presents a new model for regression problems based on Multi-Gene and Quantum Inspired Linear Genetic Programming. We discuss theoretical aspects, operators, representation, and experimental results.}, notes = {Also known as \cite{2598476} Distributed at GECCO-2014.}, } @inproceedings{Takamura:2014:GECCOcomp, author = {Seishi Takamura and Atsushi Shimizu}, title = {GPGPU-assisted denoising filter generation for video coding}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {genetic algorithms, genetic programming: Poster}, pages = {151--152}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598462}, doi = {doi:10.1145/2598394.2598462}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {State-of-the-art video coding technologies such as H.265/HEVC employ in-loop denoising filters such as deblocking filter and sample adaptive offset. This paper aims to develop a new type of in-loop denoising filter using an evolutionary method. To boost the evolution, GPGPU is used in filtering process. Generated filter is heavily nonlinear and content-specific. Simulation results demonstrate that proposed method generates better denoising filter in 100x shorter time. The bit rate reduction of 1.492 - 2.569percent was obtained against HM7.2 anchor, the reference software of H.265/HEVC.}, notes = {Also known as \cite{2598462} Distributed at GECCO-2014.}, } @inproceedings{Tao:2014:GECCOcomp, author = {Yanyun Tao and Yuzhen Zhang and Lijun Zhang and Chao Gu}, title = {A projection-based decomposition in EHW method for design of relatively large circuits}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {genetic algorithms, genetic programming: Poster}, pages = {153--154}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598411}, doi = {doi:10.1145/2598394.2598411}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Scalability is the most important issue and not well-addressed in EHW field by far. To solve scalability, this paper proposes a novel EHW system called PD-ES, which integrates a Projection-based Decomposition (PD) and Evolutionary Strategy (ES). PD gradually decomposes a Boolean function by adaptively projecting it onto the property of variables, which makes the complexity and number of sub logic blocks minimised. The gate-level approach-CGP including ES searches complete solutions for these blocks. By employing PD into EHW system, the number of logic gates used for evolving and assembling the sub blocks decreases largely, and the scalability can be improved consequently. The MCNC circuits and n-parity circuits are used to prove the ability of PD-ES in solving scalability. The results illustrate that PD-ES is superior to 3SD-ES and fixed decomposition in evolving large circuits in terms of complexity reduction. Additionally, PD-ES makes success evolution in design of larger n-even-parity circuits as SDR has done.}, notes = {Also known as \cite{2598411} Distributed at GECCO-2014.}, } @inproceedings{Adham:2014:GECCOcomp, author = {Manal T. Adham and Peter J. Bentley}, title = {An artificial ecosystem algorithm applied to the travelling salesman problem}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {integrative genetic and evolutionary computation: Poster}, pages = {155--156}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598438}, doi = {doi:10.1145/2598394.2598438}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {An ecosystem inspired algorithm that aims to take advantage of highly distributed computer architectures is proposed. Our motivation is to grasp the phenomenal properties of ecosystems and use them for large-scale real-world problems. Just as an ecosystem comprises of many separate components that adapt together to form a single synergistic whole, the Artificial Ecosystem Algorithm (AEA) solves a problem by adapting subcomponents such that they fit together and form a single optimal solution. Typical biology inspired algorithms like GA, PSO, BCO, and ACO, represent candidate solutions as individuals in a population. However, AEA uses populations of solution components that are solved individually such that they combine to form the candidate solution. Like species in an ecosystem, AEA has different species that represent sub-parts of the solution, these species evolve and cooperate to form a complete solution.}, notes = {Also known as \cite{2598438} Distributed at GECCO-2014.}, } @inproceedings{Kirley:2014:GECCOcomp, author = {Michael Kirley and Friedrich Burkhard {von der Osten}}, title = {Risk aversion and mobility in the public goods game}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {integrative genetic and evolutionary computation: Poster}, pages = {157--158}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2599988}, doi = {doi:10.1145/2598394.2599988}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper, we study the evolutionary dynamics of the public goods game where the population of mobile individuals is divided into separate groups. We extend the usual discrete strategy game, by introducing 'conditional investors' who have a real-value genetic trait that determines their level of risk aversion, or willingness to invest into the common pool. At the end of each round of the game, each individual has an opportunity to (a) update their risk aversion trait using a form of imitation from within their current group, and (b) to switch groups if they are not satisfied with their payoff in their current group. Detailed simulation experiments show that investment levels can be maintained within groups. The mean value of the risk aversion trait is significantly lower in smaller groups and is correlated with the underlying migration mode. In the conditional migration scenarios, levels of investment consistent with risk aversion emerge.}, notes = {Also known as \cite{2599988} Distributed at GECCO-2014.}, } @inproceedings{Tsang:2014:GECCOcomp, author = {Jeffrey Tsang}, title = {The structure of an 8-state finite transducer representation for prisoner's dilemma}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {integrative genetic and evolutionary computation: Poster}, pages = {159--160}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598498}, doi = {doi:10.1145/2598394.2598498}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The fingerprint operator generates a representation-independent functional signature of a game-playing strategy, which enables the automated analysis of evolved agents. With this, we attempt to study the structure of a relatively small representation - the 8-state finite transducers for Prisoner's Dilemma. Even then, there are almost 3 x 1020 strategies representable, and hence we sample 32,768 strategies uniformly at random for investigation. Accounting for phenotypic duplicates, there are 31,531 distinct strategies in the dataset; we compute all pairwise distances and use a variety of dimensionality reduction techniques to embed it into a manageable space. Results indicate no obvious cutoff scales, and a strong structural similarity with parallel studies on the entirety of even smaller state spaces.}, notes = {Also known as \cite{2598498} Distributed at GECCO-2014.}, } @inproceedings{Ye:2014:GECCOcomp, author = {Shujin Ye and Han Huang and Changjian Xu}, title = {Enhancing the differential evolution with convergence speed controller for continuous optimization problems}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {integrative genetic and evolutionary computation: Poster}, pages = {161--162}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598501}, doi = {doi:10.1145/2598394.2598501}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper, we proposed a convergence speed controller (denoted as CSC) framework to improve the performance of differential evolution for continuous optimization problems.}, notes = {Also known as \cite{2598501} Distributed at GECCO-2014.}, } @inproceedings{Gupta:2014:GECCOcomp, author = {Shikha Gupta and Naveen Kumar}, title = {GPU-based massively parallel quantum inspired genetic algorithm for detection of communities in complex networks}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {parallel evolutionary systems: Poster}, pages = {163--164}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598437}, doi = {doi:10.1145/2598394.2598437}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The paper presents a parallel implementation of a variant of quantum inspired genetic algorithm (QIGA) for the problem of community structure detection in complex networks using NVIDIA Compute Unified Device Architecture (CUDA) technology. The paper explores feasibility of the approach in the domain of complex networks. The approach does not require any knowledge of the number of communities beforehand and works well for both directed and undirected networks. Experiments on benchmark networks show that the method is able to successfully reveal community structure with high modularity.}, notes = {Also known as \cite{2598437} Distributed at GECCO-2014.}, } @inproceedings{Chen:2014:GECCOcomp, author = {Eric Y. Chen and Lin-Shung Huang and Ole J. Mengshoel and Jason D. Lohn}, title = {Darwin: a ground truth agnostic CAPTCHA generator using evolutionary algorithm}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {real world applications: Poster}, pages = {165--166}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598413}, doi = {doi:10.1145/2598394.2598413}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We designed and implemented Darwin, the first CAPTCHA generator using evolutionary algorithm. We evaluated the effectiveness of our proposed CAPTCHAs with MTurk users (non-attackers) and Antigate workers (attackers). Due to our ground-truth agnostic fitness function, we are able to discover a new category of CAPTCHAs in which attackers answer correctly but non-attackers answer incorrectly.}, notes = {Also known as \cite{2598413} Distributed at GECCO-2014.}, } @inproceedings{Cheng:2014:GECCOcompa, author = {Peng Cheng and Jeng-Shyang Pan}, title = {Completely hide sensitive association rules using EMO by deleting transactions}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {real world applications: Poster}, pages = {167--168}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598466}, doi = {doi:10.1145/2598394.2598466}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Data mining techniques enable efficient extraction of useful knowledge from a large data repository. However, it also can disclose sensitive information if used inappropriately. A feasible way to address this problem is to sanitise the database to conceal sensitive information. In this paper, we focus on privacy preserving in association rule mining. In light of the trade off between hiding sensitive rules and disclosing non-sensitive ones during the hiding process, a novel association rule hiding approach is proposed based on evolutionary multi-objective optimisation (EMO). It modifies the original database by deleting identified transactions/tuples to hide sensitive rules. Experiment results are reported to show the effectiveness of the proposed approach.}, notes = {Also known as \cite{2598466} Distributed at GECCO-2014.}, } @inproceedings{Darvishzadeh:2014:GECCOcomp, author = {Amirali Darvishzadeh and Bir Bhanu}, title = {Distributed multi-robot search in the real-world using modified particle swarm optimization}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {real world applications: Poster}, pages = {169--170}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598405}, doi = {doi:10.1145/2598394.2598405}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {A challenging issue in multi-robot system is to design effective algorithms which enable robots to collaborate with one another in order to search and find objects of interest. Unlike most of the research on PSO (particle swarm optimisation) that adopts the method to a virtual multi-agent system, in this paper, we present a framework to use a modified PSO (MPSO) algorithm in a multi-robot system for search task in real-world environments. We modify the algorithm to optimize the total path travelled by robots. Experiments with multiple robots are provided.}, notes = {Also known as \cite{2598405} Distributed at GECCO-2014.}, } @inproceedings{Krawec:2014:GECCOcompb, author = {Walter O. Krawec}, title = {Using evolutionary techniques to analyze the security of quantum key distribution protocols}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {real world applications: Poster}, pages = {171--172}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598410}, doi = {doi:10.1145/2598394.2598410}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper, we describe a new real coded GA which may be used to analyse the security of quantum key distribution (QKD) protocols by estimating the maximally tolerated error rate - an important statistic and, for many newer more complicated protocols, still unknown. Our algorithm takes advantage of several nice features of QKD protocols to simplify the search process and was evaluated on several protocols and can even detect security flaws in a protocol thus showing our algorithm's usefulness in protocol design.}, notes = {Also known as \cite{2598410} Distributed at GECCO-2014.}, } @inproceedings{Mohanty:2014:GECCOcomp, author = {Soumya D. Mohanty}, title = {Detection and estimation of unmodeled narrowband nonstationary signals: application of particle swarm optimization in gravitational wave data analysis}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {real world applications: Poster}, pages = {173--174}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598439}, doi = {doi:10.1145/2598394.2598439}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The extraction of weak signals from instrumental noise is a critical task in ongoing searches for gravitational waves. A detection and estimation method, made feasible by Particle Swarm Optimisation, is presented for a particularly challenging class of signals expected from astrophysical sources.}, notes = {Also known as \cite{2598439} Distributed at GECCO-2014.}, } @inproceedings{Ono:2014:GECCOcomp, author = {Satoshi Ono and Takeru Maehara and Kentaro Nakai and Ryo Ikeda and Koutaro Taniguchi}, title = {Semi-fragile watermark design for detecting illegal two-dimensional barcodes by evolutionary multi-objective optimization}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {real world applications: Poster}, pages = {175--176}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598456}, doi = {doi:10.1145/2598394.2598456}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This study proposes a semi-fragile watermark design method for detecting illegally copied 2D bar codes. The proposed method uses real mobile phones for fitness calculation rather than simulation. In addition, we formulate this task as a multi-objective optimisation problem to design a commonly usable watermark on various mobile phones.}, notes = {Also known as \cite{2598456} Distributed at GECCO-2014.}, } @inproceedings{Schlottfeldt:2014:GECCOcomp, author = {Shana Schlottfeldt and Jon Timmis and Maria Emilia M.T. Walter and Andre C.P.L.F. Carvalho and Jose Alexandre F. Diniz-Filho and Lorena M. Simon and Rafael D. Loyola and Mariana P.C. Telles}, title = {Multi-objective optimization applied to systematic conservation planning and spatial conservation priorities under climate change}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {real world applications: Poster}, pages = {177--178}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598404}, doi = {doi:10.1145/2598394.2598404}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Biodiversity problems require strategies to accomplish specific conservation goals. An underlying principle of these strategies is known as Systematic Conservation Planning (SCP). SCP is an inherently multi-objective (MO) problem but, in the literature, it has been usually dealt with a single objective approach. In addition, SCP analysis tend to assume that conserved biodiversity does not change throughout time. In this paper we propose a MO approach to the SCP problem which increases flexibility through the inclusion of more objectives, which whilst increasing the complexity, significantly augments the amount of information used to provide users with an improved decision support system. We employed ensemble forecasting approach, enriching our analysis by taking into account future climate simulations to estimate species occurrence projected to 2080. Our approach is able to identify sites of high priority for conservation, regions with high risk of investment and sites that may become attractive options in the future. As far as we know, this is the first attempt to apply MO algorithms to a SCP problem associated to climate forecasting, in a dynamic spatial prioritisation analysis for biodiversity conservation.}, notes = {Also known as \cite{2598404} Distributed at GECCO-2014.}, } @inproceedings{Teng:2014:GECCOcomp, author = {Ervin Teng and Derek Kozel and Bob Iannucci and Jason Lohn}, title = {Evolution of digital modulation schemes for radio systems}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {real world applications: Poster}, pages = {179--180}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598449}, doi = {doi:10.1145/2598394.2598449}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We apply an evolutionary strategies (ES) algorithm to the problem of designing modulation schemes used in wireless communication systems. The ES is used to optimise the digital symbol to analog signal mapping, called a constellation. Typical human-designed constellations are compared to the constellations produced by our algorithms in a simulated radio environment with noise and multipath, in terms of bit error rate. We conclude that the algorithm, with diversity maintenance, find solutions that equal or outperform conventional ones in a given radio channel model, especially for those with higher number of symbols in the constellation (arity).}, notes = {Also known as \cite{2598449} Distributed at GECCO-2014.}, } @inproceedings{Vanaret:2014:GECCOcomp, author = {Charlie Vanaret and Nicolas Durand and Jean-Marc Alliot}, title = {Windmill farm pattern optimization using evolutionary algorithms}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {real world applications: Poster}, pages = {181--182}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598506}, doi = {doi:10.1145/2598394.2598506}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {When designing a wind farm layout, we can reduce the number of variables by optimising a pattern instead of considering the position of each turbine. In this paper we show that, by reducing the problem to only two variables defining a grid, we can gain up to 3percent of energy output on simple examples of wind farms dealing with many turbines (up to 1000) while dramatically reducing the computation time.}, notes = {Also known as \cite{2598506} Distributed at GECCO-2014.}, } @inproceedings{Zhang:2014:GECCOcompa, author = {Guang-Wei Zhang and Zhi-Hui Zhan and Ke-Jing Du and Wei-Neng Chen}, title = {Normalization group brain storm optimization for power electronic circuit optimization}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {real world applications: Poster}, pages = {183--184}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598433}, doi = {doi:10.1145/2598394.2598433}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper proposes a novel normalisation group strategy (NGS) to extend brain storm optimisation (BSO) for power electronic circuit (PEC) design and optimization. As different variables in different dimensions of the PEC represent different circuit components such as resistor, capacitor, or inductor, they have different physical significances and various search space that are even not in comparable range. Therefore, the traditional group method used in BSO, which is based on the solution position information, is not suitable when solving PEC. In order to overcome this issue, the NGS proposed in this paper normalizes different dimensions of the solution to the same comparable range. This way, the grouping operator of BSO can work when using BSO to solve PEC. The NGS based BSO (NGBSO) approach has been implemented to optimise the design of a buck regulator in PEC. The results are compared with those obtained by using genetic algorithm (GA) and particle swarm optimization (PSO). Results show that the NGBSO algorithm outperforms GA and PSO in our PEC design and optimization study. Moreover, the NGS can be regarded as an efficient method to extend BSO to real-world application problems whose dimensions are with different physical significances and search ranges.}, notes = {Also known as \cite{2598433} Distributed at GECCO-2014.}, } @inproceedings{Cheng:2014:GECCOcompb, author = {Xin Cheng and Yuanyuan Huang and Xinye Cai and Ou Wei}, title = {An adaptive memetic agorithm based on multiobjecitve optimization for software next release problem}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {search based software engineering: Poster}, pages = {185--186}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598452}, doi = {doi:10.1145/2598394.2598452}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper proposes an adaptive multiobjective memetic algorithm to address the software next release problem. The proposed approach was tested and compared with single objective optimisation approaches as well as multiobjective evolutionary approaches on real test instances mined from bug repository. Interestingly, results show multiobjective approach outperforms single objective approach in general and The proposed approach has the best performance.}, notes = {Also known as \cite{2598452} Distributed at GECCO-2014.}, } @inproceedings{Mkaouer:2014:GECCOcomp, author = {Mohamed Wiem Mkaouer and Marouane Kessentini and Slim Bechikh and Mel \'{O}'Cinn\'{e}ide and Kalyanmoy Deb}, title = {Software refactoring under uncertainty: a robust multi-objective approach}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {search based software engineering: Poster}, pages = {187--188}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598499}, doi = {doi:10.1145/2598394.2598499}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Refactoring large systems involves several sources of uncertainty related to the severity levels of code smells to be corrected and the importance of the classes in which the smells are located. Due to the dynamic nature of software development, these values cannot be accurately determined in practice, leading to refactoring sequences that lack robustness. To address this problem, we introduced a multi-objective robust model, based on NSGA-II, for the software refactoring problem that tries to find the best trade-off between quality and robustness.}, notes = {Also known as \cite{2598499} Distributed at GECCO-2014.}, } @inproceedings{Ren:2014:GECCOcomp, author = {Zhilei Ren and He Jiang and Jifeng Xuan and Shuwei Zhang and Zhongxuan Luo}, title = {Learning from evolved next release problem instances}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {search based software engineering: Poster}, pages = {189--190}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598427}, doi = {doi:10.1145/2598394.2598427}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Taking the Next Release Problem (NRP) as a case study, we intend to analyse the relationship between heuristics and the software engineering problem instances. We adopt an evolutionary algorithm to evolve NRP instances that are either hard or easy for the target heuristic (GRASP in this study), to investigate where a heuristic works well and where it does not, when facing a software engineering problem. Thereafter, we use a feature-based approach to predict the hardness of the evolved instances, with respect to the target heuristic. Experimental results reveal that, the proposed algorithm is able to evolve NRP instances with different hardness. Furthermore, the problem-specific features enables the prediction of the target heuristic's performance.}, notes = {Also known as \cite{2598427} Distributed at GECCO-2014.}, } @inproceedings{Avramiea:2014:GECCOcomp, author = {Arthur Ervin Avramiea and Giorgos Karafotias and A.E. Eiben}, title = {Fate agent evolutionary algorithms with self-adaptive mutation}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {self-* search: Poster}, pages = {191--192}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598497}, doi = {doi:10.1145/2598394.2598497}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Fate Agent EAs form a novel flavour or subclass in EC. The idea is to decompose the main loop of traditional evolutionary algorithms into three independently acting forces, implemented by the so-called Fate Agents, and create an evolutionary process by injecting these agents into a population of candidate solutions. This paper introduces an extension to the original concept, adding a mechanism to self-adapt the mutation of the Breeder Agents. The method improves the behaviour of the original Fate Agent EA on dynamically changing fitness landscapes.}, notes = {Also known as \cite{2598497} Distributed at GECCO-2014.}, } @inproceedings{Handoko:2014:GECCOcomp, author = {Stephanus Daniel Handoko and Duc Thien Nguyen and Zhi Yuan and Hoong Chuin Lau}, title = {Reinforcement learning for adaptive operator selection in memetic search applied to quadratic assignment problem}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {self-* search: Poster}, pages = {193--194}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598451}, doi = {doi:10.1145/2598394.2598451}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Memetic search is well known as one of the state-of-the-art metaheuristics for finding high-quality solutions to NP-hard problems. Its performance is often attributable to appropriate design, including the choice of its operators. In this paper, we propose a Markov Decision Process model for the selection of crossover operators in the course of the evolutionary search. We solve the proposed model by a Q-learning method. We experimentally verify the efficacy of our proposed approach on the benchmark instances of Quadratic Assignment Problem.}, notes = {Also known as \cite{2598451} Distributed at GECCO-2014.}, } @inproceedings{Martin:2014:GECCOcomp, author = {Matthew A. Martin and Daniel R. Tauritz}, title = {Multi-sample evolution of robust black-box search algorithms}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {genetic algorithms, genetic programming, self-* search: Poster}, pages = {195--196}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598448}, doi = {doi:10.1145/2598394.2598448}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Black-Box Search Algorithms (BBSAs) tailored to a specific problem class may be expected to significantly outperform more general purpose problem solvers, including canonical evolutionary algorithms. Recent work has introduced a novel approach to evolving tailored BBSAs through a genetic programming hyper-heuristic. However, that first generation of hyper-heuristics suffered from over specialisation. This poster paper presents a second generation hyper-heuristic employing a multi-sample training approach to alleviate the overspecialization problem. A variety of experiments demonstrated the significant increase in the robustness of the generated algorithms due to the multi-sample approach, clearly showing its ability to outperform established BBSAs. The trade-off between a priori computational time and the generated algorithm robustness is investigated, demonstrating the performance gain possible given additional run-time.}, notes = {Also known as \cite{2598448} Distributed at GECCO-2014.}, } @inproceedings{Misir:2014:GECCOcomp, author = {Mustafa Misir and Stephanus Daniel Handoko and Hoong Chuin Lau}, title = {Building algorithm portfolios for memetic algorithms}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {self-* search: Poster}, pages = {197--198}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598455}, doi = {doi:10.1145/2598394.2598455}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The present study introduces an automated mechanism to build algorithm portfolios for memetic algorithms. The objective is to determine an algorithm set involving combinations of crossover, mutation and local search operators based on their past performance. The past performance is used to cluster algorithm combinations. Top performing combinations are then considered as the members of the set. The set is expected to have algorithm combinations complementing each other with respect to their strengths in a portfolio setting. In other words, each algorithm combination should be good at solving a certain type of problem instances such that this set can be used to solve different problem instances. The set is used together with an online selection strategy. An empirical analysis is performed on the Quadratic Assignment problem to show the advantages of the proposed approach.}, notes = {Also known as \cite{2598455} Distributed at GECCO-2014.}, } @inproceedings{Sim:2014:GECCOcomp, author = {Lu\'{\i}s F. Sim\,{o}es and A. E. Eiben}, title = {On the locality of neural meta-representations}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {self-* search: Poster}, pages = {199--200}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598478}, doi = {doi:10.1145/2598394.2598478}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We consider the usage of artificial neural networks for representing genotype-phenotype maps, from and into continuous decision variable domains. Through such an approach, genetic representations become explicitly controllable entities, amenable to adaptation. With a view towards understanding the kinds of space transformations neural networks are able to express, we investigate here the typical representation locality given by arbitrary neuro-encoded genotype-phenotype maps.}, notes = {Also known as \cite{2598478} Distributed at GECCO-2014.}, } @inproceedings{Buzdalov:2014:GECCOcomp, author = {Maxim Buzdalov and Arina Buzdalova}, title = {Onemax helps optimizing XdivK:: theoretical runtime analysis for RLS and EA+RL}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {theory: Poster}, pages = {201--202}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598442}, doi = {doi:10.1145/2598394.2598442}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {There exist optimisation problems with the target objective, which is to be optimised, and several extra objectives, which can be helpful in the optimization process. The previously proposed EA+RL method is designed to adaptively select objectives during the run of an optimization algorithm in order to reduce the number of evaluations needed to reach an optimum of the target objective. The case when the extra objective is a fine-grained version of the target one is probably the simplest case when using an extra objective actually helps. We define a coarse-grained version of OneMax called XdivK as follows: XdivK(x)= [OneMax(x)/k] for a parameter k which is a divisor of n- the length of a bit vector. We also define XdivK+OneMax, which is a problem where the target objective is XdivK and a single extra objective is OneMax. In this paper, the randomised local search (RLS) is used in the EA+RL method as an optimisation algorithm. We construct exact expressions for the expected running time of RLS solving the XdivK problem and of the EA+RL method solving the XdivK+OneMax problem. It is shown that the EA+RL method makes optimisation faster, and the speedup is exponential in k.}, notes = {Also known as \cite{2598442} Distributed at GECCO-2014.}, } @inproceedings{Manukyan:2014:GECCOcomp, author = {Narine Manukyan and Margaret J. Eppstein and Jeffrey S. Buzas}, title = {NM landscapes: beyond NK}, booktitle = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, 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 = {978-1-4503-2881-4}, keywords = {theory: Poster}, pages = {203--204}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2598403}, doi = {doi:10.1145/2598394.2598403}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {For the past 25 years, NK landscapes have been the classic benchmarks for modelling combinatorial fitness landscapes with epistatic interactions between up to K+1 of N binary features. However, the ruggedness of NK landscapes grows in large discrete jumps as K increases, and computing the global optimum of unrestricted NK landscapes is an NP-complete problem. Walsh polynomials are a superset of NK landscapes that solve some of the problems. In this paper, we propose a new class of benchmarks called NM landscapes, where M refers to the Maximum order of epistatic interactions between N features. NM landscapes are much more smoothly tunable in ruggedness than NK landscapes and the location and value of the global optima are trivially known. For a subset of NM landscapes the location and magnitude of global minima are also easily computed, enabling proper normalisation of fitnesses. NM landscapes are simpler than Walsh polynomials and can be used with alphabets of any arity, from binary to real-valued. We discuss several advantages of NM landscapes over NK landscapes and Walsh polynomials as benchmark problems for evaluating search strategies.}, notes = {Also known as \cite{2598403} Distributed at GECCO-2014.}, } @inproceedings{Goodman:2014:GECCOcomp, author = {Erik D. Goodman}, title = {Introduction to genetic algorithms}, booktitle = {GECCO 2014 Introductory tutorials}, year = {2014}, editor = {Mengjie Zhang}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {205--226}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605335}, doi = {doi:10.1145/2598394.2605335}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The Introduction to Genetic Algorithms Tutorial is aimed at GECCO attendees with limited knowledge of genetic algorithms, and will start 'at the beginning,' describing first a 'classical' genetic algorithm in terms of the biological principles on which it is loosely based, then present some of the fundamental results that describe its performance, using the schema concept. It will cover some variations on the classical model, some successful applications of genetic algorithms, and advances that are making genetic algorithms more useful.}, notes = {Also known as \cite{2605335} Distributed at GECCO-2014.}, } @inproceedings{OReilly:2014:GECCOcomp, author = {Una-May O'Reilly}, title = {Genetic programming: a tutorial introduction}, booktitle = {GECCO 2014 Introductory tutorials}, year = {2014}, editor = {Mengjie Zhang}, isbn13 = {978-1-4503-2881-4}, keywords = {genetic algorithms, genetic programming}, pages = {227--250}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605336}, doi = {doi:10.1145/2598394.2605336}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Genetic programming emerged in the early 1990's as one of the most exciting new evolutionary algorithm paradigms. It has rapidly grown into a thriving area of research and application. While sharing the evolutionary inspired algorithm principles of a genetic algorithm, it differs by exploiting an executable genome. Genetic programming evolves a 'program' to solve a problem rather than a single solution. This tutorial introduces the basic genetic programming framework. It explains how the powerful capability of genetic programming is derived from modular algorithmic components: executable representations such as an abstract syntax tree, variation operators that preserve syntax and explore a variable length, hierarchical solution space, appropriately chosen programming functions and fitness function specification.}, notes = {Also known as \cite{2605336} Distributed at GECCO-2014.}, } @inproceedings{Back:2014:GECCOcomp, author = {Thomas B\"{a}ck}, title = {Introduction to evolution strategies}, booktitle = {GECCO 2014 Introductory tutorials}, year = {2014}, editor = {Mengjie Zhang}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {251--280}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605337}, doi = {doi:10.1145/2598394.2605337}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This tutorial gives a basic introduction to evolution strategies, a class of evolutionary algorithms. Key features such as mutation, recombination and selection operators are explained, and specifically the concept of self-adaptation of strategy parameters is introduced. All algorithmic concepts are explained to a level of detail such that an implementation of basic evolution strategies is possible. In addition, the tutorial also presents a brief taxonomy of contemporary evolution strategy variants, including e.g. the CMA-ES and variations thereof, and compares their performance for a small number of function evaluations - which represents many of today's practical application cases. Some guidelines for utilization as well as some application examples are also given.}, notes = {Also known as \cite{2605337} Distributed at GECCO-2014.}, } @inproceedings{DeJong:2014:GECCOcomp, author = {Kenneth {De Jong}}, title = {Evolutionary computation: a unified approach}, booktitle = {GECCO 2014 Introductory tutorials}, year = {2014}, editor = {Mengjie Zhang}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {281--296}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605338}, doi = {doi:10.1145/2598394.2605338}, publisher = {ACM}, publisher_address = {New York, NY, USA}, notes = {Also known as \cite{2605338} Distributed at GECCO-2014.}, } @inproceedings{Brockhoff:2014:GECCOcomp, author = {Dimo Brockhoff}, title = {GECCO 2014 tutorial on evolutionary multiobjective optimization}, booktitle = {GECCO 2014 Introductory tutorials}, year = {2014}, editor = {Mengjie Zhang}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {297--322}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605339}, doi = {doi:10.1145/2598394.2605339}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Many optimisation problems are multiobjective in nature in the sense that multiple, conflicting criteria need to be optimised simultaneously. Due to the conflict between objectives, usually, no single optimal solution exists. Instead, the optimum corresponds to a set of so-called Pareto-optimal solutions for which no other solution has better function values in all objectives. Evolutionary Multiobjective Optimisation (EMO) algorithms are widely used in practice for solving multiobjective optimisation problems due to several reasons. As stochastic black box algorithms, EMO approaches allow to tackle problems with nonlinear, nondifferentiable, or noisy objective functions. As set-based algorithms, they allow to compute or approximate the full set of Pareto-optimal solutions in one algorithm run---opposed to classical solution-based techniques from the multicriteria decision making (MCDM) field. Using EMO approaches in practice has two other advantages: they allow to learn about a problem formulation, for example, by automatically revealing common design principles among (Pareto-optimal) solutions (innovization) and it has been shown that certain single-objective problems become easier to solve with randomised search heuristics if the problem is reformulated as a multiobjective one (multiobjectivization). This tutorial aims at giving a broad introduction to the EMO field and at presenting some of its recent research results in more detail. More specifically, we are going to (i) introduce the basic principles of EMO algorithms in comparison to classical solution-based approaches, (ii) show a few practical examples which motivate the use of EMO in terms of the mentioned innovization and multiobjectivization principles, and (iii) present a general overview of state-of-the-art algorithms and techniques. Moreover, we will present some of the most important research results in areas such as indicator-based EMO, preference articulation, and performance assessment. Though classified as introductory, this tutorial is intended for both novices and regular users of EMO. Those without any knowledge will learn about the foundations of multiobjective optimisation and the basic working principles of state-of-the-art EMO algorithms. Open questions, presented throughout the tutorial, can serve for all participants as a starting point for future research and/or discussions during the conference.}, notes = {Also known as \cite{2605339} Distributed at GECCO-2014.}, } @inproceedings{Rothlauf:2014:GECCOcomp, author = {Franz Rothlauf}, title = {Representations for evolutionary algorithms}, booktitle = {GECCO 2014 Introductory tutorials}, year = {2014}, editor = {Mengjie Zhang}, isbn13 = {978-1-4503-2881-4}, keywords = {genetic algorithms, genetic programming, grammatical evolution, cartesian genetic programming}, pages = {323--344}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605340}, doi = {doi:10.1145/2598394.2605340}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Successful and efficient use of evolutionary algorithms (EA) depends on the choice of the genotype, the problem representation (mapping from genotype to phenotype) and on the choice of search operators that are applied to the genotypes. These choices cannot be made independently of each other. The question whether a certain representation leads to better performing EAs than an alternative representation can only be answered when the operators applied are taken into consideration. The reverse is also true: deciding between alternative operators is only meaningful for a given representation. In EA practice one can distinguish two complementary approaches. The first approach uses indirect representations where a solution is encoded in a standard data structure, such as strings, vectors, or discrete permutations, and standard off-the-shelf search operators are applied to these genotypes. This is for example the case in standard genetic algorithms, evolution strategies, and some genetic programming approaches like grammatical evolution or cartesian genetic programming. To evaluate the solution, the genotype needs to be mapped to the phenotype space. The proper choice of this genotype-phenotype mapping is important for the performance of the EA search process. The second approach, the direct representation, encodes solutions to the problem in its most 'natural' space and designs search operators to operate on this representation. Research in the last few years has identified a number of key concepts to analyse the influence of representation-operator combinations on EA performance. Relevant properties of representations are locality and redundancy. Locality is a result of the interplay between the search operator and the genotype-phenotype mapping. Representations are redundant if the number of phenotypes exceeds the number of possible genotypes. Redundant representations can lead to biased encodings if some phenotypes are on average represented by a larger number of genotypes or search operators favour some kind of phenotypes. The tutorial gives a brief overview about existing guidelines for representation design, illustrates the different aspects of representations, gives a brief overview of models describing the different aspects, and illustrates the relevance of the aspects with practical examples. It is expected that the participants have a basic understanding of EA principles.}, notes = {Also known as \cite{2605340} Distributed at GECCO-2014.}, } @inproceedings{Wineberg:2014:GECCOcomp, author = {Mark Wineberg}, title = {Statistical analysis for evolutionary computation: an introduction}, booktitle = {GECCO 2014 Introductory tutorials}, year = {2014}, editor = {Mengjie Zhang}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {345--380}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605341}, doi = {doi:10.1145/2598394.2605341}, publisher = {ACM}, publisher_address = {New York, NY, USA}, notes = {Also known as \cite{2605341} Distributed at GECCO-2014.}, } @inproceedings{Engelbrecht:2014:GECCOcomp, author = {Andries Engelbrecht}, title = {Particle swarm optimization}, booktitle = {GECCO 2014 Introductory tutorials}, year = {2014}, editor = {Mengjie Zhang}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {381--406}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605342}, doi = {doi:10.1145/2598394.2605342}, publisher = {ACM}, publisher_address = {New York, NY, USA}, notes = {Also known as \cite{2605342} Distributed at GECCO-2014.}, } @inproceedings{Lanzi:2014:GECCOcomp, author = {Pier Luca Lanzi}, title = {Learning classifier systems: a gentle introduction}, booktitle = {GECCO 2014 Introductory tutorials}, year = {2014}, editor = {Mengjie Zhang}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {407--430}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605343}, doi = {doi:10.1145/2598394.2605343}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Learning Classifier Systems were introduced in the 1970s by John H. Holland as highly adaptive, cognitive systems. More than 40 years later, the introduction of Stewart W. Wilson's XCS, a highly engineered classifier system model, has transformed them into a state-of-the-art machine learning system. Learning classifier systems can effectively solve data-mining problems, reinforcement learning problems, and also cognitive, robotics control problems. In comparison to other, non-evolutionary machine learning techniques, their performance is competitive or superior, dependent on the setup and problem. Learning classifier systems can work both online and offline, they are extremely flexible, applicable to a larger range of problems, and are highly adaptive. Moreover, system knowledge can be easily extracted, visualised, or even used to focus the progressive search on particular interesting subspaces. This tutorial provides a gentle introduction to learning classifier systems and their general functionality. It then surveys the current theoretical understanding of the systems. Finally, we provide a suite of current successful LCS applications and discuss the most promising areas for future applications and research directions.}, notes = {Also known as \cite{2605343} Distributed at GECCO-2014.}, } @inproceedings{Thierens:2014:GECCOcomp, author = {Dirk Thierens and Peter A.N. Bosman}, title = {Model-based evolutionary algorithms}, booktitle = {GECCO 2014 Introductory tutorials}, year = {2014}, editor = {Mengjie Zhang}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {431--458}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605344}, doi = {doi:10.1145/2598394.2605344}, publisher = {ACM}, publisher_address = {New York, NY, USA}, notes = {Also known as \cite{2605344} Distributed at GECCO-2014.}, } @inproceedings{Lehre:2014:GECCOcomp, author = {Per Kristian Lehre and Pietro S. Oliveto}, title = {Runtime analysis of evolutionary algorithms: basic introduction}, booktitle = {GECCO 2014 Introductory tutorials}, year = {2014}, editor = {Mengjie Zhang}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {459--486}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605345}, doi = {doi:10.1145/2598394.2605345}, publisher = {ACM}, publisher_address = {New York, NY, USA}, notes = {Also known as \cite{2605345} Distributed at GECCO-2014.}, } @inproceedings{Miikkulainen:2014:GECCOcomp, author = {Risto Miikkulainen}, title = {Evolving neural networks}, booktitle = {GECCO 2014 Introductory tutorials}, year = {2014}, editor = {Mengjie Zhang}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {487--512}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605346}, doi = {doi:10.1145/2598394.2605346}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Neuroevolution, i.e. evolution of artificial neural networks, has recently emerged as a powerful technique for solving challenging reinforcement learning problems. Compared to traditional (e.g. value-function based) methods, neuroevolution is especially strong in domains where the state of the world is not fully known: The state can be disambiguated through recurrency, and novel situations handled through pattern matching. In this tutorial, I will review (1) neuroevolution methods that evolve fixed-topology networks, network topologies, and network construction processes, (2) ways of combining traditional neural network learning algorithms with evolutionary methods, and (3) applications of neuroevolution to control, robotics, artificial life, and games.}, notes = {Also known as \cite{2605346} Distributed at GECCO-2014.}, } @inproceedings{HANSEN:2014:GECCOcomp, author = {Nikolaus HANSEN and Anne Auger}, title = {Evolution strategies and CMA-ES (covariance matrix adaptation)}, booktitle = {GECCO 2014 Advanced tutorials}, year = {2014}, editor = {Mengjie Zhang}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {513--534}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605347}, doi = {doi:10.1145/2598394.2605347}, publisher = {ACM}, publisher_address = {New York, NY, USA}, notes = {Also known as \cite{2605347} Distributed at GECCO-2014.}, } @inproceedings{CoelloCoello:2014:GECCOcomp, author = {Carlos Artemio {Coello Coello}}, title = {Constraint-handling techniques used with evolutionary algorithms}, booktitle = {GECCO 2014 Advanced tutorials}, year = {2014}, editor = {Mengjie Zhang}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {535--558}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605348}, doi = {doi:10.1145/2598394.2605348}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Evolutionary Algorithms (EAs), when used for global optimisation, can be seen as unconstrained optimization techniques. Therefore, they require an additional mechanism to incorporate constraints of any kind (i.e., inequality, equality, linear, nonlinear) into their fitness function. Although the use of penalty functions (very popular with mathematical programming techniques) may seem an obvious choice, this sort of approach requires a careful fine tuning of the penalty factors to be used. Otherwise, an EA may be unable to reach the feasible region (if the penalty is too low) or may reach quickly the feasible region but being unable to locate solutions that lie in the boundary with the infeasible region (if the penalty is too severe). This has motivated the development of a number of approaches to incorporate constraints into the fitness function of an EA. This tutorial will cover the main proposals in current use, including novel approaches such as the use of tournament rules based on feasibility, multiobjective optimization concepts, hybrids with mathematical programming techniques (e.g., Lagrange multipliers), cultural algorithms, and artificial immune systems, among others. Other topics such as the importance of maintaining diversity, current benchmarks and the use of alternative search engines (e.g., particle swarm optimization, differential evolution, evolution strategies, etc.) will be also discussed (as time allows).}, notes = {Also known as \cite{2605348} Distributed at GECCO-2014.}, } @inproceedings{Whitley:2014:GECCOcomp, author = {Darrell Whitley}, title = {Blind no more: constant time non-random improving moves and exponentially powerful recombination}, booktitle = {GECCO 2014 Advanced tutorials}, year = {2014}, editor = {Mengjie Zhang}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {559--580}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605349}, doi = {doi:10.1145/2598394.2605349}, publisher = {ACM}, publisher_address = {New York, NY, USA}, notes = {Also known as \cite{2605349} Distributed at GECCO-2014.}, } @inproceedings{Spector:2014:GECCOcompa, author = {Lee Spector}, title = {Expressive genetic programming}, booktitle = {GECCO 2014 Advanced tutorials}, year = {2014}, editor = {Mengjie Zhang}, isbn13 = {978-1-4503-2881-4}, keywords = {genetic algorithms, genetic programming}, pages = {581--606}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605350}, doi = {doi:10.1145/2598394.2605350}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The language in which evolving programs are expressed can have significant impacts on the problem-solving capabilities of a genetic programming system. These impacts stem both from the absolute computational power of the languages that are used, as elucidated by formal language theory, and from the ease with which various computational structures can be produced by random code generation and by the action of genetic operators. Highly expressive languages can facilitate the evolution of programs for any computable function using, when appropriate, multiple data types, evolved subroutines, evolved control structures, evolved data structures, and evolved modular program and data architectures. In some cases expressive languages can even support the evolution of programs that express methods for their own reproduction and variation (and hence for the evolution of their offspring). This tutorial will begin with a comparative survey of approaches to the evolution of programs in expressive programming languages ranging from machine code to graphical and grammatical representations. Within this context it will then provide a detailed introduction to the Push programming language, which was designed specifically for expressiveness and specifically for use in genetic programming systems. Push programs are syntactically unconstrained but can nonetheless make use of multiple data types and express arbitrary control structures, supporting the evolution of complex, modular programs in a particularly simple and flexible way. The Push language will be described and demonstrated, and ten years of Push-based research, including the production of human-competitive results, will be briefly surveyed. The tutorial will conclude with a discussion of recent enhancements to Push that are intended to support the evolution of complex and robust software systems.}, notes = {Also known as \cite{2605350} Distributed at GECCO-2014.}, } @inproceedings{Neumann:2014:GECCOcomp, author = {Frank Neumann and Andrew M. Sutton}, title = {Parameterized complexity analysis of evolutionary algorithms}, booktitle = {GECCO 2014 Advanced tutorials}, year = {2014}, editor = {Mengjie Zhang}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {607--622}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605351}, doi = {doi:10.1145/2598394.2605351}, publisher = {ACM}, publisher_address = {New York, NY, USA}, notes = {Also known as \cite{2605351} Distributed at GECCO-2014.}, } @inproceedings{Doerr:2014:GECCOcomp, author = {Benjamin Doerr and Carola Doerr}, title = {Black-box complexity: from complexity theory to playing mastermind}, booktitle = {GECCO 2014 Advanced tutorials}, year = {2014}, editor = {Mengjie Zhang}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {623--646}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605352}, doi = {doi:10.1145/2598394.2605352}, publisher = {ACM}, publisher_address = {New York, NY, USA}, notes = {Also known as \cite{2605352} Distributed at GECCO-2014.}, } @inproceedings{Witt:2014:GECCOcomp, author = {Carsten Witt}, title = {Bioinspired computation in combinatorial optimization: algorithms and their computational complexity}, booktitle = {GECCO 2014 Advanced tutorials}, year = {2014}, editor = {Mengjie Zhang}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {647--686}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605353}, doi = {doi:10.1145/2598394.2605353}, publisher = {ACM}, publisher_address = {New York, NY, USA}, notes = {Also known as \cite{2605353} Distributed at GECCO-2014.}, } @inproceedings{Sudholt:2014:GECCOcomp, author = {Dirk Sudholt}, title = {Theory of swarm intelligence}, booktitle = {GECCO 2014 Advanced tutorials}, year = {2014}, editor = {Mengjie Zhang}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {687--708}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605354}, doi = {doi:10.1145/2598394.2605354}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Social animals as found in fish schools, bird flocks, bee hives, and ant colonies are able to solve highly complex problems in nature. This includes foraging for food, constructing astonishingly complex nests, and evading or defending against predators. Remarkably, these animals in many cases use very simple, decentralised communication mechanisms that do not require a single leader. This makes the animals perform surprisingly well, even in dynamically changing environments. The collective intelligence of such animals is known as swarm intelligence and it has inspired popular and very powerful optimisation paradigms, including ant colony optimisation (ACO) and particle swarm optimization (PSO). The reasons behind their success are often elusive. We are just beginning to understand when and why swarm intelligence algorithms perform well, and how to use swarm intelligence most effectively. Understanding the fundamental working principles that determine their efficiency is a major challenge. This tutorial will give a comprehensive overview of recent theoretical results on swarm intelligence algorithms, with an emphasis on their efficiency (runtime/computational complexity). In particular, the tutorial will show how techniques for the analysis of evolutionary algorithms can be used to analyse swarm intelligence algorithms and how the performance of swarm intelligence algorithms compares to that of evolutionary algorithms. The results shed light on the working principles of swarm intelligence algorithms, identify the impact of parameters and other design choices on performance, and thus help to use swarm intelligence more effectively. The tutorial will be divided into a first, larger part on ACO and a second, smaller part on PSO. For ACO we will consider simple variants of the MAX-MIN ant system. Investigations of example functions in pseudo-Boolean optimisation demonstrate that the choices of the pheromone update strategy and the evaporation rate have a drastic impact on the running time. We further consider the performance of ACO on illustrative problems from combinatorial optimisation: constructing minimum spanning trees, solving shortest path problems with and without noise, and finding short tours for the TSP. For particle swarm optimisation, the tutorial will cover results on PSO for pseudo-Boolean optimisation as well as a discussion of theoretical results in continuous spaces.}, notes = {Also known as \cite{2605354} Distributed at GECCO-2014.}, } @inproceedings{Malago:2014:GECCOcomp, author = {Luigi Malag\`{o} and Tobias Glasmachers}, title = {Information geometry in evolutionary computation}, booktitle = {GECCO 2014 Advanced tutorials}, year = {2014}, editor = {Mengjie Zhang}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {709--726}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605355}, doi = {doi:10.1145/2598394.2605355}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In recent years there have been independent developments in multiple branches of Evolutionary Computation (EC) that interpret population-based and model-based search algorithms in terms of information geometric concepts. This trend has resulted in the development of novel algorithms and in improved understanding of existing ones. This tutorial aims at making this new line of research accessible to a broader range of researchers. A statistical model, identified by a parametric family of distributions, is equipped with an intrinsic (Riemannian) geometry, the so-called information geometry. From this perspective, a statistical model is a manifold of distributions where the inner product is given by the Fisher information metric. Any evolutionary algorithm that implicitly or explicitly evolves the parameters of a search distribution defines a dynamic over the manifold. Taking into account the Riemannian geometry of the new search space given by the search distributions allows for the description and analysis of evolutionary operators in a new light. Notably, this framework can be used for the study of optimisation algorithms. A core idea of several recent and novel heuristics, both in the continuous and the discrete domain, such as Estimation of Distribution Algorithms (EDAs) and Natural Evolution Strategies (NESs), is to perform stochastic gradient descent directly on the space of search distributions. However the definition of gradient depends on the metric, which is why it becomes fundamental to consider the information geometry of the space of search distributions. Despite being equivalent to classical gradient-based methods for a stochastically relaxed problem the approach performs randomised direct search on the original search space: the generation of an offspring population as well as selection and strategy adaptation turn out to implicitly sample a search distribution in a statistical model and to perform a stochastic gradient step in the direction of the natural gradient. Particular strengths of the information geometric framework are its ability to unify optimisation in discrete and continuous domains as well as the traditionally separate processes of optimization and strategy parameter adaptation. Respecting the intrinsic information geometry automatically results in powerful invariance principles. The framework can be seen as an analysis toolbox for existing methods, as well as a generic design principle for novel algorithms. This tutorial will introduce from scratch the mathematical concept of information geometry to the EC community. It will transport not only rigorous definitions but also geometric intuition on Riemannian geometry, information geometry, natural gradient, and stochastic gradient descent. Stochastic relaxations of EC problems will act as a glue. The framework will be made accessible with applications to basic as well as state-of-the-art algorithms operating on discrete and continuous domains.}, notes = {Also known as \cite{2605355} Distributed at GECCO-2014.}, } @inproceedings{Alba:2014:GECCOcomp, author = {Enrique Alba}, title = {Cellular genetic algorithms}, booktitle = {GECCO 2014 Specialized tutorials}, year = {2014}, editor = {Mengjie Zhang}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {733--748}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605356}, doi = {doi:10.1145/2598394.2605356}, publisher = {ACM}, publisher_address = {New York, NY, USA}, notes = {Also known as \cite{2605356} Distributed at GECCO-2014.}, } @inproceedings{Jansen:2014:GECCOcomp, author = {Thomas Jansen and Christine Zarges}, title = {Artificial immune systems for optimisation}, booktitle = {GECCO 2014 Specialized tutorials}, year = {2014}, editor = {Mengjie Zhang}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {749--764}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605357}, doi = {doi:10.1145/2598394.2605357}, publisher = {ACM}, publisher_address = {New York, NY, USA}, notes = {Also known as \cite{2605357} Distributed at GECCO-2014.}, } @inproceedings{Stanley:2014:GECCOcomp, author = {Kenneth O. Stanley}, title = {Generative and developmental systems tutorial}, booktitle = {GECCO 2014 Specialized tutorials}, year = {2014}, editor = {Mengjie Zhang}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {765--794}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605358}, doi = {doi:10.1145/2598394.2605358}, publisher = {ACM}, publisher_address = {New York, NY, USA}, notes = {Also known as \cite{2605358} Distributed at GECCO-2014.}, } @inproceedings{Cagnoni:2014:GECCOcomp, author = {Stefano Cagnoni}, title = {Evolutionary image analysis and signal processing}, booktitle = {GECCO 2014 Specialized tutorials}, year = {2014}, editor = {Mengjie Zhang}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {795--818}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605359}, doi = {doi:10.1145/2598394.2605359}, publisher = {ACM}, publisher_address = {New York, NY, USA}, notes = {Also known as \cite{2605359} Distributed at GECCO-2014.}, } @inproceedings{Li:2014:GECCOcompa, author = {Xiaodong Li}, title = {Decomposition and cooperative coevolution techniques for large scale global optimization}, booktitle = {GECCO 2014 Specialized tutorials}, year = {2014}, editor = {Mengjie Zhang}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {819--838}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605360}, doi = {doi:10.1145/2598394.2605360}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Many real-world optimisation problems involve a large number of decision variables. For example, in shape optimization a large number of shape design variables are often used to represent complex shapes, such as turbine blades, aircraft wings, and heat exchangers. However, existing optimization methods are ill-equipped in dealing with this sort of large scale global optimisation (LSGO) problems. A natural approach to tackle LSGO problems is to adopt a divide-and-conquer strategy. A good example is the early work on a cooperative coevolutionary (CC) algorithm by Potter and De Jong (1994), where a problem is decomposed into several subcomponents of smaller sizes, and then each subcomponent is 'cooperatively coevolved' with other subcomponents. In this tutorial we will provide an overview on the recent development of CC algorithms for LSGO problems, in particular those extended from the original Potter and De Jong's CC model. One key challenge in applying CC is how to best decompose a problem in a way such that the inter-dependency between subcomponents can be kept at minimum. Another challenge is how to best allocate a fixed computational budget among different subcomponents when there is an imbalance of contributions from these subcomponents. Equally dividing the budget among these subcomponents and optimising each through a round-robin fashion (as in the classic CC method) may not be a wise strategy, since it can waste lots of computational resource. Many more research questions still remain to be answered. In recent years, several interesting decomposition methods (or variable grouping methods) have been proposed. This tutorial will survey these methods, and identify their strengths and weakness. The tutorial will also describe a contribution-based method for better allocating computation among the subcomponents. Finally we will present a newly designed variable grouping method, namely differential grouping, which outperforms those early surveyed decomposition methods. We will provide experimental results on CEC'2010 LSGO benchmark functions to demonstrate the effectiveness of this method.}, notes = {Also known as \cite{2605360} Distributed at GECCO-2014.}, } @inproceedings{Shehu:2014:GECCOcomp, author = {Amarda Shehu and Kenneth A. {De Jong}}, title = {Evolutionary search algorithms for protein modeling: from de novo structure prediction to comprehensive maps of functionally-relevant structures of protein chains and assemblies}, booktitle = {GECCO 2014 Specialized tutorials}, year = {2014}, editor = {Mengjie Zhang}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {839--856}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605361}, doi = {doi:10.1145/2598394.2605361}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In the last two decades, great progress has been made in molecular modelling through computational treatments of biological molecules grounded in evolutionary search techniques. Evolutionary search algorithms (EAs) are gaining popularity beyond exploring the relationship between sequence and function in biomolecules. In particular, recent work is showing the promise of EAs in exploring structure spaces of protein chains and protein assemblies to address open-standing problems in computational structural biology, such as de novo structure prediction and protein-protein docking. Exploring effective interleaving of global and local search has led to hybrid EAs that are now competitive with the Monte Carlo-based frameworks that have traditionally dominated de novo structure prediction. Similar advances have been made in protein-protein docking. Deeper understanding of the constraints posed by highly-coupled modular systems like proteins and integration of domain knowledge has resulted in effective reproductive operators. Multi-objective optimisation has also shown promise in dealing with the conflicting terms that make up protein energy functions and effectively exploring protein energy surfaces. Combinations of these techniques have recently resulted in powerful stochastic search frameworks that go beyond de novo structure prediction and are capable of yielding comprehensive maps of possible diverse functionally-relevant structures of proteins. The objective of this tutorial is to introduce the EC community to the rapid developments on EA-based frameworks for protein structure modelling through a concise but comprehensive review of developments in this direction over the last decade. The review will be accompanied with specific detailed highlights and interactive software demonstrations of representative methods. The tutorial will expand the view of EA-based frameworks beyond sequence-focused application settings. The tutorial will introduce EC researchers to open problems in computational structural biology and in the process spur the design of novel and powerful evolutionary search techniques.}, notes = {Also known as \cite{2605361} Distributed at GECCO-2014.}, } @inproceedings{Deb:2014:GECCOcomp, author = {Kalyanmoy Deb and Ankur Sinha}, title = {Evolutionary bilevel optimization (EBO)}, booktitle = {GECCO 2014 Specialized tutorials}, year = {2014}, editor = {Mengjie Zhang}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {857--876}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605362}, doi = {doi:10.1145/2598394.2605362}, publisher = {ACM}, publisher_address = {New York, NY, USA}, notes = {Also known as \cite{2605362} Distributed at GECCO-2014.}, } @inproceedings{Tomassini:2014:GECCOcomp, author = {Marco Tomassini}, title = {Introduction to evolutionary game theory}, booktitle = {GECCO 2014 Specialized tutorials}, year = {2014}, editor = {Mengjie Zhang}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {877--890}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605363}, doi = {doi:10.1145/2598394.2605363}, publisher = {ACM}, publisher_address = {New York, NY, USA}, notes = {Also known as \cite{2605363} Distributed at GECCO-2014.}, } @inproceedings{Smith:2014:GECCOcomp, author = {Stephen L. Smith}, title = {Medical applications of evolutionary computation}, booktitle = {GECCO 2014 Specialized tutorials}, year = {2014}, editor = {Mengjie Zhang}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {891--920}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605364}, doi = {doi:10.1145/2598394.2605364}, publisher = {ACM}, publisher_address = {New York, NY, USA}, notes = {Also known as \cite{2605364} Distributed at GECCO-2014.}, } @inproceedings{Lopez-Iba:2014:GECCOcomp, author = {Manuel L\'{o}pez-Ib\'{a}\,{n}ez and Thomas St\"{u}tzle}, title = {Automatic (offline) configuration of algorithms}, booktitle = {GECCO 2014 Specialized tutorials}, year = {2014}, editor = {Mengjie Zhang}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {921--946}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605365}, doi = {doi:10.1145/2598394.2605365}, publisher = {ACM}, publisher_address = {New York, NY, USA}, notes = {Also known as \cite{2605365} Distributed at GECCO-2014.}, } @inproceedings{Bhalla:2014:GECCOcomp, author = {Navneet Bhalla and Peter J. Bentley and Marco Dorigo}, title = {Self-assembly}, booktitle = {GECCO 2014 Specialized tutorials}, year = {2014}, editor = {Mengjie Zhang}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {947--966}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605366}, doi = {doi:10.1145/2598394.2605366}, publisher = {ACM}, publisher_address = {New York, NY, USA}, notes = {Also known as \cite{2605366} Distributed at GECCO-2014.}, } @inproceedings{Alvarez:2014:GECCOcomp, author = {Isidro M. Alvarez and Will N. Browne and Mengjie Zhang}, title = {Reusing learned functionality in XCS: code fragments with constructed functionality and constructed features}, booktitle = {17th annual international workshop on learning classifier systems}, year = {2014}, editor = {Ryan Urbanowicz and Muhammad Iqbal and Kamran Shafi}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {969--976}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2611383}, doi = {doi:10.1145/2598394.2611383}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper expands on work previously conducted on the XCS system using code fragments, which are GP-like trees that encapsulate building blocks of knowledge. The usage of code fragments in the XCS system enabled the solution of previously intractable, complex, Boolean problems, e.g. the 135 bit multiplexer domain. However, it was not previously possible to replace functionality at nodes with learnt relationships, which restricted scaling to larger problems and related domains. The aim of this paper is to reuse learnt rule sets as functions. The functions are to be stored along with the code fragments produced as a solution for a problem. The results show for the first time that these learnt functions can be reused in the inner nodes of the code fragment trees. The results are encouraging as there was no statistically significant difference in terms of classification. For the simpler problems the new system XCSCF2, required much less instances than the XCSCFC to solve the problems. However, for the more complex problems, the XCSCF2 required more instances than XCSCFC; but the additional time was not prohibitive for the continued development of this approach. The main contribution of this investigation is that functions can be learnt and later reused in the inner nodes of a code fragment tree. This is anticipated to lead to a reduced search space and increased performance both in terms of instances needed to solve a problem and classification accuracy.}, notes = {Also known as \cite{2611383} Distributed at GECCO-2014.}, } @inproceedings{Kuber:2014:GECCOcompa, author = {Karthik Kuber and Stuart W. Card and Kishan G. Mehrotra and Chilukuri K. Mohan}, title = {Rule networks in learning classifier systems}, booktitle = {17th annual international workshop on learning classifier systems}, year = {2014}, editor = {Ryan Urbanowicz and Muhammad Iqbal and Kamran Shafi}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {977--982}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2611382}, doi = {doi:10.1145/2598394.2611382}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Interrelationships between rules can be used to develop network models that can usefully represent the dynamics of Learning Classifier Systems. We examine two different kinds of rule networks and study their significance by testing them on the 20-mux problem. Through this experimentation, we establish that there is latent information in the evolving rule networks alongside the usual information that we gain from the XCS. We analyse these interrelationships using metrics from Network Science. We also show that these network measures behave as reliable indicators of rule set convergence.}, notes = {Also known as \cite{2611382} Distributed at GECCO-2014.}, } @inproceedings{Deng:2014:GECCOcompa, author = {Yiqi Deng and Peter J. Bentley}, title = {Dynamic learning of heart sounds with changing noise: an ais-based multi-agent model using systemic computation}, booktitle = {GECCO 2014 Eighth Annual Workshop on Evolutionary Computation and Multi-Agent Systems and Simulation (ECoMASS)}, year = {2014}, editor = {Forrest Stonedahl and William Rand}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {985--992}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605442}, doi = {doi:10.1145/2598394.2605442}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Agent-Based Models are used to model dynamic systems such as stock markets, societies, and complex biological systems that are difficult to model analytically using partial differential equations. Many agent-based modelling software are designed for serial von-Neumann computer architectures. That limits the speed and scalability of these systems. Systemic computation (SC) is designed to be a model of natural behaviour and, at the same time, a non Von-Neumann architecture with its characteristics similar to multi-agent system. Here we propose a novel method based on an Artificial Immune System (AIS) and implemented on a systemic computer, which is designed to adapt itself over continuous arrival of data to cope with changing patterns of noise without requirement for feedback, as a result of its own experience. Experiments with heartbeat data collected from a clinical trial in hospitals using a digital stethoscope shows the algorithm performs up to 3.60percent better in the precision rate of murmur and 3.96percent better in the recall rate of murmur than other standard anomaly detector approaches such as Multiple Kernel Anomaly Detection (MKAD).}, notes = {Also known as \cite{2605442} Distributed at GECCO-2014.}, } @inproceedings{Gharsellaoui:2014:GECCOcomp, author = {Hamza Gharsellaoui and Hamadi Hasni and Samir {Ben Ahmed}}, title = {A genetic based scheduling approach of real-time reconfigurable embedded systems}, booktitle = {GECCO 2014 Eighth Annual Workshop on Evolutionary Computation and Multi-Agent Systems and Simulation (ECoMASS)}, year = {2014}, editor = {Forrest Stonedahl and William Rand}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {993--998}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605440}, doi = {doi:10.1145/2598394.2605440}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper deals with the problem of scheduling the mixed workload of both homogeneous multiprocessor on-line and off-line periodic tasks in a critical reconfigurable real-time environment by a genetic algorithm. Two forms of automatic reconfigurations which are assumed to be applied at run-time: Addition-Removal of tasks or just modifications of their temporal parameters: worst case execution time (WCET) and/or deadlines. Nevertheless, when such a scenario is applied to save the system at the occurrence of hardware-software faults, or to improve its performance, some real-time properties can be violated at run-time. We define an Intelligent Agent that automatically checks the system's feasibility after any reconfiguration scenario to verify if all tasks meet the required deadlines after a reconfiguration scenario was applied on a multiprocessor embedded real-time system. Indeed, if the system is unfeasible, then the proposed genetic algorithm dynamically provides a solution that meets real-time constraints. This genetic algorithm based on a highly efficient decoding procedure, strongly improves the quality of real-time scheduling in a critical environment. The effectiveness and the performance of the designed approach is evaluated through simulation studies illustrated by testing Hopper's benchmark results.}, notes = {Also known as \cite{2605440} Distributed at GECCO-2014.}, } @inproceedings{Goings:2014:GECCOcomp, author = {Sherri Goings and Emily P.M. Johnston and Naozumi Hiranuma}, title = {The effect of communication on the evolution of cooperative behavior in a multi-agent system}, booktitle = {GECCO 2014 Eighth Annual Workshop on Evolutionary Computation and Multi-Agent Systems and Simulation (ECoMASS)}, year = {2014}, editor = {Forrest Stonedahl and William Rand}, isbn13 = {978-1-4503-2881-4}, keywords = {genetic algorithms, genetic programming}, pages = {999--1006}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605443}, doi = {doi:10.1145/2598394.2605443}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {A team of agents that cooperate to solve a problem together can handle many complex tasks that would not be possible without cooperation. While the benefit is clear, there are still many open questions in how best to achieve this cooperation. In this paper we focus on the role of communication in allowing agents to evolve effective cooperation for a prey capture task. Previous studies of this task have shown mixed results for the benefit of direct communication among predators, and we investigate potential explanations for these seemingly contradictory outcomes. We start by replicating the results of a study that found that agents with the ability to communicate actually performed worse than those without when each member of a team was evolved in a separate population [8]. The simulated world used for these experiments is very simple, and we suggest that communication would become beneficial in a similar but more complex environment. We test several methods of increasing the problem complexity, but find that at best communicating predators perform equally as well as those that cannot communicate. We thus propose that the representation may hinder the success of communication in this environment. The behaviour of each predator is encoded in a neural network, and the networks with communication have 6 inputs as opposed to just 2 for the standard network, giving communicating networks more than twice as many links for which to evolve weights. Another study using a relatively similar environment but genetic programming as a representation finds that communication is clearly beneficial for prey capture [4]. We suggest that adding communication is less costly to these genetic programs as compared to the earlier neural networks and outline experiments to test this theory.}, notes = {Also known as \cite{2605443} Distributed at GECCO-2014.}, } @inproceedings{Jiang:2014:GECCOcomp, author = {Bin Jiang and Lei Wang and Chao Yang and Shuming Peng and Renfa Li}, title = {Modeling the information propagation in an email communication network using an agent-based approach}, booktitle = {GECCO 2014 Eighth Annual Workshop on Evolutionary Computation and Multi-Agent Systems and Simulation (ECoMASS)}, year = {2014}, editor = {Forrest Stonedahl and William Rand}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1007--1014}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2610013}, doi = {doi:10.1145/2598394.2610013}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Development of Internet technology has made the use of email to be one of the predominant means of communication in the information society. Information exchange among people via email service has produced lots of communication data, which have been widely used in research about information propagation on virtual social networks. The focus of this paper is on the 'Enron Email Dataset'. The ideas discussed gave thorough consideration to the diversity of organisational positions' attributes, the dynamic behaviours of users to select information contents and communication partners via email service. We then established a quantitative analysis on the multiple interactive relationships of the email communication network. Further, an agent-based model for modelling the information diffusion in an organisation via email communication network was proposed, by relating the microscopic individual behaviours and the macroscopic system evolution. Based on the simulation experiments, we analysed and compared the topological characteristics and evaluative patterns of our model with the Enron Email Dataset. The experimental results proved that our model was beneficial to uncover the implicit communication mechanisms of a real organisation.}, notes = {Also known as \cite{2610013} Distributed at GECCO-2014.}, } @inproceedings{Lopes:2014:GECCOcomp, author = {Rodolfo Ayala Lopes and Rodrigo C. {Pedrosa Silva} and Alan R.R. Freitas and Felipe Campelo and Frederico G. Guimar\,{a}es}, title = {A study on the configuration of migratory flows in island model differential evolution}, booktitle = {GECCO 2014 Eighth Annual Workshop on Evolutionary Computation and Multi-Agent Systems and Simulation (ECoMASS)}, year = {2014}, editor = {Forrest Stonedahl and William Rand}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1015--1022}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605439}, doi = {doi:10.1145/2598394.2605439}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The Island Model (IM) is a well known multi-population approach for Evolutionary Algorithms (EAs). One of the critical parameters for defining a suitable IM is the migration topology. Basically it determines the Migratory Flows (MF) between the islands of the model which are able to improve the rate and pace of convergence observed in the EAs coupled with IMs. Although, it is possible to find a wide number of approaches for the configuration of MFs, there still is a lack of knowledge about the real performance of these approaches in the IM. In order to fill this gap, this paper presents a thorough experimental analysis of the approaches coupled with the state-of-the-art EA Differential Evolution. The experiments on well known benchmark functions show that there is a trade-off between convergence speed and convergence rate among the different approaches. With respect to the computational times, the results indicate that the increase in implementation complexity does not necessarily represent an increase in the overall execution time.}, notes = {Also known as \cite{2605439} Distributed at GECCO-2014.}, } @inproceedings{Acre:2014:GECCOcomp, author = {Jeremy Acre and Nicholas Zoller and Brent E. Eskridge}, title = {Effects of personality decay on collective movements}, booktitle = {GECCO 2014 student workshop}, year = {2014}, editor = {Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1025--1028}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605678}, doi = {doi:10.1145/2598394.2605678}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In natural systems, many animals organise into groups without a designated leader and still perform complex collective behaviours. Although individuals in the group may be considered equal, all the individuals differ in the traits each of them possess. Of particular interest is the idea of an individual's personality as it often plays a role in determining which individuals lead collective behaviours. Personality is, in part, developed and maintained by an individual's experiences. However, neither an individual, nor its environment remains unchanged. Therefore, there is a need for an individual to continue to gain new experiences to ensure that its information about itself and its environment are current. Since observations have shown that the effects of experience on personality can decay over time, we investigate the effects of this decay on the emergence of leaders and followers and the resulting success of a group's collective movement attempts. Results show that personality decay has a negative effect on the overall success of the group in collective movements as it prevents the emergence of distinct personalities, a necessary requirement for individuals to assume distinct leader and follower roles.}, notes = {Also known as \cite{2605678} Distributed at GECCO-2014.}, } @inproceedings{Ahmed:2014:GECCOcompa, author = {Hazem Radwan Ahmed}, title = {An efficient fitness-based stagnation detection method for particle swarm optimization}, booktitle = {GECCO 2014 student workshop}, year = {2014}, editor = {Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1029--1032}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605669}, doi = {doi:10.1145/2598394.2605669}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Stagnation is a prevalent issue in many heuristic search algorithms, such as Particle Swarm Optimisation (PSO). PSO stagnation occurs when the rate of position changes (or velocities) that attract particles to the global best position approaches zero, potentially leading the swarm to being trapped in a local optimum, especially for deceptive multimodal optimization problems. This paper proposes a novel fitness-based stagnation detection method that effectively and efficiently restarts the search process to escape potential local optima. The main idea of the proposed method is to make use of the already calculated fitness values of swarm particles, instead of their pairwise distance values, to predict an imminent stagnation situation. That is, the proposed fitness-based method does not require any computational overhead of repeatedly calculating pairwise distances between all particles at each iteration. The proposed fitness-based method substantially outperforms the commonly used distance-based method when tested on several classical and advanced (shifted/rotated) benchmark optimisation functions in three ways: 1) The optimisation performance is significantly better performing (using Wilcoxon rank-sum test). 2) The optimisation performance is considerably faster (up to three times). 3) The proposed fitness-based method is less dependent on the problem search space, compared with the distance-based method.}, notes = {Also known as \cite{2605669} Distributed at GECCO-2014.}, } @inproceedings{Buzdalova:2014:GECCOcomp, author = {Arina Buzdalova and Vladislav Kononov and Maxim Buzdalov}, title = {Selecting evolutionary operators using reinforcement learning: initial explorations}, booktitle = {GECCO 2014 student workshop}, year = {2014}, editor = {Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1033--1036}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605681}, doi = {doi:10.1145/2598394.2605681}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In evolutionary optimisation, it is important to use efficient evolutionary operators, such as mutation and crossover. But it is often difficult to decide, which operator should be used when solving a specific optimisation problem. So an automatic approach is needed. We propose an adaptive method of selecting evolutionary operators, which takes a set of possible operators as input and learns what operators are efficient for the considered problem. One evolutionary algorithm run should be enough for both learning and obtaining suitable performance. The proposed EA+RL(O) method is based on reinforcement learning. We test it by solving H-IFF and Travelling Salesman optimisation problems. The obtained results show that the proposed method significantly outperforms random selection, since it manages to select efficient evolutionary operators and ignore inefficient ones.}, notes = {Also known as \cite{2605681} Distributed at GECCO-2014.}, } @inproceedings{Buzhinsky:2014:GECCOcomp, author = {Igor Buzhinsky and Daniil Chivilikhin and Vladimir Ulyantsev and Fedor Tsarev}, title = {Improving the quality of supervised finite-state machine construction using real-valued variables}, booktitle = {GECCO 2014 student workshop}, year = {2014}, editor = {Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1037--1040}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605679}, doi = {doi:10.1145/2598394.2605679}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The use of finite-state machines (FSMs) is a reliable choice for control system design since they can be formally verified. In this paper a problem of constructing FSMs with real-valued input and control parameters is considered. It is supposed that a set of human-created behaviour examples, or tests, is available. One of the earlier approaches for solving the problem suggested using genetic algorithms together with a transition labelling algorithm. This paper improves this approach via the use of real-valued variables which are calculated using the FSM's input data. FSMs with real-valued variables are represented as systems of linear controllers. The new approach allows to synthesise FSMs of better quality than it was possible earlier.}, notes = {Also known as \cite{2605679} Distributed at GECCO-2014.}, } @inproceedings{Chennupati:2014:GECCOcompa, author = {Gopinath Chennupati and R. Muhammad Atif Azad and Conor Ryan}, title = {Multi-core GE: automatic evolution of CPU based multi-core parallel programs}, booktitle = {GECCO 2014 student workshop}, year = {2014}, editor = {Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-2881-4}, keywords = {genetic algorithms, genetic programming, grammatical evolution}, pages = {1041--1044}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605670}, doi = {doi:10.1145/2598394.2605670}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We describe the use of on-chip multiple CPU architectures to automatically evolve parallel computer programs. These programs have the capability of exploiting the computational efficiency of the modern multi-core machines. This is significantly different from other parallel EC approaches because not only do we produce individuals that, in their final form, can exploit parallel architectures, we can also exploit the same parallel architecture during evolution to reduce evolution time. We use Grammatical Evolution along with OpenMP specific grammars to produce natively parallel code, and demonstrate that not only do we enjoy the benefit of final individuals that can run in parallel, but that our system scales effectively with the number of cores.}, notes = {Also known as \cite{2605670} Distributed at GECCO-2014.}, } @inproceedings{Gupta:2014:GECCOcompa, author = {Shikha Gupta and Naveen Kumar}, title = {Parameter tuning in quantum-inspired evolutionary algorithms for partitioning complex networks}, booktitle = {GECCO 2014 student workshop}, year = {2014}, editor = {Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1045--1048}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605672}, doi = {doi:10.1145/2598394.2605672}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We propose a numeric variant of quantum-inspired evolutionary algorithm (QIEA) where gene in the quantum chromosome is a superposition of k qubits, thus allowing the genes of the classical chromosome to take numeric values. We also present a modified form of real observation QIEA. Both these techniques are applied to the problem of partitioning a complex network. The algorithm parameters are tuned using an evolutionary bilevel search optimisation technique.}, notes = {Also known as \cite{2605672} Distributed at GECCO-2014.}, } @inproceedings{Khazanov:2014:GECCOcomp, author = {Mark Khazanov and Julian Jocque and John Rieffel}, title = {Developing morphological computation in tensegrity robots for controllable actuation}, booktitle = {GECCO 2014 student workshop}, year = {2014}, editor = {Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1049--1052}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605680}, doi = {doi:10.1145/2598394.2605680}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Conventionally control can be achieved by attempting to simplify complex dynamics. The field of morphological computation explores how mechanical complexity can be advantageous. In this paper we demonstrate morphological computation in tensegrity robots. We present a novel approach to tensegrity actuation and explore the capabilities of our self-evolving system. Methods of finding desirable gaits through both hand selection and evolution are described and the effectiveness of the system is demonstrated by our robot's ability to pursue a moving target. We conclude with a discussion of a bootstrapped system with the potential of significantly reducing evolution time and need for user presence.}, notes = {Also known as \cite{2605680} Distributed at GECCO-2014.}, } @inproceedings{Liu:2014:GECCOcompb, author = {Yiping Liu and Dunwei Gong and Xiaoyan Sun and Yong Zhang}, title = {A reference points-based evolutionary algorithm for many-objective optimization}, booktitle = {GECCO 2014 student workshop}, year = {2014}, editor = {Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1053--1056}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605674}, doi = {doi:10.1145/2598394.2605674}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Many-objective optimisation problems are common in real-world applications, few evolutionary optimisation methods, however, are suitable for them up to date due to their difficulties. We proposed a reference points-based evolutionary algorithm (RPEA) to solve many-objective optimization problems in this study. In RPEA, a series of reference points with good performances in convergence and distribution are generated according to the current population to guide the evolution. Furthermore, superior individuals are selected based on the assessment of each individual by calculating the distances between the reference points and the individual in the objective space. The algorithm was applied to four benchmark optimization problems and compared with NSGA-II and HypE. The results experimentally demonstrate that the algorithm is strengthened in obtaining Pareto optimal set with high performances.}, notes = {Also known as \cite{2605674} Distributed at GECCO-2014.}, } @inproceedings{Rokita:2014:GECCOcomp, author = {\lukasz Rokita and Przemys\Law Ogrodowski}, title = {Particles types in a swarm: searching for efficiency}, booktitle = {GECCO 2014 student workshop}, year = {2014}, editor = {Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1057--1060}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605682}, doi = {doi:10.1145/2598394.2605682}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This research is devoted analysis of convergence in particle swarm where each of the particles can represent different dynamic behaviour in search space.}, notes = {Also known as \cite{2605682} Distributed at GECCO-2014.}, } @inproceedings{Shorten:2014:GECCOcomp, author = {David Shorten and Geoff Nitschke}, title = {Flood evolution: changing the evolutionary substrate from a path of stepping stones to a field of rocks}, booktitle = {GECCO 2014 student workshop}, year = {2014}, editor = {Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1061--1064}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605675}, doi = {doi:10.1145/2598394.2605675}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We present ongoing research that is an extension of novelty search, flood evolution. This technique aims to improve evolutionary algorithms by presenting them with large sets of problems, as opposed to individual ones. If the older approach of incremental evolution were analogous to moving over a path of stepping stones, then this approach is similar to navigating a rocky field. The method is discussed and preliminary results are presented.}, notes = {Also known as \cite{2605675} Distributed at GECCO-2014.}, } @inproceedings{Solum:2014:GECCOcomp, author = {Timothy Solum and Brent E. Eskridge and Ingo Schlupp}, title = {Consensus costs and conflict in robot swarms}, booktitle = {GECCO 2014 student workshop}, year = {2014}, editor = {Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1065--1068}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605683}, doi = {doi:10.1145/2598394.2605683}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {It is commonly observed that aggregation in nature provides significant benefits to the group members. However, to reach a consensus individual preferences are frequently lost. Conflict is generally avoided because of the negative influence it could have on the success of collective movements. However, it could be used to balance consensus costs with individual preferences. Using a biologically-based collective movement model, this work investigates the possibility of conflict in a group movement allowing for differing individual goals to be accomplished, while still maintaining group cohesion much of the time. Individuals focus on their own needs, which may include the protection of being a part of a group or the desire to move away from the group and towards its preferred destination. Results show that by allowing conflict in group decision-making, consensus costs were balanced with individual preferences in such a way that group level success still occurred, while significantly improving the success of differing goals.}, notes = {Also known as \cite{2605683} Distributed at GECCO-2014.}, } @inproceedings{Suzuki:2014:GECCOcomp, author = {Masaki Suzuki and Taro Matsumaru and Setsuo Tsuruta and Rainer Knauf and Takaaki Motomura and Yoshitaka Sakurai}, title = {A case based approach for an intelligent route optimization technology}, booktitle = {GECCO 2014 student workshop}, year = {2014}, editor = {Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1069--1072}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605676}, doi = {doi:10.1145/2598394.2605676}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The paper introduces a Case Based Approximation method to solve large scale Travelling Salesman Problems in a short time with a low error rate. It is useful for domains with most solutions being similar to solutions that have been created. Thus, a solution can be derived by (1) selecting a most similar TSP from a library of former TSP solutions, (2) removing the locations that are not part of the current TSP and (3) adding the missing locations of the current TSP by mutation, namely Nearest Insertion (NI). This way of creating solutions by Case Based Reasoning (CBR) avoids the computational costs to create new solutions from scratch.}, notes = {Also known as \cite{2605676} Distributed at GECCO-2014.}, } @inproceedings{Yang:2014:GECCOcompa, author = {Hongjun Yang and Yixu Song and Lihua Wang and Peifa Jia}, title = {A niching cumulative genetic algorithm with evaluated probability for multimodal optimization}, booktitle = {GECCO 2014 student workshop}, year = {2014}, editor = {Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1073--1076}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605677}, doi = {doi:10.1145/2598394.2605677}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Multi-modal problems with multiple local/global optima are ubiquitous in real-world application. Many multi-modal optimisation algorithms have been developed to search as many local/global optima as possible. However, to locate and maintain many optima simultaneously, both the search quality and efficiency of these algorithms may be influenced. Here, we propose a new niching genetic algorithm that attempts to improve both the search quality and efficiency. To the end, we incorporate two mechanisms into the algorithm: cumulative population technique and an evaluated probability of new individuals. The first mechanism is designed to keep the found solutions by storing all known information, whilst the second is responsible for exploiting unexplored space effectively by guiding the exploration process. The proposed approach is compared with five different niche genetic algorithms on six well known multimodal functions of different characteristics. Empirical results indicate that the proposed approach outperforms other algorithms. It not only increases the probability of finding both global and local optima, but also reduces the average number of function evaluations.}, notes = {Also known as \cite{2605677} Distributed at GECCO-2014.}, } @inproceedings{Zhan:2014:GECCOcomp, author = {Haoxi Zhan}, title = {A quantitative analysis of the simplification genetic operator}, booktitle = {GECCO 2014 student workshop}, year = {2014}, editor = {Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-2881-4}, keywords = {genetic algorithms, genetic programming}, pages = {1077--1080}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605684}, doi = {doi:10.1145/2598394.2605684}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The simplification function was introduced to PushGP as a tool to reduce the sizes of evolved programs in final reports. While previous work suggests that simplification could reduce the sizes significantly, nothing has been done to study its impacts on the evolution of Push programs. In this paper, we show the impact of simplification as a genetic operator. By conducting test runs on the U.S. change problem, we show that using simplification operator with PushGP, lexicase selection and ULTRA could increase the possibility to find solutions in the short term while it might remove some useful genetic materials for the long term.}, notes = {Also known as \cite{2605684} Distributed at GECCO-2014.}, } @inproceedings{Craven:2014:GECCOcomp, author = {Matthew J. Craven and Henri C. Jimbo}, title = {EA stability visualization: perturbations, metrics and performance}, booktitle = {GECCO 2014 VizGEC: Workshop on visualisation in genetic and evolutionary computation}, year = {2014}, editor = {David Walker and Richard Everson and Jonathan Fieldsend}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1083--1090}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2610549}, doi = {doi:10.1145/2598394.2610549}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {It is well-known that Evolutionary Algorithms (EAs) are sensitive to changes in their control parameters, and it is generally agreed that too large a change may turn the EA from being successful to unsuccessful. This work reports on an experimental hybrid visualisation scheme for the determination of EA stability according to perturbation of EA parameters. The scheme gives a visual representation of local neighbourhoods of the parameter space according to a choice of two perturbation metrics, relating perturbations to EA performance as a variant of Kolmogorov distance. Through visualization and analysis of twelve thousand case study EA runs, we illustrate that we are able to distinguish between EA stability and instability depending upon perturbation and performance metrics. Finally we use what we have learnt in the case study to provide a methodology for more general EAs.}, notes = {Also known as \cite{2610549} Distributed at GECCO-2014.}, } @inproceedings{Freitas:2014:GECCOcomp, author = {Alan R.R. Freitas and Rodrigo C.P. Silva and Frederico G. Guimar\,{a}es}, title = {On the visualization of trade-offs and reducibility in many-objective optimization}, booktitle = {GECCO 2014 VizGEC: Workshop on visualisation in genetic and evolutionary computation}, year = {2014}, editor = {David Walker and Richard Everson and Jonathan Fieldsend}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1091--1098}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2610550}, doi = {doi:10.1145/2598394.2610550}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper proposes a technique of Aggregation Trees to visualise the results of high-dimensional multiobjective optimisation problems, or many-objective problems. The high-dimensionality makes it difficult to represent the relation between objectives and solutions. Most approaches in the literature are based on the representation of solutions in lower dimensions. The technique of Aggregation Trees proposed here is based on iterative aggregation of objectives which are represented in a tree. Besides, the location of conflict is also calculated and represented on the tree. Thus, the tree can represent which objectives and groups of objectives are harmonic the most, what sort of conflict is present between groups of objectives, and which aggregations would be more interesting in order to reduce the problem dimension.}, notes = {Also known as \cite{2610550} Distributed at GECCO-2014.}, } @inproceedings{Tuvsar:2014:GECCOcomp, author = {Tea Tu\v{s}ar and Bogdan Filipi\v{c}}, title = {Initial experiments in visualization of empirical attainment function differences using maximum intensity projection}, booktitle = {GECCO 2014 VizGEC: Workshop on visualisation in genetic and evolutionary computation}, year = {2014}, editor = {David Walker and Richard Everson and Jonathan Fieldsend}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1099--1106}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2610548}, doi = {doi:10.1145/2598394.2610548}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In multiobjective optimisation, the Empirical Attainment Function (EAF) can be used to determine which areas of the objective space are attained by an optimisation algorithm. If two algorithms are to be compared, differences in EAF values show which areas of the objective space are more often attained by one of the algorithms. While the visualisation of EAF values and differences is rather straightforward in 2D, the 3D case presents a great challenge as we need to visualize a large number of 3D cuboids. This paper presents a method for computing the cuboids with constant EAF values and reports on initial experiments using Maximum Intensity Projection, a very-well known volume rendering technique used in medicine.}, notes = {Also known as \cite{2610548} Distributed at GECCO-2014.}, } @inproceedings{Beham:2014:GECCOcomp, author = {Andreas Beham and Johannes Karder and Gabriel Kronberger and Stefan Wagner and Michael Kommenda and Andreas Scheibenpflug}, title = {Scripting and framework integration in heuristic optimization environments}, booktitle = {GECCO 2014 Workshop on Evolutionary Computation Software Systems (EvoSoft)}, year = {2014}, editor = {Stefan Wagner and Michael Affenzeller}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1109--1116}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605690}, doi = {doi:10.1145/2598394.2605690}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Rapid prototyping and testing of new ideas has been a major argument for evolutionary computation frameworks. These frameworks facilitate the application of evolutionary computation and allow experimenting with new and modified algorithms and problems by building on existing, well tested code. However, one could argue, that despite the many frameworks of the metaheuristics community, software packages such as MATLAB, GNU Octave, Scilab, or RStudio are quite popular. These software packages however are associated more closely with numerical analysis rather than evolutionary computation. In contrast to typical evolutionary computation frameworks which provide standard implementations of algorithms and problems, these popular frameworks provide a direct programming environment for the user and several helpful functions and mathematical operations. The user does not need to use traditional development tools such as a compiler or linker, but can implement, execute, and visualise his ideas directly within the environment. HeuristicLab has become a popular environment for heuristic optimisation over the years, but has not been recognised as a programming environment so far. In this article we will describe new scripting capabilities created in HeuristicLab and give information on technical details of the implementation. Additionally, we show how the programming interface can be used to integrate further metaheuristic optimization frameworks in HeuristicLab. }, notes = {Also known as \cite{2605690} Distributed at GECCO-2014.}, } @inproceedings{Brookhouse:2014:GECCOcomp, author = {James Brookhouse and Fernando E.B. Otero and Michael Kampouridis}, title = {Working with OpenCL to speed up a genetic programming financial forecasting algorithm: initial results}, booktitle = {GECCO 2014 Workshop on Evolutionary Computation Software Systems (EvoSoft)}, year = {2014}, editor = {Stefan Wagner and Michael Affenzeller}, isbn13 = {978-1-4503-2881-4}, keywords = {genetic algorithms, genetic programming}, pages = {1117--1124}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605689}, doi = {doi:10.1145/2598394.2605689}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The genetic programming tool EDDIE has been shown to be a successful financial forecasting tool, however it has suffered from an increase in execution time as new features have been added. Speed is an important aspect in financial problems, especially in the field of algorithmic trading, where a delay in taking a decision could cost millions. To offset this performance loss, EDDIE has been modified to take advantage of multi-core CPUs and dedicated GPUs. This has been achieved by modifying the candidate solution evaluation to use an OpenCL kernel, allowing the parallel evaluation of solutions. Our computational results have shown improvements in the running time of EDDIE when the evaluation was delegated to the OpenCL kernel running on a multi-core CPU, with speed ups up to 21 times faster than the original EDDIE algorithm. While most previous works in the literature reported significantly improvements in performance when running an OpenCL kernel on a GPU device, we did not observe this in our results. Further investigation revealed that memory copying overheads and branching code in the kernel are potentially causes of the (under-)performance of the OpenCL kernel when running on the GPU device.}, notes = {Also known as \cite{2605689} Distributed at GECCO-2014.}, } @inproceedings{Etemaadi:2014:GECCOcomp, author = {Ramin Etemaadi and Michel R.V. Chaudron}, title = {Distributed optimization on super computers: case study on software architecture optimization framework}, booktitle = {GECCO 2014 Workshop on Evolutionary Computation Software Systems (EvoSoft)}, year = {2014}, editor = {Stefan Wagner and Michael Affenzeller}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1125--1132}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605686}, doi = {doi:10.1145/2598394.2605686}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Nowadays advanced software systems need to satisfy large number of quality attributes at the same time. It is a very complex optimisation problem which software architects must address. Evolutionary algorithms can help architects to find optimal solutions which meet these conflicting quality attributes. However, these metaheuristic approaches in multiobjective problems especially for high dimensions mostly take so long time to be executed. One of the best solutions to speed up this process is distributing execution of evolutionary algorithm on multiple nodes of a super computer or on the cloud. This paper presents the results of distributed execution of evolutionary algorithm for multiobjective optimisation of software architecture. We report implementation of two different ways for distributed execution of evolutionary algorithm: (1) Actor-based approach, (2) MapReduce approach. The case study in this experiment is an industrial software system which is derived from a real world automotive embedded system. The results of this computationally-intensive experiment on a super computer give us 81.27percent parallelization efficiency for distribution among 5 nodes.}, notes = {Also known as \cite{2605686} Distributed at GECCO-2014.}, } @inproceedings{Brownlee:2014:GECCOcomp, author = {Alexander E.I. Brownlee and Jerry Swan and Ender \"{O}zcan and Andrew J. Parkes}, title = {Hyperion2: a toolkit for {meta-, hyper-} heuristic research}, booktitle = {GECCO 2014 Workshop on Evolutionary Computation Software Systems (EvoSoft)}, year = {2014}, editor = {Stefan Wagner and Michael Affenzeller}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1133--1140}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605687}, doi = {doi:10.1145/2598394.2605687}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In order for appropriate meta-heuristics to be chosen and tuned for specific problems, it is critical that we better understand the problems themselves and how algorithms solve them. This is particularly important as we seek to automate the process of choosing and tuning algorithms and their operators via hyper-heuristics. If meta-heuristics are viewed as sampling algorithms, they can be classified by the trajectory taken through the search space. We term this trajectory a trace. In this paper, we present Hyperion2, a Java framework for meta- and hyper- heuristics which allows the analysis of the trace taken by an algorithm and its constituent components through the search space. Built with the principles of interoperability, generality and efficiency, we intend that this framework will be a useful aid to scientific research in this domain.}, notes = {Also known as \cite{2605687} Distributed at GECCO-2014.}, } @inproceedings{Cirillo:2014:GECCOcomp, author = {Simone Cirillo and Stefan Lloyd}, title = {A scalable symbolic expression tree interpreter for the heuristiclab optimization framework}, booktitle = {GECCO 2014 Workshop on Evolutionary Computation Software Systems (EvoSoft)}, year = {2014}, editor = {Stefan Wagner and Michael Affenzeller}, isbn13 = {978-1-4503-2881-4}, keywords = {genetic algorithms, genetic programming}, pages = {1141--1148}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605692}, doi = {doi:10.1145/2598394.2605692}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper we describe a novel implementation of the Interpreter class for the metaheuristic optimisation framework HeuristicLab, comparing it with the three existing interpreters provided with the framework. The Interpreter class is an internal software component used by HeuristicLab for the evaluation of the symbolic expression trees on which its Genetic Programming (GP) implementation relies. The proposed implementation is based on the creation and compilation of a .NET Expression Tree. We also analyse the Interpreters' performance, evaluating the algorithm execution times on GP Symbolic Regression problems for different run settings. Our implementation results to be the fastest on all evaluations, with comparatively better performance the larger the run population size, dataset length and tree size are, increasing HeuristicLab's computational efficiency for large problem setups.}, notes = {Also known as \cite{2605692} Distributed at GECCO-2014.}, } @inproceedings{Leroux:2014:GECCOcomp, author = {Claris Leroux and Fernando E.B. Otero and Colin G. Johnson}, title = {A genetic programming problem definition language code generator for the epochX framework}, booktitle = {GECCO 2014 Workshop on Evolutionary Computation Software Systems (EvoSoft)}, year = {2014}, editor = {Stefan Wagner and Michael Affenzeller}, isbn13 = {978-1-4503-2881-4}, keywords = {genetic algorithms, genetic programming}, pages = {1149--1154}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605691}, doi = {doi:10.1145/2598394.2605691}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {There are many different genetic programming (GP) frameworks that can be used to implement algorithms to solve a particular optimisation problem. In order to use a framework, users need to become familiar with a large numbers of source code before actually implementing the algorithm, adding a learning overhead. In some cases, this can prevent users from trying out different frameworks. This paper discusses the implementation of a code generator in the EpochX framework to facilitate the implementation of GP algorithms. The code generator is based on the GP definition language (GPDL), which is a framework-independent language that can be used to specify GP problems.}, notes = {Also known as \cite{2605691} Distributed at GECCO-2014.}, } @inproceedings{Merelo:2014:GECCOcompa, author = {Juan-Juli\'{a}n Merelo and Pedro Castillo and Antonio Mora and Anna Esparcia-Alc\'{a}zar and V\'{\i}ctor Rivas-Santos}, title = {NodEO, a multi-paradigm distributed evolutionary algorithm platform in JavaScript}, booktitle = {GECCO 2014 Workshop on Evolutionary Computation Software Systems (EvoSoft)}, year = {2014}, editor = {Stefan Wagner and Michael Affenzeller}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1155--1162}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605688}, doi = {doi:10.1145/2598394.2605688}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {After more than fifteen years, JavaScript has finally risen as a popular language for implementing all kind of applications, from server-based to rich Internet applications. The fact that it is implemented in the browser and in server-side tools makes it interesting for designing evolutionary algorithm frameworks that encompass both tiers, but besides, they allow a change in paradigm that goes beyond the canonical evolutionary algorithm. In this paper we will experiment with different architectures, client-server and peer to peer to assess which ones offer most advantages in terms of performance, scalability and ease of use for the computer scientist. All implementations have been released as open source, and besides showing that the concept of working with evolutionary algorithms in JavaScript can be done efficiently, we prove that a master-slave parallel architecture offers the best combination of time and algorithmic improvements in a parallel evolutionary algorithm that leverages JavaScript implementation features.}, notes = {Also known as \cite{2605688} Distributed at GECCO-2014.}, } @inproceedings{Cai:2014:GECCOcomp, author = {Xinye Cai and Xin Cheng and Zhun Fan}, title = {A probabilistic pareto local search based on historical success counting for multiobjective optimization}, booktitle = {GECCO 2014 evolutionary synthesis of dynamical systems}, year = {2014}, editor = {Zhun Fan and Yaochu Jin and Hod Lipson and Erik Goodman}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1163--1168}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2610011}, doi = {doi:10.1145/2598394.2610011}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper, we propose a multiobjective probabilistic Pareto local search to address combinatorial optimization problems (COPs). The probability is determined by the success counts of local search offspring entering an external domination archive and this probabilistic information is used to further guide the selection of promising solutions for Pareto local search. In addition, simulated annealing is integrated in this framework as the local refinement process. This multiobjective probabilistic Pareto local search algorithm (MOPPLS), is tested on two famous COPs and compared with some well-known multiobjective evolutionary algorithms. Experimental results suggest that MOPPLS outperforms other compared algorithms.}, notes = {Also known as \cite{2610011} Distributed at GECCO-2014.}, } @inproceedings{Fan:2014:GECCOcomp, author = {Zhun Fan and Xinye Cai and Wenji Li and Huibiao Lin and Shuxiang Xie and Sheng Wang}, title = {Evolutionary synthesis of dynamical systems: the past, current, and future}, booktitle = {GECCO 2014 evolutionary synthesis of dynamical systems}, year = {2014}, editor = {Zhun Fan and Yaochu Jin and Hod Lipson and Erik Goodman}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1169--1174}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2610009}, doi = {doi:10.1145/2598394.2610009}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {When Electronic Design Automation (EDA) has achieved great success both in academy and industry, design automation for mechanical systems seems to be lagged behind. One underlying reason for this is that the coupling of subsystems of mechanical systems is very strong, whereas this coupling for digital electronic system is usually much weaker. Or in other words, the modularity of electronic systems, especially digital electronic systems is much stronger. On the other hand, the mechatronic systems are becoming more and more modularised, which makes them more amenable to be designed automatically, just as digital electronic systems do. In this sense, Mechanic Design Automation (MDA) is very likely to become the next wave after EDA both in academy and industry. In this paper, we give a survey of the topic of evolutionary synthesis of dynamical systems in general, and wish to shed some light on the future development of this subject.}, notes = {Also known as \cite{2610009} Distributed at GECCO-2014.}, } @inproceedings{Li:2014:GECCOcompb, author = {Wenji Li and Zhun Fan and Xinye Cai and Huibiao Lin and Shuxiang Xie and Sheng Wang}, title = {Design optimization of MEMS using constrained multi-objective evolutionary algorithm}, booktitle = {GECCO 2014 evolutionary synthesis of dynamical systems}, year = {2014}, editor = {Zhun Fan and Yaochu Jin and Hod Lipson and Erik Goodman}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1175--1180}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2610010}, doi = {doi:10.1145/2598394.2610010}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {MEMS layout optimization is a typical multi-objective constrained optimization problem. This paper proposes an improved MOEA called cMOEA/D to solve this problem. The cMOEA/D is based on MOEA/D but also uses the frequency of individual update of sub-problems to locate the promising sub-problems. By dynamically allocating computing resources to more promising sub-problems, we can effectively improve the performance of the algorithm to find more non-dominated solutions in MEMS layout optimization. In addition, we compared two mechanisms of constraint handling, Stochastic Ranking (SR) and Constraint-domination principle (CDP). The experimental results show that CDP works better than SR and the proposed algorithm outperforms the state-of-art algorithms such as NSGA-II and MOEA/D, in terms of convergence and diversity.}, notes = {Also known as \cite{2610010} Distributed at GECCO-2014.}, } @inproceedings{Yang:2014:GECCOcompb, author = {Zhixiang Yang and Xinye Cai and Zhun Fan}, title = {Epsilon constrained method for constrained multiobjective optimization problems: some preliminary results}, booktitle = {GECCO 2014 evolutionary synthesis of dynamical systems}, year = {2014}, editor = {Zhun Fan and Yaochu Jin and Hod Lipson and Erik Goodman}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1181--1186}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2610012}, doi = {doi:10.1145/2598394.2610012}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper, the Epsilon constrained method and Adaptive operator selection (AOS) are used in Multiobjective evolutionary algorithm based on decomposition (MOEA/D). The epsilon constrained method is an algorithm transformation method, which can convert algorithms for unconstrained problems to algorithms for constrained problems using the epsilon level comparison, which compares search points based on the pair of objective value and constraint violation of them. AOS is used to determine the application rates of different operators in an online manner based on their recent performances within an optimization process. The experimental results show our proposed approach for multiobjective constrained optimization is very competitive compared with other state-of-art algorithms.}, notes = {Also known as \cite{2610012} Distributed at GECCO-2014.}, } @inproceedings{Luong:2014:GECCOcomp, author = {Ngoc Hoang Luong and Marinus O.W. Grond and Han {La Poutr\'{e}} and Peter A.N. Bosman}, title = {Efficiency enhancements for evolutionary capacity planning in distribution grids}, booktitle = {GECCO 2014 Workshop on Green and Efficient Energy Applications of Genetic and Evolutionary Computation (GreenGEC)}, year = {2014}, editor = {Alexandru-Adrian Tantar and Emilia Tantar and Peter A. N. Bosman}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1189--1196}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605696}, doi = {doi:10.1145/2598394.2605696}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper, we tackle the distribution network expansion planning (DNEP) problem by employing two evolutionary algorithms (EAs): the classical Genetic Algorithm (GA) and a linkage-learning EA, specifically a Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA). We furthermore develop two efficiency-enhancement techniques for these two EAs for solving the DNEP problem: a restricted initialisation mechanism to reduce the size of the explorable search space and a means to filter linkages (for GOMEA) to disregard linkage groups during genetic variation that are likely not useful. Experimental results on a benchmark network show that if we may assume that the optimal network will be very similar to the starting network, restricted initialization is generally useful for solving DNEP and moreover it becomes more beneficial to use the simple GA. However, in the more general setting where we cannot make the closeness assumption and the explorable search space becomes much larger, GOMEA outperforms the classical GA.}, notes = {Also known as \cite{2605696} Distributed at GECCO-2014.}, } @inproceedings{MeseguerLlopis:2014:GECCOcomp, author = {Joan {Meseguer Llopis} and \Lukasz Rajewski and S\lawomir Kuklinski}, title = {Reinforcement learning based energy efficient LTE RAN}, booktitle = {GECCO 2014 Workshop on Green and Efficient Energy Applications of Genetic and Evolutionary Computation (GreenGEC)}, year = {2014}, editor = {Alexandru-Adrian Tantar and Emilia Tantar and Peter A. N. Bosman}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1197--1204}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605694}, doi = {doi:10.1145/2598394.2605694}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Reducing power consumption in LTE networks has become an important issue for mobile network operators. The 3GPP organisation has included such operation as one of SON (Self-Organising Networks) functions [1][2]. Using the approach presented in this paper the decision about turning Radio Access Network (RAN) nodes off and on, according to the network load (which is typically low at night), is taken into account. The process is controlled using a combination of Fuzzy Logic and Q-Learning techniques (FQL). The effectiveness of the proposed approach has been evaluated using the LTE-Sim simulator with some extensions. The simulations are very close to real network implementation: we used the RAN node parameters that are defined by 3GPP and simulations take into account the network behaviour in the micro time scale.}, notes = {Also known as \cite{2605694} Distributed at GECCO-2014.}, } @inproceedings{Ren:2014:GECCOcompa, author = {Yi Ren and Junichi Suzuki and Chonho Lee and Athanasios V. Vasilakos and Shingo Omura and Katsuya Oba}, title = {Balancing performance, resource efficiency and energy efficiency for virtual machine deployment in DVFS-enabled clouds: an evolutionary game theoretic approach}, booktitle = {GECCO 2014 Workshop on Green and Efficient Energy Applications of Genetic and Evolutionary Computation (GreenGEC)}, year = {2014}, editor = {Alexandru-Adrian Tantar and Emilia Tantar and Peter A. N. Bosman}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1205--1212}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605693}, doi = {doi:10.1145/2598394.2605693}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper proposes and evaluates a multiobjective evolutionary game theoretic framework for adaptive and stable application deployment in clouds that support dynamic voltage and frequency scaling (DVFS) for CPUs. The proposed framework, called Cielo, aids cloud operators to adapt the resource allocation to applications and their locations according to the operational conditions in a cloud (e.g., workload and resource availability) with respect to multiple conflicting objectives such as response time performance, resource and power consumption. Moreover, Cielo theoretically guarantees that each application performs an evolutionarily stable deployment strategy, which is an equilibrium solution under given operational conditions. Simulation results verify this theoretical analysis; applications seek equilibria to perform adaptive and evolutionarily stable deployment strategies. Cielo allows applications to successfully leverage DVFS to balance their response time performance, resource and power consumption.}, notes = {Also known as \cite{2605693} Distributed at GECCO-2014.}, } @inproceedings{Tantar:2014:GECCOcomp, author = {Alexandru-Adrian Tantar and Emilia Tantar}, title = {A survey on sustainability in ICT: a computing perspective}, booktitle = {GECCO 2014 Workshop on Green and Efficient Energy Applications of Genetic and Evolutionary Computation (GreenGEC)}, year = {2014}, editor = {Alexandru-Adrian Tantar and Emilia Tantar and Peter A. N. Bosman}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1213--1220}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605695}, doi = {doi:10.1145/2598394.2605695}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The rise of the data centres industry, together with the emergence of large cloud computing that require large quantities of resources to be maintained, brought the need of providing a sustainable development process. Through this paper we aim to provide an introductory insight on the status and tools available to tackle this perspective within the evolutionary and genetic algorithms community. Existing advancements are also emphasised and perspectives outlined.}, notes = {Also known as \cite{2605695} Distributed at GECCO-2014.}, } @inproceedings{Jauhri:2014:GECCOcomp, author = {Abhinav Jauhri and Jason D. Lohn and Derek S. Linden}, title = {A comparison of antenna placement algorithms}, booktitle = {GECCO 2014 Workshop on Problem Understanding and Real-world Optimisation (PURO)}, year = {2014}, editor = {Kent McClymont and Kevin Sim and Gabriela Ochoa and Ed Keedwell}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1223--1230}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605446}, doi = {doi:10.1145/2598394.2605446}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Co-location of multiple antenna systems on a single fixed or mobile platform can be challenging due to a variety of factors, such as mutual coupling, individual antenna constraints, multipath, obstructions, and parasitic effects due to the platform. The situation frequently arises where a new communication capability, and hence antenna system, is needed on an existing platform. The problem of placing new antennas requires a long, manual effort in order to complete an antenna placement study. An automated procedure for determining such placements would not only save time, but would be able to optimise the performance of all co-located antenna systems. In this paper we examine a set of stochastic algorithms to determine their effectiveness at finding optimal placements for multiple antennas on a platform. To our knowledge, this is the first study to investigate optimising multiple antenna placement on a single platform using multiple stochastic algorithms. Of the four algorithms studied, simulated annealing and evolutionary strategy were found to be most effective in finding optimal placements.}, notes = {Also known as \cite{2605446} Distributed at GECCO-2014.}, } @inproceedings{Yang:2014:GECCOcompc, author = {Chao Yang and Shuming Peng and Bin Jiang and Lei Wang and Renfa Li}, title = {Hyper-heuristic genetic algorithm for solving frequency assignment problem in TD-SCDMA}, booktitle = {GECCO 2014 Workshop on Problem Understanding and Real-world Optimisation (PURO)}, year = {2014}, editor = {Kent McClymont and Kevin Sim and Gabriela Ochoa and Ed Keedwell}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1231--1238}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605445}, doi = {doi:10.1145/2598394.2605445}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper studies the frequency assignment problem (FAP) in TD-SCDMA network of mobile communications industry in China. The problem considers finding the optimal frequency allocation scheme for carriers with a limited frequency resource, such that the entire network interference is minimised. Besides, the allocation of frequencies needs to satisfy some constraints to avoid the effect of call interference within the same cell or adjacent cell. Given the formula for calculation of the network interference, we take the FAP as a constrained optimization problem and use a hyper-heuristic genetic algorithm (HHGA) to optimise the assignment of frequencies. We first define six low-level heuristics (LLHs) search strategies based on the computation of interference, and then use genetic algorithm (GA) at a high-level to find the best combination sequence of LLH strategies to reduce interferences of the overall network. GA uses two-point crossover, uniform mutation, and Minimal Generation Gap (MGG) as the generation alternation model. In order to speed up the search, we define a Tabu table to avoid repeat search of LLHs. Compared with scatter search as one of the meta-heuristic algorithm with best performance, our experimental results on real data sets of TD-SCDMA network have shown better result.}, notes = {Also known as \cite{2605445} Distributed at GECCO-2014.}, } @inproceedings{Hart:2014:GECCOcomp, author = {Emma Hart and Kevin Sim and Neil Urquhart}, title = {A real-world employee scheduling and routing application}, booktitle = {GECCO 2014 Workshop on Problem Understanding and Real-world Optimisation (PURO)}, year = {2014}, editor = {Kent McClymont and Kevin Sim and Gabriela Ochoa and Ed Keedwell}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1239--1242}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605447}, doi = {doi:10.1145/2598394.2605447}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We describe a hyper-heuristic application developed for a client to find quick, acceptable solutions to Workforce Scheduling and Routing problems. An interactive fitness function controlled by the user enables five different objectives to be weighted according to client preference. The application uses a real road network in order to calculate driving distances between locations, and is designed to integrate with a web-based application to access employee calendars.}, notes = {Also known as \cite{2605447} Distributed at GECCO-2014.}, } @inproceedings{Mora:2014:GECCOcomp, author = {Antonio M. Mora and Paloma {De las Cuevas} and Juan Juli\'{a}n Merelo and Sergio Zamarripa and Anna I. Esparcia-Alc\'{a}zar}, title = {Enforcing corporate security policies via computational intelligence techniques}, booktitle = {GECCO 2014 Workshop on genetic and evolutionary computation in defense, security and risk management (SecDef)}, year = {2014}, editor = {Anna I Esparcia-Alcazar and Frank W. Moore}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1245--1252}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605438}, doi = {doi:10.1145/2598394.2605438}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper presents an approach, based in a project in development, which combines Data Mining, Machine Learning and Computational Intelligence techniques, in order to create a user-centric and adaptable corporate security system. Thus, the system, named MUSES, will be able to analyse the user's behaviour (modelled as events) when interacting with the company's server, accessing to corporate assets, for instance. As a result of this analysis, and after the application of the aforementioned techniques, the Corporate Security Policies, and specifically, the Corporate Security Rules will be adapted to deal with new anomalous situations, or to better manage user's behaviour. The work reviews the current state of the art in security issues resolution by means of these kind of methods. Then it describes the MUSES features in this respect and compares them with the existing approaches.}, notes = {Also known as \cite{2605438} Distributed at GECCO-2014.}, } @inproceedings{Haddadi:2014:GECCOcomp, author = {Fariba Haddadi and Dylan Runkel and A. Nur Zincir-Heywood and Malcolm I. Heywood}, title = {On botnet behaviour analysis using GP and C4.5}, booktitle = {GECCO 2014 Workshop on genetic and evolutionary computation in defense, security and risk management (SecDef)}, year = {2014}, editor = {Anna I Esparcia-Alcazar and Frank W. Moore}, isbn13 = {978-1-4503-2881-4}, keywords = {genetic algorithms, genetic programming}, pages = {1253--1260}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605435}, doi = {doi:10.1145/2598394.2605435}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Botnets represent a destructive cyber security threat that aim to hide their malicious activities within legitimate Internet traffic. Part of what makes botnets so affective is that they often upgrade themselves over time, hence reacting to improved detection mechanisms. In addition, Internet common communication protocols (i.e. HTTP) are used for the purposes of constructing subversive communication channels. This work employs machine learning algorithms (genetic programming and decision trees) to detect distinct behaviours in various botnets. That is to say, botnets mimic legitimate HTTP traffic while actually serving botnet purposes. To this end, two different feature sets are employed and analysed to see how differences between three botnets - Zeus, Conficker and Torpig - can be distinguished. Specific recommendations are then made regarding the utility of different feature sets and machine learning algorithms for detecting each type of botnet.}, notes = {Also known as \cite{2605435} Distributed at GECCO-2014.}, } @inproceedings{John:2014:GECCOcomp, author = {David J. John and Robert W. Smith and William H. Turkett and Daniel A. Ca\,{n}as and Errin W. Fulp}, title = {Evolutionary based moving target cyber defense}, booktitle = {GECCO 2014 Workshop on genetic and evolutionary computation in defense, security and risk management (SecDef)}, year = {2014}, editor = {Anna I Esparcia-Alcazar and Frank W. Moore}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1261--1268}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605437}, doi = {doi:10.1145/2598394.2605437}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {A Moving Target (MT) defence constantly changes a system's attack surface, in an attempt to limit the usefulness of the reconnaissance the attacker has collected. One approach to this defence strategy is to intermittently change a system's configuration. These changes must maintain functionality and security, while also being diverse. Finding suitable configuration changes that form a MT defence is challenging. There are potentially a large number of individual configurations' settings to consider, without a full understanding of the settings' interdependencies. Evolution-based algorithms, which formulate better solutions from good solutions, can be used to create a MT defense. New configurations are created based on the security of previous configurations and can be periodically implemented to change the system's attack surface. This approach not only has the ability to discover new, more secure configurations, but is also proactive and resilient since it can continually adapt to the current environment in a fashion similar to systems found in nature. This article presents and compares two genetic algorithms to create a MT defence. The primary difference between the two is based on their approaches to mutation. One mutates values, and the other modifies the domains from which values are chosen.}, notes = {Also known as \cite{2605437} Distributed at GECCO-2014.}, } @inproceedings{McCausland:2014:GECCOcomp, author = {Jamieson McCausland and Rami Abielmona and Rafael Falcon and Ana-Maria Cretu and Emil M. Petriu}, title = {On the role of multi-objective optimization in risk mitigation for critical infrastructures with robotic sensor networks}, booktitle = {GECCO 2014 Workshop on genetic and evolutionary computation in defense, security and risk management (SecDef)}, year = {2014}, editor = {Anna I Esparcia-Alcazar and Frank W. Moore}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1269--1276}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2605436}, doi = {doi:10.1145/2598394.2605436}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The use of robotic sensor networks (RSNs) for Territorial Security (TerrSec) applications has earned an increasing popularity in recent years. In Critical Infrastructure Protection (CIP) applications, the RSN goal is to provide the information needed to maintain a secure perimeter around the desired infrastructure and efficiently coordinate a corporate response to any event that arises in the monitored region. Such a response will only involve the most suitable robotic nodes and must successfully counter any detected vulnerability in the system. This paper is a preliminary study of the role played by multi-objective optimization (MOO) in the elicitation of responses from a risk-aware RSN that is deployed around a critical infrastructure. Contrary to previous studies showcasing a pre-optimization auctioning scheme, where the RSN nodes bid on the basis of their knowledge of the event, we introduce a post-optimization auctioning scheme in which the nodes place their bids knowing what their final positions along the perimeter will be, hence calling for a more informed decision at the node level. The impact of the pre- vs. post-optimization stage in a first-price sealed bid auction system over the risk mitigation strategies elicited by the RSN is evaluated and discussed. Empirical results reveal that the pre-optimization auctioning is more suitable for dense RSNs whereas the post-optimization one is preferred in sparse RSNs. To the best of our knowledge, this is the first attempt to assess the role of MOO in risk mitigation for CIP with RSNs.}, notes = {Also known as \cite{2605436} Distributed at GECCO-2014.}, } @inproceedings{Cerri:2014:GECCOcomp, author = {Ricardo Cerri and Rodrigo C. Barros and Alex A. Freitas and Andr\'{e} C.P.L.F. {de Carvalho}}, title = {Evolving relational hierarchical classification rules for predicting gene ontology-based protein functions}, booktitle = {GECCO 2014 Workshop on Evolutionary Computation for Big Data and Big Learning}, year = {2014}, editor = {Jaume Bacardit and Ignacio Arnaldo and Kalyan Veeramachaneni and Una-May O'Reilly}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1279--1286}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2611384}, doi = {doi:10.1145/2598394.2611384}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Hierarchical Multi-Label Classification (HMC) is a complex classification problem where instances can be classified into many classes simultaneously, and these classes are organised in a hierarchical structure, having subclasses and superclasses. In this paper, we investigate the HMC problem of assign functions to proteins, being each function represented by a class (term) in the Gene Ontology (GO) taxonomy. It is a very difficult task, since the GO taxonomy has thousands of classes. We propose a Genetic Algorithm (GA) to generate HMC rules able to classify a given protein in a set of GO terms, respecting the hierarchical constraints imposed by the GO taxonomy. The proposed GA evolves rules with propositional and relational tests. Experiments using ten protein function datasets showed the potential of the method when compared to other literature methods.}, notes = {Also known as \cite{2611384} Distributed at GECCO-2014.}, } @inproceedings{Vahdat:2014:GECCOcomp, author = {Ali Vahdat and Aaron Atwater and Andrew R. McIntyre and Malcolm I. Heywood}, title = {On the application of GP to streaming data classification tasks with label budgets}, booktitle = {GECCO 2014 Workshop on Evolutionary Computation for Big Data and Big Learning}, year = {2014}, editor = {Jaume Bacardit and Ignacio Arnaldo and Kalyan Veeramachaneni and Una-May O'Reilly}, isbn13 = {978-1-4503-2881-4}, keywords = {genetic algorithms, genetic programming}, pages = {1287--1294}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2611385}, doi = {doi:10.1145/2598394.2611385}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {A framework is introduced for applying GP to streaming data classification tasks under label budgets. This is a fundamental requirement if GP is going to adapt to the challenge of streaming data environments. The framework proposes three elements: a sampling policy, a data subset and a data archiving policy. The sampling policy establishes on what basis data is sampled from the stream, and therefore when label information is requested. The data subset is used to define what GP individuals evolve against. The composition of such a subset is a mixture of data forwarded under the sampling policy and historical data identified through the data archiving policy. The combination of sampling policy and the data subset achieve a decoupling between the rate at which the stream passes and the rate at which evolution commences. Benchmarking is performed on two artificial data sets with specific forms of sudden shift and gradual drift as well as a well known real-world data set.}, notes = {Also known as \cite{2611385} Distributed at GECCO-2014.}, } @inproceedings{Bhardwaj:2014:GECCOcomp, author = {Arpit Bhardwaj and Aruna Tiwari and M. Vishaal Varma and M. Ramesh Krishna}, title = {Classification of EEG signals using a novel genetic programming approach}, booktitle = {GECCO 2014 Workshop on Medical Applications of Genetic and Evolutionary Computation (MedGEC)}, year = {2014}, editor = {Stephen L. Smith and Stefano Cagnoni and Robert M. Patton}, isbn13 = {978-1-4503-2881-4}, keywords = {genetic algorithms, genetic programming}, pages = {1297--1304}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2609851}, doi = {doi:10.1145/2598394.2609851}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper, we present a new method for classification of electroencephalogram (EEG) signals using Genetic Programming (GP). The Empirical Mode Decomposition (EMD) is used to extract the features of EEG signals which served as an input for the GP. In this paper, new constructive crossover and mutation operations are also produced to improve GP. In these constructive crossover and mutation operators hill climbing search is integrated to remove the destructive nature of these operators. To improve GP, we apply constructive crossover on all the individuals which remain after reproduction. A new concept of selecting the global prime off-springs of the generation is also proposed. The constructive mutation approach is applied to poor individuals who are left after selecting globally prime off-springs. Improvement of the method is measured against classification accuracy, training time and the number of generations for EEG signal classification. As we show in the results section, the classification accuracy can be estimated to be 98.69percent on the test cases, which is better than classification accuracy of Liang and coworkers method which was published in 2010.}, notes = {Also known as \cite{2609851} Distributed at GECCO-2014.}, } @inproceedings{Hidalgo:2014:GECCOcomp, author = {J. Ignacio Hidalgo and J. Manuel Colmenar and Jose L. Risco-Martin and Esther Maqueda and Marta Botella and Jose Antonio Rubio and Alfredo Cuesta-Infante and Oscar Garnica and Juan Lanchares}, title = {Clarke and parkes error grid analysis of diabetic glucose models obtained with evolutionary computation}, booktitle = {GECCO 2014 Workshop on Medical Applications of Genetic and Evolutionary Computation (MedGEC)}, year = {2014}, editor = {Stephen L. Smith and Stefano Cagnoni and Robert M. Patton}, isbn13 = {978-1-4503-2881-4}, keywords = {genetic algorithms, genetic programming, grammatical evolution}, pages = {1305--1312}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2609856}, doi = {doi:10.1145/2598394.2609856}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Diabetes mellitus is a disease that affects to hundreds of millions of people worldwide. Maintaining a good control of the disease is critical to avoid severe long-term complications. In recent years, a lot of research has been made to improve the quality of life of the diabetic patient, especially in the automation of glucose level control. One of the main problems that arises in the (semi) automatic control of diabetes, is to obtain a model that explains the behaviour of blood glucose levels with insulin, food intakes and other external factors, fitting the characteristics of each individual or patient. Recently, Grammatical Evolution (GE), has been proposed to solve this lack of models. A proposal based on GE was able to obtain customised models of five in-silico patient data with a mean percentage average error of 13.69percent, modelling well also both hyper and hypoglycemic situations. In this paper we have extended the study of Error Grid Analysis (EGA) to prediction models in up to 8 in-silico patients. EGA is commonly used in Endocrinology to test the clinical significance of differences between measurements and real value of blood glucose, but has not been used before as a metric in obtention of glycemia models.}, notes = {Also known as \cite{2609856} Distributed at GECCO-2014.}, } @inproceedings{Lahmiri:2014:GECCOcomp, author = {Salim Lahmiri and Mounir Boukadoum}, title = {An evaluation of particle swarm optimization techniques in segmentation of biomedical images}, booktitle = {GECCO 2014 Workshop on Medical Applications of Genetic and Evolutionary Computation (MedGEC)}, year = {2014}, editor = {Stephen L. Smith and Stefano Cagnoni and Robert M. Patton}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1313--1320}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2609855}, doi = {doi:10.1145/2598394.2609855}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Image segmentation is a common image processing step to many computer vision applications with the purpose to segment pixels into different classes. As improved variants of particle swarm optimization (PSO) algorithms, the fractional-order Darwinian particle swarm optimization (FODPSO) and Darwinian particle swarm optimization (DPSO) have been proposed for image segmentation. The purpose of this paper is to compare the segmentation performance of PSO, DPSO, and FODPSO as parametric approaches to existing methods; namely the parametric fuzzy c-means (FCM) algorithm, and the non-parametric Otsu segmentation technique with application to five biomedical images. All PSO-based experiments are conducted with twenty runs to assess the effectiveness of PSO models. The universal quality index is used to evaluate the segmentation results. The obtained experimental results showed that particle swarm based algorithms outperformed both FCM and Otsu segmentation technique.}, notes = {Also known as \cite{2609855} Distributed at GECCO-2014.}, } @inproceedings{Lones:2014:GECCOcomp, author = {Michael A. Lones and Jane E. Alty and Phillipa Duggan-Carter and Andrew J. Turner and D.R. Stuart Jamieson and Stephen L. Smith}, title = {Classification and characterisation of movement patterns during levodopa therapy for parkinson's disease}, booktitle = {GECCO 2014 Workshop on Medical Applications of Genetic and Evolutionary Computation (MedGEC)}, year = {2014}, editor = {Stephen L. Smith and Stefano Cagnoni and Robert M. Patton}, isbn13 = {978-1-4503-2881-4}, keywords = {genetic algorithms, genetic programming}, pages = {1321--1328}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2609852}, doi = {doi:10.1145/2598394.2609852}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Parkinson's disease is a chronic neurodegenerative condition that manifests clinically with various movement disorders. These are often treated with the dopamine-replacement drug levodopa. However, the dosage of levodopa must be kept as low as possible in order to avoid the drug's side effects, such as the involuntary, and often violent, muscle spasms called dyskinesia, or levodopa-induced dyskinesia. In this paper, we investigate the use of genetic programming for training classifiers that can monitor the effectiveness of levodopa therapy. In particular, we evolve classifiers that can recognise tremor and dyskinesia, movement states that are indicative of insufficient or excessive doses of levodopa, respectively. The evolved classifiers achieve clinically useful rates of discrimination, with AUC>0.9. We also find that temporal classifiers generally out-perform spectral classifiers. By using classifiers that respond to low-level features of the data, we identify the conserved patterns of movement that are used as a basis for classification, showing how this approach can be used to characterise as well as classify abnormal movement.}, notes = {Also known as \cite{2609852} Distributed at GECCO-2014.}, } @inproceedings{Suzuki:2014:GECCOcompa, author = {Junichi Suzuki and Pruet Boonma}, title = {Noise-aware evolutionary TDMA optimization for neuronal signaling in medical sensor-actuator networks}, booktitle = {GECCO 2014 Workshop on Medical Applications of Genetic and Evolutionary Computation (MedGEC)}, year = {2014}, editor = {Stephen L. Smith and Stefano Cagnoni and Robert M. Patton}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1329--1336}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2609854}, doi = {doi:10.1145/2598394.2609854}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Neuronal signalling is one of several approaches to network nano-machines in the human body. This paper formulates a noisy optimization problem for a neuronal signalling protocol based on Time Division Multiple Access (TDMA) and solves the problem with a noise-aware optimiser that leverages an evolutionary algorithm. The proposed optimiser is intended to minimise signaling latency by multiplexing and parallelling signal transmissions in a given neuronal network, while maximising signaling robustness (i.e., unlikeliness of signal interference). Since latency and robustness objectives conflict with each other, the proposed optimiser seeks the optimal trade-offs between them. It exploits a nonparametric (i.e. distribution-free) statistical operator because it is not fully known what distribution(s) noise follows in each step/component in neuronal signalling. Simulation results show that the proposed optimiser efficiently obtains quality TDMA signalling schedules and operates a TDMA protocol by balancing conflicting objectives in noisy environments.}, notes = {Also known as \cite{2609854} Distributed at GECCO-2014.}, } @inproceedings{Winkler:2014:GECCOcomp, author = {Stephan M. Winkler and Michael Affenzeller and Susanne Schaller and Herbert Stekel}, title = {Data based prediction of cancer diagnoses using heterogeneous model ensembles: a case study for breast cancer, melanoma, and cancer in the respiratory system}, booktitle = {GECCO 2014 Workshop on Medical Applications of Genetic and Evolutionary Computation (MedGEC)}, year = {2014}, editor = {Stephen L. Smith and Stefano Cagnoni and Robert M. Patton}, isbn13 = {978-1-4503-2881-4}, keywords = {genetic algorithms, genetic programming}, pages = {1337--1344}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2609853}, doi = {doi:10.1145/2598394.2609853}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper we discuss heterogeneous estimation model ensembles for cancer diagnoses produced using various machine learning algorithms. Based on patients' data records including standard blood parameters, tumour markers, and information about the diagnosis of tumors, the goal is to identify mathematical models for estimating cancer diagnoses. Several machine learning approaches implemented in HeuristicLab and WEKA have been applied for identifying estimators for selected cancer diagnoses: k-nearest neighbour learning, decision trees, artificial neural networks, support vector machines, random forests, and genetic programming. The models produced using these methods have been combined to heterogeneous model ensembles. All models trained during the learning phase are applied during the test phase; the final classification is annotated with a confidence value that specifies how reliable the models are regarding the presented decision: We calculate the final estimation for each sample via majority voting, and the relative ratio of a sample's majority vote is used for calculating the confidence in the final estimation. We use a confidence threshold that specifies the minimum confidence level that has to be reached; if this threshold is not reached for a sample, then there is no prediction for that specific sample. As we show in the results section, the accuracies of diagnoses of breast cancer, melanoma, and respiratory system cancer can so be increased significantly. We see that increasing the confidence threshold leads to higher classification accuracies, bearing in mind that the ratio of samples, for which there is a classification statement, is significantly decreased.}, notes = {Also known as \cite{2609853} Distributed at GECCO-2014.}, } @inproceedings{Alvarado:2014:GECCOcomp, author = {L. A. Alvarado and L. M. Torres-Trevi\,{n}o and F. Gonzalez and L. Nieves}, title = {A mathematical model of a cold rolling mill by symbolic regression alpha-beta}, booktitle = {GECCO 2014 Workshop on Symbolic Regression and Modelling}, year = {2014}, editor = {Steven Gustafson and Ekaterina Vladislavleva}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1347--1352}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2609858}, doi = {doi:10.1145/2598394.2609858}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Improvement of processes in metallurgical industry is a constant of competitive enterprises, however, changes made in a process are risky and involves high cost and time, considering this, a model can be made even using inputs usually not presented in real process and its analysis could be useful for the improvement of the process. In this work, a mathematical model is built using only experimental data of a four high tandem cold rolling mill, a set of input variables involving characteristics of the process. The performance of the model is determined by residual analysis considering new data. Results are a non black box model with a good performance; by this way, the model is a good representation of the process under study.}, notes = {Also known as \cite{2609858} Distributed at GECCO-2014.}, } @inproceedings{Chennupati:2014:GECCOcompb, author = {Gopinath Chennupati and R. Muhammad Atif Azad and Conor Ryan}, title = {Predict the performance of GE with an ACO based machine learning algorithm}, booktitle = {GECCO 2014 Workshop on Symbolic Regression and Modelling}, year = {2014}, editor = {Steven Gustafson and Ekaterina Vladislavleva}, isbn13 = {978-1-4503-2881-4}, keywords = {genetic algorithms, genetic programming, grammatical evolution}, pages = {1353--1360}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2609860}, doi = {doi:10.1145/2598394.2609860}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The quality of the evolved solutions of an evolutionary algorithm (EA) varies across different runs and a significant percentage of runs can produce solutions of undesirable quality. These runs are a waste of computational resources, particularly in difficult problems where practitioners have time bound limitations in repeating runs. This paper proposes a completely novel approach, that of a Run Prediction Model (RPM) in which we identify and terminate evolutionary runs that are likely to produce low-quality solutions. This is justified with an Ant Colony Optimization (ACO) based classifier that learns from the early generations of a run and decides whether to continue or not. We apply RPM to Grammatical Evolution (GE) applied to four benchmark symbolic regression problems and consider several contemporary machine learning algorithms to train the predictive models and find that ACO produces the best results and acceptable predictive accuracy for this first investigation. The ACO discovered prediction models are in the form of a list of simple rules. We further analyse that list manually to tune them in order to predict poor GE runs. We then apply the analysed model to GE runs on the regression problems and terminate the runs identified by the model likely to be poor, thus increasing the rate of production of successful runs while reducing the computational effort required. We demonstrate that, although there is a high bootstrapping cost for RPM, further investigation is warranted as the mean success rate and the total execution time enjoys a statistically significant boost on all the four benchmark problems.}, notes = {Also known as \cite{2609860} Distributed at GECCO-2014.}, } @inproceedings{Kommenda:2014:GECCOcomp, author = {Michael Kommenda and Michael Affenzeller and Bogdan Burlacu and Gabriel Kronberger and Stephan M. Winkler}, title = {Genetic programming with data migration for symbolic regression}, booktitle = {GECCO 2014 Workshop on Symbolic Regression and Modelling}, year = {2014}, editor = {Steven Gustafson and Ekaterina Vladislavleva}, isbn13 = {978-1-4503-2881-4}, keywords = {genetic algorithms, genetic programming}, pages = {1361--1366}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2609857}, doi = {doi:10.1145/2598394.2609857}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this publication genetic programming (GP) with data migration for symbolic regression is presented. The motivation for the development of the algorithm is to evolve models which generalise well on previously unseen data. GP with data migration uses multiple subpopulations to maintain the genetic diversity during the algorithm run and a sophisticated training subset selection strategy. Each subpopulation is evaluated on a different fixed training subset (FTS) and additionally a variable training subset (VTS) is exchanged between the subpopulations at specific data migration intervals. Thus, the individuals are evaluated on the unification of FTS and VTS and should have better generalisation properties due to the regular changes of the VTS. The implemented algorithm is compared to several GP variants on a number of symbolic regression benchmark problems to test the effectiveness of the multiple populations and data migration strategy. Additionally, different algorithm configurations and migration strategies are evaluated to show their impact with respect to the achieved quality.}, notes = {Also known as \cite{2609857} Distributed at GECCO-2014.}, } @inproceedings{Torres-Trevi:2014:GECCOcomp, author = {L. M. Torres-Trevi\,{n}o}, title = {Identification and prediction using symbolic regression alpha-beta: preliminary results}, booktitle = {GECCO 2014 Workshop on Symbolic Regression and Modelling}, year = {2014}, editor = {Steven Gustafson and Ekaterina Vladislavleva}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1367--1372}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2609859}, doi = {doi:10.1145/2598394.2609859}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {A novel approach is proposed for generating equations from measured data of dynamic processes. A composition of unary (alpha) and binary (beta) functions is represented by a real vector and adapted by an evolutionary algorithm to build mathematical equations. The equations can be used for identification and prediction considering a mathematical model with specific number of inputs and outputs. Three cases are used for illustration of the approach where mathematical models and plots of theirs performance are presented with promising results.}, notes = {Also known as \cite{2609859} Distributed at GECCO-2014.}, } @inproceedings{Haraldsson:2014:GECCOcomp, author = {Saemundur O. Haraldsson and John R. Woodward}, title = {Automated design of algorithms and genetic improvement: contrast and commonalities}, booktitle = {GECCO 2014 4th workshop on evolutionary computation for the automated design of algorithms}, year = {2014}, editor = {John Woodward and Jerry Swan and Earl Barr}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1373--1380}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2609874}, doi = {doi:10.1145/2598394.2609874}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Automated Design of Algorithms (ADA) and Genetic Improvement (GI) are two relatively young fields of research that have been receiving more attention in recent years. Both methodologies can improve programs using evolutionary search methods and successfully produce human competitive programs. ADA and GI are used for improving functional properties such as quality of solution and non-functional properties, e.g. speed, memory and, energy consumption. Only GI of the two has been used to fix bugs, probably because it is applied globally on the whole source code while ADA typically replaces a function or a method locally. While GI is applied directly to the source code ADA works ex-situ, i.e. as a separate process from the program it is improving. Although the methodologies overlap in many ways they differ on some fundamentals and for further progress to be made researchers from both disciplines should be aware of each other's work.}, notes = {Also known as \cite{2609874} Distributed at GECCO-2014.}, } @inproceedings{Hong:2014:GECCOcomp, author = {Libin Hong and John H. Drake and Ender \"{O}zcan}, title = {A step size based self-adaptive mutation operator for evolutionary programming}, booktitle = {GECCO 2014 4th workshop on evolutionary computation for the automated design of algorithms}, year = {2014}, editor = {John Woodward and Jerry Swan and Earl Barr}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1381--1388}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2609873}, doi = {doi:10.1145/2598394.2609873}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The mutation operator is the only genetic operator in Evolutionary Programming (EP). In the past researchers have nominated Gaussian, Cauchy, and Levy distributions as mutation operators. According to the No Free Lunch theorem [9], no single mutation operator is able to outperform all others over the set of all possible functions. Potentially there is a lot of useful information generated when EP is ongoing. In this paper, we collect such information and propose a step size based self-adaptive mutation operator for Evolutionary Programming (SSEP). In SSEP, the mutation operator might be changed during the evolutionary process, based on the step size, from generation to generation. Principles for selecting an appropriate mutation operator for EP is proposed, with SSEP grounded on the principles. SSEP is shown to outperform static mutation operators in Evolutionary Programming on most of the functions tested. We also compare the experimental results of SSEP with other recent Evolutionary Programming methods, which uses multiple mutation operators.}, notes = {Also known as \cite{2609873} Distributed at GECCO-2014.}, } @inproceedings{Martin:2014:GECCOcompa, author = {Matthew A. Martin and Daniel R. Tauritz}, title = {A problem configuration study of the robustness of a black-box search algorithm hyper-heuristic}, booktitle = {GECCO 2014 4th workshop on evolutionary computation for the automated design of algorithms}, year = {2014}, editor = {John Woodward and Jerry Swan and Earl Barr}, isbn13 = {978-1-4503-2881-4}, keywords = {genetic algorithms, genetic programming}, pages = {1389--1396}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2609872}, doi = {doi:10.1145/2598394.2609872}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Black-Box Search Algorithms (BBSAs) tailored to a specific problem class may be expected to significantly outperform more general purpose problem solvers, including canonical evolutionary algorithms. Recent work has introduced a novel approach to evolving tailored BBSAs through a genetic programming hyper-heuristic. However, that first generation of hyper-heuristics suffered from over-specialisation. This paper presents a study on the second generation hyper-heuristic which employs a multi-sample training approach to alleviate the over-specialisation problem. In particular, the study is focused on the affect that the multi-sample approach has on the problem configuration landscape. A variety of experiments are reported on which demonstrate the significant increase in the robustness of the generated algorithms to changes in problem configuration due to the multi-sample approach. The results clearly show the resulting BBSAs' ability to outperform established BBSAs, including canonical evolutionary algorithms. The trade-off between a priori computational time and the generated algorithm robustness is investigated, demonstrating the performance gain possible given additional run-time.}, notes = {Also known as \cite{2609872} Distributed at GECCO-2014.}, } @inproceedings{Woodward:2014:GECCOcomp, author = {John Woodward and Simon Martin and Jerry Swan}, title = {Benchmarks that matter for genetic programming}, booktitle = {GECCO 2014 4th workshop on evolutionary computation for the automated design of algorithms}, year = {2014}, editor = {John Woodward and Jerry Swan and Earl Barr}, isbn13 = {978-1-4503-2881-4}, keywords = {genetic algorithms, genetic programming}, pages = {1397--1404}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2609875}, doi = {doi:10.1145/2598394.2609875}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {There have been several papers published relating to the practice of benchmarking in machine learning and Genetic Programming (GP) in particular. In addition, GP has been accused of targeting over-simplified 'toy' problems that do not reflect the complexity of real-world applications that GP is ultimately intended. There are also theoretical results that relate the performance of an algorithm with a probability distribution over problem instances, and so the current debate concerning benchmarks spans from the theoretical to the empirical. The aim of this article is to consolidate an emerging theme arising from these papers and suggest that benchmarks should not be arbitrarily selected but should instead be drawn from an underlying probability distribution that reflects the problem instances which the algorithm is likely to be applied to in the real-world. These probability distributions are effectively dictated by the application domains themselves (essentially data-driven) and should thus re-engage the owners of the originating data. A consequence of properly-founded benchmarking leads to the suggestion of meta-learning as a methodology for automatically designing algorithms rather than manually designing algorithms. A secondary motive is to reduce the number of research papers that propose new algorithms but do not state in advance what their purpose is (i.e. in what context should they be applied). To put the current practice of GP benchmarking in a particular harsh light, one might ask what the performance of an algorithm on Koza's lawnmower problem (a favourite toy-problem of the GP community) has to say about its performance on a very real-world cancer data set: the two are completely unrelated.}, notes = {Also known as \cite{2609875} Distributed at GECCO-2014.}, } @inproceedings{Kovitz:2014:GECCOcomp, author = {Ben Kovitz and Jerry Swan}, title = {Structural stigmergy: a speculative pattern language for metaheuristics}, booktitle = {GECCO 2014 Workshop on Metaheuristic Design Patterns (MetaDeeP)}, year = {2014}, editor = {Jerry Swan and Krzysztof Krawiec and John Woodward and Chris Simons and John Clark}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1407--1410}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2609845}, doi = {doi:10.1145/2598394.2609845}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {To construct graphs whose quality results from complicated relationships that pervade the entire graph, especially relationships at multiple scales, follow a strategy of repeatedly making local patches to a single graph. Look for small, easily recognised flaws in local areas of the graph and fix them. Add tags to the graph to represent non-local relationships and higher-level structures as individual nodes. The tags then have easily recognised flaws that relate to non-local and higher-level concerns, enabling local patching to set off cascades of local fixes that address those concerns.}, notes = {Also known as \cite{2609845} Distributed at GECCO-2014.}, } @inproceedings{Kovitz:2014:GECCOcompa, author = {Ben Kovitz and Jerry Swan}, title = {Tagging in metaheuristics}, booktitle = {GECCO 2014 Workshop on Metaheuristic Design Patterns (MetaDeeP)}, year = {2014}, editor = {Jerry Swan and Krzysztof Krawiec and John Woodward and Chris Simons and John Clark}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1411--1414}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2609844}, doi = {doi:10.1145/2598394.2609844}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Could decisions made during some search iterations use information discovered by other search iterations? Then store that information in tags: data that persist between search iterations.}, notes = {Also known as \cite{2609844} Distributed at GECCO-2014.}, } @inproceedings{Krawiec:2014:GECCOcomp, author = {Krzysztof Krawiec}, title = {Metaheuristic design pattern: candidate solution repair}, booktitle = {GECCO 2014 Workshop on Metaheuristic Design Patterns (MetaDeeP)}, year = {2014}, editor = {Jerry Swan and Krzysztof Krawiec and John Woodward and Chris Simons and John Clark}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1415--1418}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2609847}, doi = {doi:10.1145/2598394.2609847}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In metaheuristic algorithms applied to certain problems, it may be difficult to design search operators that guarantee producing feasible search points. In such cases, it may be more efficient to allow a search operator to yield an infeasible solution, and then turn it into a feasible one using a repair process. This paper is an attempt to provide a broad perspective on the candidate solution repair and frame it as a metaheuristic design pattern.}, notes = {Also known as \cite{2609847} Distributed at GECCO-2014.}, } @inproceedings{Lones:2014:GECCOcompa, author = {Michael A. Lones}, title = {Metaheuristics in nature-inspired algorithms}, booktitle = {GECCO 2014 Workshop on Metaheuristic Design Patterns (MetaDeeP)}, year = {2014}, editor = {Jerry Swan and Krzysztof Krawiec and John Woodward and Chris Simons and John Clark}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1419--1422}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2609841}, doi = {doi:10.1145/2598394.2609841}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {To many people, the terms nature-inspired algorithm and metaheuristic are interchangeable. However, this contemporary usage is not consistent with the original meaning of the term metaheuristic, which referred to something closer to a design pattern than to an algorithm. In this paper, it is argued that the loss of focus on true metaheuristics is a primary reason behind the explosion of 'novel' nature-inspired algorithms and the issues this has raised. To address this, this paper attempts to explicitly identify the metaheuristics that are used in conventional optimisation algorithms, discuss whether more recent nature-inspired algorithms have delivered any fundamental new knowledge to the field of metaheuristics, and suggest some guidelines for future research in this field.}, notes = {Also known as \cite{2609841} Distributed at GECCO-2014.}, } @inproceedings{Lopez-Iba:2014:GECCOcompa, author = {Manuel L\'{o}pez-Ib\'{a}\,{n}ez and Franco Mascia and Marie-\'{E}l\'{e}onore Marmion and Thomas St\"{u}tzle}, title = {A template for designing single-solution hybrid metaheuristics}, booktitle = {GECCO 2014 Workshop on Metaheuristic Design Patterns (MetaDeeP)}, year = {2014}, editor = {Jerry Swan and Krzysztof Krawiec and John Woodward and Chris Simons and John Clark}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1423--1426}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2609846}, doi = {doi:10.1145/2598394.2609846}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Single-solution metaheuristics are among the earliest and most successful metaheuristics, with many variants appearing in the literature. Even among the most popular variants, there is a large degree of overlap in terms of actual behaviour. Moreover, in the case of hybrids of different metaheuristics, traditional names do not actually reflect how the hybrids are composed. In this paper, we discuss a template for single-solution hybrid metaheuristics. Our template builds upon the Paradiseo-MO framework, but restricts itself to a pre-defined structure based on iterated local search (ILS). The flexibility is given by generalising the components of ILS (perturbation, local search and acceptance criterion) in order to incorporate components from other metaheuristics. We give precise definitions of these components within the context of our proposed template. The template proposed is flexible enough to reproduce many classical single-solution metaheuristics and hybrids thereof, while at the same time being sufficiently concrete to generate code from a grammar description in order to support automatic design of algorithms. We give examples of three IG-VNS hybrids that can be instantiated from the proposed template.}, notes = {Also known as \cite{2609846} Distributed at GECCO-2014.}, } @inproceedings{Neumann:2014:GECCOcompa, author = {Geoffrey Neumann and Jerry Swan and Mark Harman and John A. Clark}, title = {The executable experimental template pattern for the systematic comparison of metaheuristics: Extended Abstract}, booktitle = {GECCO 2014 Workshop on Metaheuristic Design Patterns (MetaDeeP)}, year = {2014}, editor = {Jerry Swan and Krzysztof Krawiec and John Woodward and Chris Simons and John Clark}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1427--1430}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2609850}, doi = {doi:10.1145/2598394.2609850}, publisher = {ACM}, publisher_address = {New York, NY, USA}, notes = {Also known as \cite{2609850} Distributed at GECCO-2014.}, } @inproceedings{Shackelford:2014:GECCOcomp, author = {Mark R.N. Shackelford and Christopher L. Simons}, title = {Metaheuristic design pattern: interactive solution presentation}, booktitle = {GECCO 2014 Workshop on Metaheuristic Design Patterns (MetaDeeP)}, year = {2014}, editor = {Jerry Swan and Krzysztof Krawiec and John Woodward and Chris Simons and John Clark}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1431--1434}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2609849}, doi = {doi:10.1145/2598394.2609849}, publisher = {ACM}, publisher_address = {New York, NY, USA}, notes = {Also known as \cite{2609849} Distributed at GECCO-2014.}, } @inproceedings{Swan:2014:GECCOcomp, author = {Jerry Swan and Zoltan A. Kocsis and Alexei Lisitsa}, title = {The 'representative' metaheuristic design pattern}, booktitle = {GECCO 2014 Workshop on Metaheuristic Design Patterns (MetaDeeP)}, year = {2014}, editor = {Jerry Swan and Krzysztof Krawiec and John Woodward and Chris Simons and John Clark}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1435--1436}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2609842}, doi = {doi:10.1145/2598394.2609842}, publisher = {ACM}, publisher_address = {New York, NY, USA}, notes = {Also known as \cite{2609842} Distributed at GECCO-2014.}, } @inproceedings{Woodward:2014:GECCOcompa, author = {John R. Woodward and Jerry Swan}, title = {Template method hyper-heuristics}, booktitle = {GECCO 2014 Workshop on Metaheuristic Design Patterns (MetaDeeP)}, year = {2014}, editor = {Jerry Swan and Krzysztof Krawiec and John Woodward and Chris Simons and John Clark}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1437--1438}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2609843}, doi = {doi:10.1145/2598394.2609843}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The optimization literature is awash with metaphorically-inspired metaheuristics and their subsequent variants and hybridisation. This results in a plethora of methods, with descriptions that are often polluted with the language of the metaphor which inspired them [8]. Within such a fragmented field, the traditional approach of manual 'operator tweaking' makes it difficult to establish the contribution of individual metaheuristic components to the overall success of a methodology. Irrespective of whether it happens to best the state-of-the-art, such 'tweaking' is so labour-intensive that does relatively little to advance scientific understanding. In order to introduce further structure and rigour, it is therefore desirable to not only to be able to specify entire families of metaheuristics (rather than individual metaheuristics), but also be able to generate and test them. In particular, the adoption of a model agnostic approach towards the generation of metaheuristics would help to establish which metaheuristic components are useful contributors to a solution.}, notes = {Also known as \cite{2609843} Distributed at GECCO-2014.}, } @inproceedings{Woodward:2014:GECCOcompb, author = {John Woodward and Jerry Swan and Simon Martin}, title = {The 'composite' design pattern in metaheuristics}, booktitle = {GECCO 2014 Workshop on Metaheuristic Design Patterns (MetaDeeP)}, year = {2014}, editor = {Jerry Swan and Krzysztof Krawiec and John Woodward and Chris Simons and John Clark}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1439--1444}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2609848}, doi = {doi:10.1145/2598394.2609848}, publisher = {ACM}, publisher_address = {New York, NY, USA}, notes = {Also known as \cite{2609848} Distributed at GECCO-2014.}, } @inproceedings{Buzdalov:2014:GECCOcompa, author = {Maxim Buzdalov and Irina Petrova and Arina Buzdalova}, title = {NSGA-II implementation details may influence quality of solutions for the job-shop scheduling problem}, booktitle = {GECCO 2014 Late breaking abstracts workshop}, year = {2014}, editor = {Dirk Sudholt}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1445--1446}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2602288}, doi = {doi:10.1145/2598394.2602288}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The helper-objective approach for solving the job-shop scheduling problem using multi-objective evolutionary algorithms is considered. We implemented the approach from the Lochtefeld and Ciarallo paper using NSGA-II with the correct implementation of the non-dominated sorting procedure which is able to work with equal values of objectives. The experimental evaluation showed the significant improvement of solution quality. We also report new best results for 16 out of 24 problem instances used in the considered paper.}, notes = {Also known as \cite{2602288} Distributed at GECCO-2014.}, } @inproceedings{Chen:2014:GECCOcompa, author = {Stephen Chen and James Montgomery and Antonio Boluf\'{e}-R\"{o}hler}, title = {Some measurements on the effects of the curse of dimensionality}, booktitle = {GECCO 2014 Late breaking abstracts workshop}, year = {2014}, editor = {Dirk Sudholt}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1447--1448}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2602271}, doi = {doi:10.1145/2598394.2602271}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The existence of the curse of dimensionality is well known, and its general effects are well acknowledged. However, perhaps due to this colloquial understanding, specific measurements on the curse of dimensionality and its effects are not as extensive. In continuous domains, the volume of the search space grows exponentially with dimensionality. Conversely, the number of function evaluations budgeted to explore this search space usually grows only linearly. New experiments show that particle swarm optimization and differential evolution have super-linear growth in convergence time as dimensionality grows. When restricted by a linear growth in allotted function evaluations, this super-linear growth in convergence time leads to a decrease in the allowed population size.}, notes = {Also known as \cite{2602271} Distributed at GECCO-2014.}, } @inproceedings{Cwiek:2014:GECCOcomp, author = {Marcin Cwiek and Jakub Nalepa}, title = {A fast genetic algorithm for the flexible job shop scheduling problem}, booktitle = {GECCO 2014 Late breaking abstracts workshop}, year = {2014}, editor = {Dirk Sudholt}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1449--1450}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2602280}, doi = {doi:10.1145/2598394.2602280}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper presents a fast genetic algorithm (GA) for solving the flexible job shop scheduling problem (FJSP). The FJSP is an extension of a classical NP-hard job shop scheduling problem. Here, we combine the active schedule constructive crossover (ASCX) with the generalised order crossover (GOX). Also, we show how to divide a population of solutions in the high-low fit selection scheme in order to guide the search efficiently. An initial experimental study indicates high convergence capabilities of the proposed GA.}, notes = {Also known as \cite{2602280} Distributed at GECCO-2014.}, } @inproceedings{David:2014:GECCOcomp, author = {Omid E. David and Iddo Greental}, title = {Genetic algorithms for evolving deep neural networks}, booktitle = {GECCO 2014 Late breaking abstracts workshop}, year = {2014}, editor = {Dirk Sudholt}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1451--1452}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2602287}, doi = {doi:10.1145/2598394.2602287}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In recent years, deep learning methods applying unsupervised learning to train deep layers of neural networks have achieved remarkable results in numerous fields. In the past, many genetic algorithms based methods have been successfully applied to training neural networks. In this paper, we extend previous work and propose a GA-assisted method for deep learning. Our experimental results indicate that this GA-assisted approach improves the performance of a deep autoencoder, producing a sparser neural network.}, notes = {Also known as \cite{2602287} Distributed at GECCO-2014.}, } @inproceedings{deVasconcelosSegundo:2014:GECCOcomp, author = {Emerson Hochsteiner {de Vasconcelos Segundo} and Gabriel Fiori Neto and Andre Mendes {da Silva} and Viviana Cocco Mariani and Leandro dos Santos Coelho}, title = {A modified gravitational search algorithm for continuous optimization}, booktitle = {GECCO 2014 Late breaking abstracts workshop}, year = {2014}, editor = {Dirk Sudholt}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1453--1454}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2602281}, doi = {doi:10.1145/2598394.2602281}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The gravitational search algorithm (GSA) is a stochastic population-based metaheuristic inspired by the interaction of masses via Newtonian gravity law. In this paper, we propose a modified GSA (MGSA) based on logarithm and Gaussian signals for enhancing the performance of standard GSA. To evaluate the performance of the proposed MGSA, well-known benchmark functions in the literature are optimised using the proposed MGSA, and provides comparisons with the standard GSA.}, notes = {Also known as \cite{2602281} Distributed at GECCO-2014.}, } @inproceedings{Coelho:2014:GECCOcomp, author = {Leandro dos Santos Coelho and Viviana Cocco Mariani and Helon Vicente Hultmann Ayala and Andre Mendes {da Silva} and Nelson Jhoe Batistela and Jean Vianei Leite}, title = {Bat-inspired optimization approach applied to jiles-atherton hysteresis parameters tuning}, booktitle = {GECCO 2014 Late breaking abstracts workshop}, year = {2014}, editor = {Dirk Sudholt}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1455--1456}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2602275}, doi = {doi:10.1145/2598394.2602275}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Recently, it was proposed a new metaheuristic optimization approach namely bat algorithm (BA), which is based on the fascinating capability of microbats in to find their prey and discriminate different types of insects even in complete darkness. In this paper, we introduce a novel enhanced hybrid approach combining BA with a mutation style operator from differential evolution and beta probability distribution (BADEBD) and report its performance on to identification of the 10 hysteresis parameters for the 2-dimensional Jiles-Atherton hysteresis modelling to a material under rotational excitation. Compared with the classical BA, the proposed BADEBD performs better in terms of the solution accuracy and the convergence rate.}, notes = {Also known as \cite{2602275} Distributed at GECCO-2014.}, } @inproceedings{Fernandez-Rendon:2014:GECCOcomp, author = {Javier Fern\'{a}ndez-Rend\'{o}n and Katya Rodr\'{\i}guez-V\'{a}zquez}, title = {Portfolio optimization using an integer genetic algorithm}, booktitle = {GECCO 2014 Late breaking abstracts workshop}, year = {2014}, editor = {Dirk Sudholt}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1457--1458}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2602278}, doi = {doi:10.1145/2598394.2602278}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The portfolio selection is an essential component of fund administration because it contributes to economic growth of the investor. Many of the related works use the Markowitz's mean-variance portfolio selection model approach to solve this optimization problem. However, the use of continuous variables in this approach does not allow us to implement directly the obtained solutions because assets cannot be divided. This paper presents a portfolio selection model that involves integer variables, allowing a more realistic treatment. Due to the complexity of this mixed-integer nonlinear programming problem, a corresponding genetic algorithm is used to solve it.}, notes = {Also known as \cite{2602278} Distributed at GECCO-2014.}, } @inproceedings{Garcia:2014:GECCOcomp, author = {Mauricio Garcia and Hugo J. Escalante and Manuel Montes and Alicia Morales and Eduardo Morales}, title = {Towards the automated generation of term-weighting schemes for text categorization}, booktitle = {GECCO 2014 Late breaking abstracts workshop}, year = {2014}, editor = {Dirk Sudholt}, isbn13 = {978-1-4503-2881-4}, keywords = {genetic algorithms, genetic programming}, pages = {1459--1460}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2602286}, doi = {doi:10.1145/2598394.2602286}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper describes ongoing research on the use of genetic programming to learn term-weighting schemes to be used for text classification. A term-weighting scheme (TWS) determines the way in which documents are represented before applying a text classification model. We propose a genetic program that aims at learning an effective TWS that can improve the performance in text classification. We report preliminary experimental results that give evidence of the validity of the proposal.}, notes = {Also known as \cite{2602286} Distributed at GECCO-2014.}, } @inproceedings{Kim:2014:GECCOcomp, author = {Jinhyun Kim and Byung-Ro Moon}, title = {A genetic algorithm for linear ordering problem using an approximate fitness evaluation}, booktitle = {GECCO 2014 Late breaking abstracts workshop}, year = {2014}, editor = {Dirk Sudholt}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1461--1462}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2602282}, doi = {doi:10.1145/2598394.2602282}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Genetic algorithms are widely used to solve combinatorial optimization problems, but they often take a long time. Usually, generating and evaluating a large number of different solutions spend most of the running time. We propose a genetic algorithm for the linear ordering problem which uses an approximate fitness evaluation. We use a part of the edges to compute the fitness function value, and the number of the edges for this is gradually increased during the evolutionary process. We present experimental results on the benchmark library LOLIB. The approximation scheme reduced the running time without loss of solution quality in general.}, notes = {Also known as \cite{2602282} Distributed at GECCO-2014.}, } @inproceedings{Mollinetti:2014:GECCOcomp, author = {Marco A.F. Mollinetti and Daniel Leal Souza and Ot\'{a}vio Noura Teixeira}, title = {ABC+ES: a novel hybrid artificial bee colony algorithm with evolution strategies}, booktitle = {GECCO 2014 Late breaking abstracts workshop}, year = {2014}, editor = {Dirk Sudholt}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1463--1464}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2602277}, doi = {doi:10.1145/2598394.2602277}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper has the purpose of presenting a new hybridisation of the Artificial Bee Colony Algorithm (ABC) based on the evolutionary strategies (ES) found on the Evolutionary Particle Swarm Optimization (EPSO). The main motivation of this approach is to augment the original ABC in a way that combines the effectiveness and simplicity of the ABC with the robustness and increased exploitation of the Evolution Strategies. The algorithm is intended to be tested on two large-scale engineering design problem and its results compared to other optimization techniques.}, notes = {Also known as \cite{2602277} Distributed at GECCO-2014.}, } @inproceedings{Moore:2014:GECCOcomp, author = {Frank W. Moore and Brendan J. Babb}, title = {Evolved transforms for improved reconstruction of lossy-compressed NASA images}, booktitle = {GECCO 2014 Late breaking abstracts workshop}, year = {2014}, editor = {Dirk Sudholt}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1465--1466}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2602276}, doi = {doi:10.1145/2598394.2602276}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper describes a methodology for evolving image reconstruction transforms consisting of an arbitrarily large, user-selected number of wavelet and scaling numbers. Given images previously subjected to lossy compression using NASA's wavelet-based ICER compressor, these novel transforms are capable of reconstructing those images with less error than ICER's own reconstruction scheme. This advance has the potential to enhance the science value of all images subjected to lossy compression.}, notes = {Also known as \cite{2602276} Distributed at GECCO-2014.}, } @inproceedings{Nalepa:2014:GECCOcomp, author = {Jakub Nalepa}, title = {Adaptive memetic algorithm for the vehicle routing problem with time windows}, booktitle = {GECCO 2014 Late breaking abstracts workshop}, year = {2014}, editor = {Dirk Sudholt}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1467--1468}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2602273}, doi = {doi:10.1145/2598394.2602273}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper presents an adaptive memetic algorithm (AMA) to minimise the total travel distance in the NP-hard vehicle routing problem with time windows (VRPTW). Although memetic algorithms (MAs) have been proved to be very efficient in solving the VRPTW, their main drawback is an unclear tuning of their numerous parameters. Here, we introduce the AMA in which the selection scheme and the population size are adjusted during the search. We propose a new adaptive selection scheme to balance the exploration and exploitation of the search space. An extensive experimental study confirms that the AMA outperforms a standard MA in terms of the convergence capabilities.}, notes = {Also known as \cite{2602273} Distributed at GECCO-2014.}, } @inproceedings{Ngo:2014:GECCOcomp, author = {Mathias Ngo and Rapha\"{e}l Labayrade}, title = {An iterative model refinement approach for MOEA computation time reduction}, booktitle = {GECCO 2014 Late breaking abstracts workshop}, year = {2014}, editor = {Dirk Sudholt}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1469--1470}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2602284}, doi = {doi:10.1145/2598394.2602284}, publisher = {ACM}, publisher_address = {New York, NY, USA}, notes = {Also known as \cite{2602284} Distributed at GECCO-2014.}, } @inproceedings{Osaba:2014:GECCOcomp, author = {Eneko Osaba and Fernando Diaz and Roberto Carballedo and Idoia {de la Iglesia} and Enrique Onieva and Asier Perallos}, title = {A study on the efficiency of neutral crossover operators in genetic algorithms applied to the bin packing problem}, booktitle = {GECCO 2014 Late breaking abstracts workshop}, year = {2014}, editor = {Dirk Sudholt}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1471--1472}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2602268}, doi = {doi:10.1145/2598394.2602268}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper examines the influence of neutral crossover operators in a genetic algorithm (GA) applied to the one-dimensional bin packing problem. In the experimentation 16 benchmark instances have been used and the results obtained by three different GAs are compared with the ones obtained by an evolutionary algorithm (EA). The aim of this work is to determine whether an EA (with no crossover functions) can perform similarly to a GA.}, notes = {Also known as \cite{2602268} Distributed at GECCO-2014.}, } @inproceedings{Osaba:2014:GECCOcompa, author = {Eneko Osaba and Fernando Diaz and Roberto Carballedo and Enrique Onieva and Pedro Lopez}, title = {A study on the impact of heuristic initialization functions in a genetic algorithm solving the N-queens problem}, booktitle = {GECCO 2014 Late breaking abstracts workshop}, year = {2014}, editor = {Dirk Sudholt}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1473--1474}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2602269}, doi = {doi:10.1145/2598394.2602269}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper the influence of using heuristic functions to initialise the population of a classic genetic algorithm (GA) applied to the N-Queens Problem (NQP) is analysed. The aim of this work is to evaluate the impact of the heuristic initialisation phase on the results of the classic GA. In order to probe this, several experiments using two different initialisation functions have been carried out. In this paper, the well-known NQP has been used as benchmark problem, but the objective of the authors is to contrast the findings of this study with other combinatorial optimization problems.}, notes = {Also known as \cite{2602269} Distributed at GECCO-2014.}, } @inproceedings{Ouaddah:2014:GECCOcomp, author = {Ahlem Ouaddah and Dalila Boughaci}, title = {Improving reconstructed images using hybridization between local search and harmony search meta-heuristics}, booktitle = {GECCO 2014 Late breaking abstracts workshop}, year = {2014}, editor = {Dirk Sudholt}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1475--1476}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2602283}, doi = {doi:10.1145/2598394.2602283}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Image reconstruction from projections in tomography is an ill-posed, inverse problem. This problem is always a challenge because there are no standard methods which give satisfactory results. In this paper we propose hybridisation between Local Search (LS) and Harmony Search (HS) metaheuristics to improve quality of reconstructed images. The proposed method is implemented, tested on some images and compared to LS and Filtered backprojection (FBP) methods. The preliminary results are promising and prove the efficiency of our method.}, notes = {Also known as \cite{2602283} Distributed at GECCO-2014.}, } @inproceedings{Sato:2014:GECCOcomp, author = {Mikiko Sato and Shigeyoshi Tsutsui and Noriyuki Fujimoto and Yuji Sato and Mitaro Namiki}, title = {First results of performance comparisons on many-core processors in solving QAP with ACO: kepler GPU versus xeon PHI}, booktitle = {GECCO 2014 Late breaking abstracts workshop}, year = {2014}, editor = {Dirk Sudholt}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1477--1478}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2602274}, doi = {doi:10.1145/2598394.2602274}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper compares the performance of parallel computation on two types of many-core processors, Tesla K20c GPU and Xeon Phi 5110P, in solving the quadratic assignment problem (QAP) with ant colony optimization (ACO). The results show that the performance on Xeon Phi 5110P is not so promising compared to the Tesla K20c GPU on these problems. Further efficient implementation methods must be investigated for Xeon Phi.}, notes = {Also known as \cite{2602274} Distributed at GECCO-2014.}, } @inproceedings{Yang:2014:GECCOcompd, author = {Ming-Der Yang and Yeh-Feng Yang and Yi-Ping Chen}, title = {Evaluation of fitness functions of GA classification}, booktitle = {GECCO 2014 Late breaking abstracts workshop}, year = {2014}, editor = {Dirk Sudholt}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1479--1480}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2602267}, doi = {doi:10.1145/2598394.2602267}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Genetic Algorithm (GA) classifier can automatically search a proper clustering number according to fitness evaluation instead of assignment by users. In this study, a GA classifier with various fitness functions is adopted to search the cluster centres and a suitable cluster number for digital images to overcome the disadvantages of the conventional unsupervised classifier. By employing a proper clustering index as fitness, a GA with length-variable chromosome can determine the most suitable number of clusters and the most proper cluster centers. This paper evaluates three popular classification indexes, including DBI, FCMI, and PASI, as fitness functions in GA operation. The GA classifier is applied to SPOT-5 satellite image to verify its accuracy and robustness. The results show the GA classifier adopting FCMI having the best performance, followed by DBI and PASI, sequentially. Regarding to computation efficiency, the GA classification with DBI took much less computation time of the GA classifications with FCMI and PASI.}, notes = {Also known as \cite{2602267} Distributed at GECCO-2014.}, } @inproceedings{Zhang:2014:GECCOcompb, author = {Haopeng Zhang and Fumin Zhang and Qing Hui}, title = {A speed-up and speed-down strategy for swarm optimization}, booktitle = {GECCO 2014 Late breaking abstracts workshop}, year = {2014}, editor = {Dirk Sudholt}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1481--1482}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2602285}, doi = {doi:10.1145/2598394.2602285}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper, inspired by speed-up and speed-down (SUSD) mechanism observed by the fish swarm avoiding light, an SUSD strategy is proposed to develop new swarm intelligence based optimization algorithms to enhance the accuracy and efficiency of swarm optimization algorithms. By comparing with the global best solution, each particle adaptively speeds up and speeds down towards the best solution. Specifically, a new directed speed term is added to the original particle swarm optimization (PSO) algorithm or other PSO variations. Due to the SUSD mechanism, the algorithm shows a great improvement of the accuracy and convergence rate compared with the original PSO and other PSO variations. The numerical evaluation is provided by solving recent benchmark functions in IEEE CEC 2013.}, notes = {Also known as \cite{2602285} Distributed at GECCO-2014.}, } @inproceedings{Zhang:2014:GECCOcompc, author = {Ming-an Zhang and Yong Deng and Dong-xia Chang}, title = {A novel genetic clustering algorithm with variable-length chromosome representation}, booktitle = {GECCO 2014 Late breaking abstracts workshop}, year = {2014}, editor = {Dirk Sudholt}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1483--1484}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2602272}, doi = {doi:10.1145/2598394.2602272}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The paper proposed a new genetic clustering algorithm with variable-length chromosome representation (GCVCR), which can automatically evolve and find the optimal number of clusters as well as proper cluster centres of the data set. A new clustering criterion based on message passing between data points and the candidate centers described by the chromosome are presented to make the clustering problem more effective. The simulation results show the effectiveness of the proposed algorithm.}, notes = {Also known as \cite{2602272} Distributed at GECCO-2014.}, } @inproceedings{Zhang:2014:GECCOcompd, author = {Ming-an Zhang and Yong Deng and Dong-xia Chang}, title = {A novel quantum genetic clustering algorithm for data segmentation}, booktitle = {GECCO 2014 Late breaking abstracts workshop}, year = {2014}, editor = {Dirk Sudholt}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1485--1486}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2602270}, doi = {doi:10.1145/2598394.2602270}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Based on the concept and principles of quantum computing, a novel genetic clustering algorithm is proposed, which can automatically clustering a data set into clusters, and evolve the optimal number of clusters as well as the cluster centres of a data set. A Q-gate with adaptive selection of the angle for every niche is introduced as a variation operator to drive individuals toward better solutions. Experiments show that the algorithm proposed is better than simple clustering algorithms.}, notes = {Also known as \cite{2602270} Distributed at GECCO-2014.}, } @inproceedings{OReilly:2014:GECCOcompa, author = {Una-May O'Reilly and Anna Esparcia and Anne Auger and Carola Doerr and Aniko Ekart and Gabriela Ochoa}, title = {Women@GECCO 2014}, booktitle = {Women@GECCO 2014}, year = {2014}, editor = {Una-May O'Reilly and Anna Esparcia and Gabriela Ochoa and Aniko Ekart and Carola Doerr and Anne Auger}, isbn13 = {978-1-4503-2881-4}, keywords = {}, pages = {1487--1488}, month = {12-16 July}, organisation = {SIGEVO}, address = {Vancouver, BC, Canada}, doi = {http://dx.doi.org/10.1145/2598394.2611386}, doi = {doi:10.1145/2598394.2611386}, publisher = {ACM}, publisher_address = {New York, NY, USA}, notes = {Also known as \cite{2611386} Distributed at GECCO-2014.}, } @proceedings(Arnold:2014:GECCOcomp, title = {GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion}, year = 2014, editor = {Dirk Arnold and Mengjie Zhang and Ryan Urbanowicz and Muhammad Iqbal and Kamran Shafi and Forrest Stonedahl and William Rand and Tea Tusar and Boris Naujoks and David Walker and Richard Everson and Jonathan Fieldsend and Stefan Wagner and Michael Affenzeller and Zhun Fan and Yaochu Jin and Hod Lipson and Erik Goodman and Alexandru-Adrian Tantar and Emilia Tantar and Peter A. N. Bosman and Kent McClymont and Kevin Sim and Gabriela Ochoa and Ed Keedwell and Anna I Esparcia-Alcazar and Frank W. Moore and Jaume Bacardit and Ignacio Arnaldo and Kalyan Veeramachaneni and Una-May O'Reilly and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Steven Gustafson and Ekaterina Vladislavleva and John Woodward and Jerry Swan and Earl Barr and Krzysztof Krawiec and Chris Simons and John Clark and Dirk Sudholt and Anna Esparcia and Aniko Ekart and Carola Doerr and Anne Auger}, address = {Vancouver, BC, Canada}, publisher_address = {New York, NY, USA}, month = {12-16 July}, organisation = {SIGEVO}, keywords = {genetic algorithms, genetic programming, Keynotes and invited talk, 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, Introductory tutorials, Advanced tutorials, Specialized tutorials, 17th annual international workshop on learning classifier systems, Eighth Annual Workshop on Evolutionary Computation and Multi-Agent Systems and Simulation (ECoMASS), student workshop, VizGEC: Workshop on visualisation in genetic and evolutionary computation, Workshop on Evolutionary Computation Software Systems (EvoSoft), evolutionary synthesis of dynamical systems, Workshop on Green and Efficient Energy Applications of Genetic and Evolutionary Computation (GreenGEC), Workshop on Problem Understanding and Real-world Optimisation (PURO), Workshop on genetic and evolutionary computation in defense, security and risk management (SecDef), Workshop on Evolutionary Computation for Big Data and Big Learning, Workshop on Medical Applications of Genetic and Evolutionary Computation (MedGEC), Workshop on Symbolic Regression and Modelling, 4th workshop on evolutionary computation for the automated design of algorithms, Workshop on Metaheuristic Design Patterns (MetaDeeP), Late breaking abstracts workshop, Women@GECCO 2014}, ISBN13 = {978-1-4503-2881-4}, url = {http://dl.acm.org/citation.cfm?id=2598394}, notes = {Distributed at GECCO-2014.}, )