%5 Aug 2011 ensure keys are unique %processed by gecco2011_authors.awk Thu Jul 28 20:37:23 BST 2011 %1 gecco2011.bib3_ %processed by gecco2011_keywords.txt Thu Jul 28 20:37:22 BST 2011 %1 gecco2011_keywords.txt %2 gecco2011.bib2_ %processed by gecco2011_toc.awk $Revision: 1.10 $ Thu Jul 28 20:36:47 BST 2011 %1 gecco2011_toc.txt %2 gecco2011.bib %Bernard Rous, ACM, Jul 26, 2011 %WBL 1 Aug 2017 ensure passes bibclean v3.02 @inproceedings{Schulman:2011:GECCO, author = {Rebecca Schulman}, title = {Beyond biology: designing a new mechanism for self-replication and evolution at the nanoscale}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {7--14}, note = {Invited talk}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001578}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {As biology demonstrates, evolutionary algorithms are an extraordinarily powerful way to design complex nanoscale systems. While we can harness the biological apparatus for replicating and selecting DNA sequences to evolve enzymes and to some extent, organisms, we would like to build replication machinery that would allow us to evolve designs for a much wider variety of materials and systems. Here we describe work that uses techniques from the new field of structural DNA nanotechnology to modularly design nanoscale components that together can be assembled into a system for self-replicating a new form of chemical information or genome, and thus for evolving a new type of chemical sequence.}, notes = {Also known as \cite{2001578} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Rasmussen:2011:GECCO, author = {Steen Rasmussen and Anders Albertsen and Harold Fellermann and Pernille Lykke Pedersen and Carsten Svaneborg and Hans Ziock}, title = {Assembling living materials and engineering life-like technologies}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {15--20}, note = {Invited talk}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001579}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Von Neumann, the inventor of the modern computer, realised that if life is a physical process, it should be possible to implement life in other media than biochemistry. In the 1950s, he was one of the first to propose the possibility of implementing genuine living processes in computers and robots. This perspective, while still controversial, is rapidly gaining momentum in many science and engineering communities. Below, we summarise our recent activities to create artificial life from scratch in physicochemical systems. We also outline the nature of the grand science and engineering challenges faced as we seek to realise Von Neumann's vision: Integration of information processing and material production from the nano- to the macroscale in technical systems.}, notes = {Also known as \cite{2001579} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Rada-Vilela:2011:GECCO, author = {Juan Rada-Vilela and Mengjie Zhang and Winston Seah}, title = {A performance study on synchronous and asynchronous updates in particle swarm optimization}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {21--28}, keywords = {Ant colony optimization and swarm intelligence}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001581}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This work provides a further study on the difference between synchronous and asynchronous updates in Particle Swarm Optimisation with different neighbourhood sizes ranging from local best to global best. Ten well-known functions are used as benchmarks on both variants. Statistical tests performed on the results provide strong evidence to claim that synchronous updates yield in general better results with similar or even faster speed of convergence than its asynchronous counterpart, contrary to observations and conclusions of previous studies based solely on descriptive statistics.}, notes = {Also known as \cite{2001581} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{HongliangLiu:2011:GECCO, author = {Hongliang Liu and Enda Howely and Jim Duggan}, title = {Particle swarm optimisation with gradually increasing directed neighbourhoods}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {29--36}, keywords = {Ant colony optimization and swarm intelligence}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001582}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Particle swarm optimisation (PSO) is an intelligent random search algorithm, and the key to success is to effectively balance between the exploration of the solution space in the early stages and the exploitation of the solution space in the late stages. This paper presents a new dynamic topology called gradually increasing directed neighbourhoods (GIDN) that provides an effective way to balance between exploration and exploitation in the entire iteration process. In our model, each particle begins with a small number of connections and there are many small isolated swarms that improve the exploration ability. At each iteration, we gradually add a number of new connections between particles which improves the ability of exploitation gradually. Furthermore, these connections among particles are created randomly and have directions. We formalise this topology using random graph representations. Experiments are conducted on 31 benchmark test functions to validate our proposed topology. The results show that the PSO with GIDN performs much better than a number of the state of the art algorithms on almost all of the 31 functions.}, notes = {Also known as \cite{2001582} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{al-Rifaie:2011:GECCO, author = {Mohammad Majid al-Rifaie and Mark John Bishop and Tim Blackwell}, title = {An investigation into the merger of stochastic diffusion search and particle swarm optimisation}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {37--44}, keywords = {Ant colony optimization and swarm intelligence}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001583}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This study reports early research aimed at applying the powerful resource allocation mechanism deployed in Stochastic Diffusion Search (SDS) [4] to the Particle Swarm Optimiser (PSO) metaheuristic [22], effectively merging the two swarm intelligence algorithms. The results reported herein suggest that the hybrid algorithm, exploiting information sharing between particles, has the potential to improve the optimisation capability of conventional PSOs.}, notes = {Also known as \cite{2001583} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Setayesh:2011:GECCO, author = {Mahdi Setayesh and Mengjie Zhang and Mark Johnston}, title = {Detection of continuous, smooth and thin edges in noisy images using constrained particle swarm optimisation}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {45--52}, keywords = {Ant colony optimization and swarm intelligence}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001584}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Detecting continuous edges is a hard problem especially in noisy images. We propose an algorithm based on particle swarm optimisation (PSO) to detect continuous and smooth edges in such images. A constrained PSO-based algorithm with a new penalised objective function and two constraints is proposed to overcome noise and reduce broken edges. The new algorithm is examined and compared with a modified version of the Canny algorithm, the robust rank order (RRO)-based algorithm, and an existing PSO-based algorithm on two sets of images with different types and levels of noise. The results suggest that the new algorithm detect edges more accurately than these three algorithms and the detected edges are smoother than those detected by the previous PSO algorithm and thinner than those detected by RRO.}, notes = {Also known as \cite{2001584} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{StephenChen:2011:GECCO, author = {Stephen Chen and James Montgomery}, title = {Selection strategies for initial positions and initial velocities in multi-optima particle swarms}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {53--60}, keywords = {Ant colony optimization and swarm intelligence}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001585}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Standard particle swarm optimisation cannot guarantee convergence to the global optimum in multi-modal search spaces, so multiple swarms can be useful. The multiple swarms all need initial positions and initial velocities for their particles. Several simple strategies to select initial positions and initial velocities are presented. A series of experiments isolates the effects of these selected initial positions and velocities compared to random initial positions and velocities. A first set of experiments shows how locust swarms benefit from scouting for initial positions and the use of initial velocities that launch away from the previous optimum. A second set of experiments show that the performance of WoSP (Waves of Swarm Particles) can be improved by using new search strategies to select the initial positions and initial velocities for the particles in its sub-swarms.}, notes = {Also known as \cite{2001585} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Pace:2011:GECCO, author = {Shannon S. Pace and Clinton J. Woodward}, title = {Diversity preservation using excited particle swarm optimisation}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {61--68}, keywords = {Ant colony optimization and swarm intelligence}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001586}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The particle swarm optimisation (PSO) algorithm suffers from the possibility of premature convergence. This problem has historically been addressed ab intra - manipulating velocity and swarm topology - yet the judicious addition of external mechanisms has been shown to adjust search behaviour to yield significantly improved results across many problems. This paper introduces an addition to the canonical particle swarm algorithm, designed to preserve the diversity typically lost by attraction to suboptimal positions. The proposed excited PSO method stimulates exploration upon the discovery of a candidate solution by manipulating the position to which particles are attracted. It is shown to maintain a suitable degree of diversity for the duration of an experiment, as well as an ability for self-scaling. Comparisons to the canonical PSO algorithm demonstrate improved solutions in both unimodal and multimodal spaces.}, notes = {Also known as \cite{2001586} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Zapotecas-Martinez:2011:GECCO, author = {Saul {Zapotecas Martinez} and Carlos A. {Coello Coello}}, title = {A multi-objective particle swarm optimizer based on decomposition}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {69--76}, keywords = {Ant colony optimization and swarm intelligence}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001587}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The simplicity and success of particle swarm optimisation (PSO) algorithms, has motivated researchers to extend the use of these techniques to the multi-objective optimisation field. This paper presents a multi-objective particle swarm optimisation (MOPSO) algorithm based on a decomposition approach, which is intended for solving continuous and unconstrained multi-objective optimization problems (MOPs). The proposed decomposition-based multi-objective particle swarm optimiser (dMOPSO), updates the position of each particle using a set of solutions considered as the global best according to the decomposition approach. dMOPSO is mainly characterised by the use of a memory reinitialisation process which aims to provide diversity to the swarm. Our proposed approach is compared with respect to two decomposition-based multi-objective evolutionary algorithms (MOEAs) which are representative of the state-of-the-art in the area. Our results indicate that our proposed approach is competitive and it outperforms the two MOEAs with respect to which it was compared in most of the test problems adopted.}, notes = {Also known as \cite{2001587} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Fu:2011:GECCO, author = {Wenlong Fu and Mark Johnston and Mengjie Zhang}, title = {Hybrid particle swarm optimisation based on history information sharing}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {77--84}, keywords = {Ant colony optimization and swarm intelligence}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001588}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Particle Swarm Optimisation (PSO) is an intelligent search method based on swarm intelligence and has been widely used in many fields. However it is also easily trapped in local optima. In order to find a global optimum, some evolutionary search operators used in multi-agent genetic algorithms are integrated into a novel hybrid PSO, with the expectation of effectively escaping from local optima. Particles share their history information and then update their positions using the latest and best history information. Some benchmark high-dimensional functions (from 20 to 10000 dimensions) are used to test the performance of the hybrid algorithms. The results demonstrate that the algorithm can solve high-dimensional nonlinear optimisation problems and that the number of function evaluations required to do so increases with function dimension at a sublinear rate.}, notes = {Also known as \cite{2001588} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Song:2011:GECCO, author = {Chunhe Song and Hai Zhao and Wei Jing and Hongbo Zhu}, title = {PSO based motion deblurring for single image}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {85--92}, keywords = {Ant colony optimization and swarm intelligence}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001589}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper addresses the issue of non-uniform motion deblurring due to hand shake for a single photograph. The main difficulty of spatially variant motion deblurring is that the deconvolution algorithm can not directly be used to estimate the blur kernel as the kernel of different pixels are different to each other. In this paper, the blurred image is considered as a weighed summation of all possible poses, and we proposed to use a PSO (particle swarm optimisation) to optimise the weighed parameters of the corresponding poses after building the motion model of the camera. The main issue of using a PSO for deblurring is that it is generally impossible to obtain the ground true of the observed blurred image, which must be used as the input of the PSO algorithm. To solve this problem, firstly a novel image prediction method is proposed which combines a shock filter and a non-linear structure tensor with anisotropic diffusion. The main advantage of the proposed prediction method is that the deblurring process is not misled by rich texture in the image. Secondly an alternatively optimising procedure is used to gradually refine the motion kernel and the latent image. Experimental results show that our approach makes it possible to model and remove non-uniform motion blur without hardware support.}, notes = {Also known as \cite{2001589} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Diwold:2011:GECCO, author = {Konrad Diwold and Daniel Himmelbach and Ren\'{e} Meier and Carsten Baldauf and Martin Middendorf}, title = {Bonding as a swarm: applying bee nest-site selection behaviour to protein docking}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {93--100}, keywords = {Ant colony optimization and swarm intelligence}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001590}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The identification of protein binding sites and the prediction of protein-ligand complexes play a key role in the pharmaceutical drug design process and many domains of life sciences. Computational approaches for protein-ligand docking (or molecular docking) have received increased attention over the last years as they allow inexpensive and fast prediction of protein-ligand complexes. Here we introduce the principle of Bee Nest-Site Selection Optimisation (BNSO), which solves optimisation problems using a novel scheme inspired by the nest-site selection behaviour found in honeybees. Moreover, the first BNSO algorithm -- Bee-Nest -- is proposed and applied to molecular docking. The performance of Bee-Nest is tested on 173 docking instances from the PDBbind core set and compared to the performance of three reference algorithms. The results show that Bee-Nest could find ligand poses with very small energy levels. Interestingly, the reference Particle Swarm Optimisation (PSO) produces results that are qualitatively closer to wet-lab experimentally derived complexes but have higher energy levels than the results found by Bee-Nest. Our results highlight the superior performance of Bee-Nest in semi-local optimization for the molecular docking problem and suggests Bee-Nest's usefulness in a hybrid strategy.}, notes = {Also known as \cite{2001590} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Marinakis:2011:GECCO, author = {Yannis Marinakis and Magdalene Marinaki}, title = {A honey bees mating optimization algorithm for the open vehicle routing problem}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {101--108}, keywords = {Ant colony optimization and swarm intelligence}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001591}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Honey Bees Mating Optimisation algorithm is a relatively new nature inspired algorithm. In this paper, this nature inspired algorithm is used in a hybrid scheme with other metaheuristic algorithms for successfully solving the Open Vehicle Routing Problem. More precisely, the proposed algorithm for the solution of the Open Vehicle Routing Problem, the Honey Bees Mating Optimization (HBMOOVRP), combines a Honey Bees Mating Optimization (HBMO) algorithm and the Expanding Neighbourhood Search (ENS) algorithm. Two set of benchmark instances is used in order to test the proposed algorithm. The results obtained for both sets are very satisfactory. More specifically, in the fourteen instances proposed by Christofides, the average quality is 0.35percent when a hierarchical objective function is used, where, first, the number of vehicles is minimised and, afterwards, the total travel distance is minimized and the average quality is 0.42percent when only the travel distance is minimized, while for the eight instances proposed by Li et al. when a hierarchical objective function is used the average quality is 0.21percent.}, notes = {Also known as \cite{2001591} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{El-Abd:2011:GECCO, author = {Mohammed El-Abd}, title = {Opposition-based artificial bee colony algorithm}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {109--116}, keywords = {Ant colony optimization and swarm intelligence}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001592}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The Artificial Bee Colony (ABC) algorithm is a relatively new algorithm for function optimisation. The algorithm is inspired by the foraging behaviour of honey bees. In this work, the performance of ABC is enhanced by introducing the concept of opposition-based learning. This concept is introduced through the initialisation step and through generation jumping. The performance of the proposed opposition-based ABC (OABC) is compared to the performance of ABC and opposition-based Differential Evolution (ODE) when applied to the Black-Box Optimization Benchmarking (BBOB) library introduced in the previous two GECCO conferences.}, notes = {Also known as \cite{2001592} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Dominguez:2011:GECCO, author = {Christian Dominguez and Nareli Cruz-Cort\'{e}s}, title = {Energy-efficient and location-aware ant colony based routing algorithms for wireless sensor networks}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {117--124}, keywords = {Ant colony optimization and swarm intelligence}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001593}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In recent years, advances in miniaturisation, low-power circuit design, simple, low power, yet reasonably efficient wireless communication equipment, and improved small-scale energy supplies have combined with reduced manufacturing costs to make a new technological vision possible, Wireless Sensor Networks (WSN). As WSN are still a young research field, much activity is still on-going to solve many open issues. One is the data routing problem. As the size of the network increases, this problem becomes more complex due the amount of sensor nodes in the network. The meta-heuristic Ant Colony Optimisation (ACO) has been proposed to solve this issue. ACO based routing algorithms can add a significant contribution to assist in the maximisation of the network lifetime and in the minimisation of the latency in data transmissions, but this is only possible by means of an adaptable and balanced algorithm that takes into account the WSN main restrictions, for example, memory and power supply. A comparison of two ACO based routing algorithms for WSN is presented, taking into account current amounts of energy consumption under a WSN scenario proposed in this work. Furthermore, a new routing algorithm is defined.}, notes = {Also known as \cite{2001593} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Liao:2011:GECCO, author = {Tianjun Liao and Marco A. {Montes de Oca} and Dogan Aydin and Thomas St\"{u}tzle and Marco Dorigo}, title = {An incremental ant colony algorithm with local search for continuous optimization}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {125--132}, keywords = {Ant colony optimization and swarm intelligence}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001594}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {ACOR is one of the most popular ant colony optimisation algorithms for tackling continuous optimization problems. In this paper, we propose IACOR-LS, which is a variant of ACOR that uses local search and that features a growing solution archive. We experiment with Powell's conjugate directions set, Powell's BOBYQA, and Lin-Yu Tseng's Mtsls1 methods as local search procedures. Automatic parameter tuning results show that IACOR-LS with Mtsls1 (IACOR-Mtsls1) is not only a significant improvement over ACOR, but that it is also competitive with the state-of-the-art algorithms described in a recent special issue of the Soft Computing journal. Further experimentation with IACOR-Mtsls1 on an extended benchmark functions suite, which includes functions from both the special issue of Soft Computing and the IEEE 2005 Congress on Evolutionary Computation, demonstrates its good performance on continuous optimisation problems.}, notes = {Also known as \cite{2001594} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Lin:2011:GECCO, author = {Ying Lin and Jing-Hui Zhong and Jun Zhang}, title = {Parallel exploitation in estimated basins of attraction: a new derivative-free optimization algorithm}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {133--138}, keywords = {Ant colony optimization and swarm intelligence}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001595}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Direct search (DS) and evolutionary algorithms (EAs) are two of the most representative branches of derivative-free optimisation methods. However, traditional DS becomes deficient in multimodal problems, while EAs suffer from long computational time due to the blind search caused by randomness in evolutionary operators. This paper proposes a new derivative-free optimisation algorithm that addresses both the above issues, avoiding prematurity while maintaining fast convergence speed. The new algorithm first estimates basins of attractions in the search space by analysing samples of the objective function. An adaptive exploitation method with the ability to predict promising search directions is then applied to search the estimated basins in parallel. The new algorithm is evaluated on both unimodal and multimodal benchmark functions. Experimental results show that the algorithm is a promising global optimiser with fast convergence speed.}, notes = {Also known as \cite{2001595} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Monteiro:2011:GECCO, author = {Marta S.R. Monteiro and Dalila B.M.M. Fontes and Fernando A.C.C. Fontes}, title = {An ant colony optimization algorithm to solve the minimum cost network flow problem with concave cost functions}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {139--146}, keywords = {Ant colony optimization and swarm intelligence}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001596}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this work we address the Singe-Source Uncapacitated Minimum Cost Network Flow Problem with concave cost functions. Given that this problem is of a combinatorial nature and also that the total costs are nonlinear, we propose a hybrid heuristic to solve it. In this type of algorithms one usually tries to manage two conflicting aspects of searching behaviour: exploration, the algorithm's ability to search broadly through the search space; and exploitation, the algorithm ability to search locally around good solutions that have been found previously. In our case, we use an Ant Colony Optimisation algorithm to mainly deal with the exploration, and a Local Search algorithm to cope with the exploitation of the search space. Our method proves to be very efficient while solving both small and large size problem instances. The problems we have used to test the algorithm were previously solved by other authors using other population based heuristics and our algorithm was able to improve upon their results, both in terms of computing time and solution quality.}, notes = {Also known as \cite{2001596} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Garca-Nieto:2011:GECCO, author = {Jose Garca-Nieto and Enrique Alba}, title = {Empirical computation of the quasi-optimal number of informants in particle swarm optimization}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {147--154}, keywords = {Ant colony optimization and swarm intelligence}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001597}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In the standard particle swarm optimisation (PSO), a new particle's position is generated using two main informant elements: the best position the particle has found so far and the best performer among its neighbours. In fully informed PSO, each particle is influenced by all the remaining ones in the swarm, or by a series of neighbours structured in static topologies (ring, square, or clusters). In this paper, we generalise and analyse the number of informants that take part in the calculation of new particles. Our aim is to discover if a quasi-optimal number of informants exists for a given problem. The experimental results seem to suggest that 6 to 8 informants could provide our PSO with higher chances of success in continuous optimisation for well-known benchmarks.}, notes = {Also known as \cite{2001597} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Wei-nengChen:2011:GECCO, author = {Wei-neng Chen and Jun Zhang}, title = {Ant colony optimization for determining the optimal dimension and delays in phase space reconstruction}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {155--162}, keywords = {Ant colony optimization and swarm intelligence}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001598}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The selection of parameters in time-delay embedding for phase space reconstruction is crucial to chaotic time series analysis and forecasting. Although various methods have been developed for determining the parameters of embedding dimension and time delay for uniform embedding, the study of parameter selection for non-uniform embedding is progressed at a slow pace. In a non-uniform embedding which enables different dimensions in the phase space to have different time delays, the optimal selection of time delays presents a difficult optimisation problem with combinatorial explosion. To solve this problem, this paper proposes an ant colony optimization (ACO) approach. The advantages of ACO for the embedding parameter selection problem are in two aspects. First, as ACO builds solution in an incremental way, it does not need to use a fixed embedding dimension as the encoding length of a solution. Instead, both the embedding dimension and the time delays can be optimised together. Second, ACO enables the use of problem-based heuristics. Therefore heuristics designed based on the original observed time series can be used to accelerate the search speed of ACO. Experimental results show that the proposed algorithm is promising.}, notes = {Also known as \cite{2001598} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Thiruvady:2011:GECCO, author = {Dhananjay Raghavan Thiruvady and Bernd Meyer and Andreas Ernst}, title = {Car sequencing with constraint-based ACO}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {163--170}, keywords = {Ant colony optimization and swarm intelligence}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001599}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Hybrid methods for solving combinatorial optimisation problems have become increasingly popular recently. The present paper is concerned with hybrids of ant colony optimisation and constraint programming which are typically useful for problems with hard constraints. However, the original algorithm suffered from large CPU time requirements. It was shown that such an integration can be made efficient via a further hybridisation with beam search resulting in CP-Beam-ACO. The original work suggested this in the context of job scheduling. We show here that this algorithm type is also effective on another problem class, namely the car sequencing. We consider an optimization version, where we aim to optimise the use rates across the sequence. Car sequencing is a notoriously difficult problem, because it is difficult to obtain good bounds via relaxations. We show that stochastic sampling provides superior results to well known lower bounds for this problem when combined with CP-Beam-ACO.}, notes = {Also known as \cite{2001599} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Karafotias:2011:GECCO, author = {Giorgos Karafotias and Evert Haasdijk and Agoston Endre Eiben}, title = {An algorithm for distributed on-line, on-board evolutionary robotics}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {171--178}, keywords = {Artificial life/robotics/evolvable hardware}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001601}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper presents part of an endeavour towards robots and robot collectives that can adapt their controllers autonomously and self-sufficiently and so independently learn to cope with situations unforeseen by their designers. We introduce the Embodied Distributed Evolutionary Algorithm (DEA) for on-line, on-board adaptation of robot controllers. We experimentally evaluate DEA using a number of well-known tasks in the evolutionary robotics field to determine whether it is a viable implementation of on-line, on-board evolution. We compare it to the encapsulated mu + 1 ON- LINE algorithm in terms of (the stability of) task performance and the sensitivity to parameter settings. Experiments show that DEA provides an effective method for on-line, on-board adaptation of robot controllers. Compared to mu + 1 ON- LINE, in terms of performance there is no clear winner, but in terms of sensitivity to parameter settings and stability of performance DEA is significantly better than mu + 1 ON- LINE.}, notes = {Also known as \cite{2001601} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Bongard:2011:GECCO, author = {Josh C. Bongard}, title = {Morphological and environmental scaffolding synergize when evolving robot controllers: artificial life/robotics/evolvable hardware}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {179--186}, keywords = {Artificial life/robotics/evolvable hardware}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001602}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Scaffolding---initially simplifying the task environment of autonomous robots---has been shown to increase the probability of evolving robots capable of performing in more complex task environments. Recently, it has been shown that changes to the body of a robot may also scaffold the evolution of non trivial behaviour. This raises the question of whether two different kinds of scaffolding (environmental and morphological) synergize with one another when combined. Here it is shown that, for legged robots evolved to perform phototaxis, synergy can be achieved, but only if morphological and environmental scaffolding are combined in a particular way: The robots must first undergo morphological scaffolding, followed by environmental scaffolding. This suggests that additional kinds of scaffolding may create additional synergies that lead to the evolution of increasingly complex robot behaviours.}, notes = {Also known as \cite{2001602} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Haasdijk:2011:GECCO, author = {Evert Haasdijk and Arif Atta-ul-Qayyum and Agoston Endre Eiben}, title = {Racing to improve on-line, on-board evolutionary robotics}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {187--194}, keywords = {Artificial life/robotics/evolvable hardware}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001603}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In evolutionary robotics, robot controllers are often evolved in a separate development phase preceding actual deployment - we call this off-line evolution. In on-line evolutionary robotics, by contrast, robot controllers adapt through evolution while the robots perform their proper tasks, not in a separate preliminary phase. In this case, individual robots can contain their own self-sufficient evolutionary algorithm (the encapsulated approach) where individuals are typically evaluated by means of a time sharing scheme: an individual is given the run of the robot for some amount of time and fitness corresponds to the robot's task performance in that period. Racing was originally introduced as a model selection procedure that quickly discards clearly inferior models. We propose and experimentally validate racing as a technique to cut short the evaluation of poor individuals before the regular evaluation period expires. This allows an increase of the number of individuals evaluated per time unit, but it also increases the robot's actual performance by virtue of abandoning controllers that perform inadequately. Our experiments show that racing can improve the performance of robots that adapt their controllers by means of an on-line evolutionary algorithm significantly.}, notes = {Also known as \cite{2001603} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Hamann:2011:GECCO, author = {Heiko Hamann and Thomas Schmickl and Karl Crailsheim}, title = {Coupled inverted pendulums: a benchmark for evolving decentral controllers in modular robotics}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {195--202}, keywords = {Artificial life/robotics/evolvable hardware}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001604}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The challenging scientific field of self-reconfiguring modular robotics (i.e., decentrally controlled 'super-robots' based on autonomous, interacting robot modules with variable morphologies) calls for novel paradigms of designing robot controllers. One option is the approach of evolutionary robotics. In this approach, the challenge is to achieve high evaluation numbers with the available resources which may even affect the feasibility of this approach. Simulations are usually applied at least in a preliminary stage of research to support controller design. However, even simulations are computationally expensive which gets even more burdensome once comprehensive studies and comparisons between different controller designs and approaches have to be done. Hence, a benchmark with low computational cost is needed that still contains the typical challenges of decentral control, is comparable, and easily manageable. We propose such a benchmark and report an empirical study of its characteristics including the transition from the single-robot setting to the multi-robot setting, typical local optima, and properties of adaptive walks through the fitness landscape.}, notes = {Also known as \cite{2001604} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Walker:2011:GECCO, author = {Jacob Charles Walker}, title = {The evolution of optimal foraging strategies in populations of digital organisms}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {203--210}, keywords = {Artificial life/robotics/evolvable hardware}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001605}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Foraging strategies in uncertain environments is the subject of a great deal of biological investigation, much of which is grounded in mathematical models. One theoretical prediction with wide empirical support is the ideal free distribution (IFD), where agents distribute themselves among patches of resources in proportion to their suitability. However, the IFD assumes that agents have perfect information of the environment. In nature, this assumption is often violated, yet the IFD is still observed. Insights into evolved mechanisms and behaviours that result in the IFD show how such efficient outcomes may emerge from little information. In this study, the artificial life platform Avida is used to observe populations of digital organisms as they evolved to optimise resource intake in an environment with unpredictable resource distributions. It is shown that the ideal free distribution can emerge from simple foraging strategies that require minimal information. It is demonstrated that this distribution is a result of choices made by the organisms, and not simply due to those in a more advantageous setting producing more offspring. Deviations from the IFD appear to be correlated with reduced information or foraging aggregation. Distributions with organisms of differing abilities are also investigated, demonstrating further correspondence with theoretical predictions.}, notes = {Also known as \cite{2001605} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Lehman:2011:GECCO, author = {Joel Lehman and Kenneth O. Stanley}, title = {Evolving a diversity of virtual creatures through novelty search and local competition}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {211--218}, keywords = {Artificial life/robotics/evolvable hardware}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001606}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {An ambitious challenge in artificial life is to craft an evolutionary process that discovers a wide diversity of well-adapted virtual creatures within a single run. Unlike in nature, evolving creatures in virtual worlds tend to converge to a single morphology because selection therein greedily rewards the morphology that is easiest to exploit. However, novelty search, a technique that explicitly rewards diverging, can potentially mitigate such convergence. Thus in this paper an existing creature evolution platform is extended with multi-objective search that balances drives for both novelty and performance. However, there are different ways to combine performance-driven search and novelty search. The suggested approach is to provide evolution with both a novelty objective that encourages diverse morphologies and a local competition objective that rewards individuals outperforming those most similar in morphology. The results in an experiment evolving moving virtual creatures show that novelty search with local competition discovers more functional morphological diversity within a single run than models with global competition, which are more predisposed to converge. The conclusions are that novelty search with local competition may complement recent advances in evolving virtual creatures and may in general be a principled approach to combining novelty search with pressure to achieve.}, notes = {Also known as \cite{2001606} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Zaman:2011:GECCO, author = {Luis Zaman and Suhas Devangam and Charles Ofria}, title = {Rapid host-parasite coevolution drives the production and maintenance of diversity in digital organisms}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {219--226}, keywords = {Artificial life/robotics/evolvable hardware}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001607}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Accumulating evidence suggests evolution and ecology can happen on similar time scales. Coevolution between hosts and parasites is a practical example of interacting ecological and evolutionary dynamics. Antagonistic interactions theoretically and experimentally increase host diversity, but the contribution of novel variation to diversity is not well understood. In laboratory or natural settings it is infeasible to prohibit novel mutations in communities while still allowing frequencies of extant organisms to change. We turn to digital organisms to investigate the effects of rapid evolution on host-parasite community diversity in the presence and absence of novel variation. We remove the source of variation in coevolved digital host-parasite communities and allow them to reach an equilibrium. We find that coevolved host-parasite communities are surprisingly stable in the absence of new variation. However, the communities at equilibrium are less diverse than those that continued to experience mutations. In either case, hosts coevolving with parasites are significantly more diverse than hosts evolving alone. Harnessing an advantage of in silico evolution, we show that novel variation increases host diversity in communities with parasites further than the trivial increase expected from new mutations.}, notes = {Also known as \cite{2001607} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Connelly:2011:GECCO, author = {Brian D. Connelly and Luis Zaman and Philip K. McKinley and Charles Ofria}, title = {Modeling the evolutionary dynamics of plasmids in spatial populations}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {227--234}, keywords = {Artificial life/robotics/evolvable hardware}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001608}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {One of the processes by which microorganisms are able to rapidly adapt to changing conditions is horizontal gene transfer, whereby an organism incorporates additional genetic material from sources other than its parent. These genetic elements may encode a wide variety of beneficial traits. Under certain conditions, many computational models capture the evolutionary dynamics of adaptive behaviours such as toxin production, quorum sensing, and biofilm formation, and have even provided new insights into otherwise unknown or misunderstood phenomena. However, such models rarely incorporate horizontal gene transfer, so they may be incapable of fully representing the vast repertoire of behaviors exhibited by natural populations. Although models of horizontal gene transfer exist, they rarely account for the spatial structure of populations, which is often critical to adaptive behaviors. In this work we develop a spatial model to examine how conjugation, one mechanism of horizontal gene transfer, can be maintained in populations. We investigate how both the costs of transfer and the benefits conferred affect evolutionary outcomes. Further, we examine how rates of transmission evolve, allowing this system to adapt to different environments. Through spatial models such as these, we can gain a greater understanding of the conditions under which horizontally-acquired behaviours are evolved and are maintained.}, notes = {Also known as \cite{2001608} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Jones:2011:GECCO, author = {Ben Jones and Andrea Soltoggio and Bernhard Sendhoff and Xin Yao}, title = {Evolution of neural symmetry and its coupled alignment to body plan morphology}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {235--242}, keywords = {Artificial life/robotics/evolvable hardware}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001609}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Body morphology is thought to have heavily influenced the evolution of neural architecture. However, the extent of this interaction and its underlying principles are largely unclear. To help us elucidate these principles, we examine the artificial evolution of a hypothetical nervous system embedded in a fish-inspired animat. The aim is to observe the evolution of neural structures in relation to both body morphology and required motor primitives. Our investigations reveal that increasing the pressure to evolve a wider range of movements also results in higher levels of neural symmetry. We further examine how different body shapes affect the evolution of neural structure; we find that, in order to achieve optimal movements, the neural structure integrates and compensates for asymmetrical body morphology. Our study clearly indicates that different parts of the animat - specifically, nervous system and body plan - evolve in concert with and become highly functional with respect to the other parts. The autonomous emergence of morphological and neural computation in this model contributes to unveiling the surprisingly strong coupling of such systems in nature.}, notes = {Also known as \cite{2001609} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Byers:2011:GECCO, author = {Chad M. Byers and Betty H.C. Cheng and Philip K. McKinley}, title = {Digital enzymes: agents of reaction inside robotic controllers for the foraging problem}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {243--250}, keywords = {genetic algorithms, genetic programming, Artificial life/robotics/evolvable hardware}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001610}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Over billions of years, natural selection has continued to select for a framework based on (1) parallelism and (2) cooperation across various levels of organisation within organisms to drive their behaviours and responses. We present a design for a bottom-up, reactive controller where the agent's response emerges from many parallelled, enzymatic interactions (bottom-up) within the biologically-inspired process of signal transduction (reactive). We use enzymes to explore the potential for evolving simulated robot controllers for the central-place foraging problem. The properties of the robot and stimuli present in its environment are encoded in a digital format (molecule) capable of being manipulated and altered through self-contained computational programs (enzymes) executing in parallel inside each controller to produce the robot's foraging behaviour. Evaluation of this design in unbounded worlds reveals evolved strategies employing one or more of the following complex behaviors: (1) swarming, (2) coordinated movement, (3) communication of concepts using a primitive language based on sound and colour, (4) cooperation, and (5) division of labour.}, notes = {Also known as \cite{2001610} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Bongard:2011:GECCOa, author = {Josh C. Bongard}, title = {Spontaneous evolution of structural modularity in robot neural network controllers: artificial life/robotics/evolvable hardware}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {251--258}, keywords = {Artificial life/robotics/evolvable hardware}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001611}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In order to evolve large robot controllers for increasingly complex tasks, fully connected neural networks are not feasible. However, manually designing sparse neural connectivity is not intuitive, and thus should be placed under evolutionary control. Here I show how spontaneous structural modularity can arise in the connectivity of evolved robot controllers if the controllers are Boolean networks, and are selected to converge on point attractors that correspond to successful robot behaviours.}, notes = {Also known as \cite{2001611} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Pinville:2011:GECCO, author = {Tony Pinville and Sylvain Koos and Jean-Baptiste Mouret and St\'{e}phane Doncieux}, title = {How to promote generalisation in evolutionary robotics: the ProGAb approach}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {259--266}, keywords = {Artificial life/robotics/evolvable hardware}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001612}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In Evolutionary Robotics (ER), controllers are assessed in a single or a few environments. As a consequence, good performances in new different contexts are not guaranteed. While a lot of ER works deal with robustness, i.e. the ability to perform well on new contexts close to the ones used for evaluation, no current approach is able to promote broader generalisation abilities without any assumption on the new contexts. In this paper, we introduce the ProGAb approach, which is based on the standard three data sets methodology of supervised machine learning, and compare it to state-of-the-art ER methods on two simulated robotic tasks: a navigation task in a T-maze and a more complex ball-collecting task in an arena. In both applications, the ProGAb approach: (1) produced controllers with better generalisation abilities than the other methods; (2) needed two to three times fewer evaluations to discover such solutions.}, notes = {Also known as \cite{2001612} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Ollion:2011:GECCO, author = {Charles Ollion and St\'{e}phane Doncieux}, title = {Why and how to measure exploration in behavioral space}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {267--274}, keywords = {Artificial life/robotics/evolvable hardware}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001613}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Exploration and exploitation are two complementary aspects of Evolutionary Algorithms. Exploration, in particular, is promoted by specific diversity keeping mechanisms generally relying on the genotype or the fitness value. Recent works suggest that, in the case of Evolutionary Robotics or more generally behavioural system evolution, promoting exploration directly in the behavioural space is of critical importance. In this work an exploration indicator is proposed, based on the sparseness of the population in the behavioural space. This exploration measure is used on two challenging neuro-evolution experiments and validated by showing the dependence of the fitness at the end of the run on the exploration measure during the very first generations. Such a prediction ability could be used to design parameter settings algorithms or selection algorithms dedicated to the evolution of behavioral systems. Several other potential uses of this measure are also proposed and discussed.}, notes = {Also known as \cite{2001613} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Roth:2011:GECCO, author = {Christian Roth and Matt Knudson and Kagan Tumer}, title = {Agent fitness functions for evolving coordinated sensor networks}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {275--282}, keywords = {Artificial life/robotics/evolvable hardware}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001614}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Distributed sensor networks are an attractive area for research in agent systems. This is due primarily to the level of information available in applications where sensing technology has improved dramatically. These include energy systems and area coverage where it is desirable for sensor networks to have the ability to self-organise and be robust to changes in network structure. The challenges presented when investigating distributed sensor networks for such applications include the need for small sensor packages that are still capable of making good decisions to cover areas where multiple types of information may be present. For example in energy systems, singular areas in power plants may produce several types of valuable information, such as temperature, pressure, or chemical indicators. The approach of the work presented in this paper provides agent fitness functions for use with a neuro-evolutionary algorithm to address some of these challenges. In particular, we show that for self-organisation and robustness to network changes, it is more advantageous to evolve individual policies, rather than a shared policy that all sensor units use. Further, we show that using a difference objective approach to the decomposition of system-level fitness functions provides a better target for evolving these individual policies. This is because the difference evaluation for fitness provides a cleaner signal, while maintaining vital information from the system level that implicitly promotes coordination among individual sensor units in the network.}, notes = {Also known as \cite{2001614} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Reid:2011:GECCO, author = {Fergal Reid and Neil Hurley}, title = {Analysing structure in complex networks using quality functions evolved by genetic programming}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {283--290}, keywords = {genetic algorithms, genetic programming, Bioinformatics, computational, systems, and synthetic biology}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001616}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {When studying complex networks, we are often interested in identifying structures within the networks. Previous work has successfully used algorithmically identified network structures to predict functional groups; for example, where structures extracted from protein-protein interaction networks have been predictive of functional protein complexes. One way structures in complex networks have previously been described is as collections of nodes that maximise a local quality function. For a particular set of structures, we search the space of quality functions using Genetic Programming, to find a function that locally describes that set of structures. This technique allows us to investigate the common network properties of defined sets of structures. We also use this technique to classify and differentiate between different types of structure. We apply this method on several synthetic benchmarks, and on a protein-protein interaction network. Our results indicate this is a useful technique of investigating properties that sets of network structures have in common.}, notes = {Also known as \cite{2001616} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Mendoza:2011:GECCO, author = {Mariana Recamonde Mendoza and Ana L\'{u}cia C. Bazzan}, title = {Evolving random boolean networks with genetic algorithms for regulatory networks reconstruction}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {291--298}, keywords = {Bioinformatics, computational, systems, and synthetic biology}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001617}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The discovery of the structure of genetic regulatory networks is of great interest for biologists and geneticists due to its pivotal role in organisms' metabolism. In the present paper we aim to investigate the inference power of genetic regulatory networks modelled as random Boolean networks without the use of any prior biological information. The solutions space is explored by means of genetic algorithms, whose main goal is to find a consistent network given the target data obtained from biological experiments. We show that this approach succeeds in reconstructing a model with satisfactory level of accuracy, representing an useful tool to guide biologist towards the most probable interactions between the target genes.}, notes = {Also known as \cite{2001617} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Hardison:2011:GECCO, author = {Nicholas E. Hardison and Alison A. Motsinger-Reif}, title = {The power of quantitative grammatical evolution neural networks to detect gene-gene interactions}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {299--306}, keywords = {genetic algorithms, genetic programming, grammatical evolution, Bioinformatics, computational, systems, and synthetic biology}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001618}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Applying grammatical evolution to evolve neural networks (GENN) has been increasing used in genetic epidemiology to detect gene-gene or gene-environment interactions, also known as epistasis, in high dimensional data. GENN approaches have previously been shown to be highly successful in a range of simulated and real case-control studies, and has recently been applied to quantitative traits. In the current study, we evaluate the potential of an application of GENN to quantitative traits (QTGENN) to a range of simulated genetic models. We demonstrate the power of the approach, and compare this power to more traditional linear regression analysis approaches. We find that the QTGENN approach has relatively high power to detect both single-locus models as well as several completely epistatic two-locus models, and favourably compares to the regression methods.}, notes = {Also known as \cite{2001618} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Schmidt:2011:GECCO, author = {Michael Douglas Schmidt and Hod Lipson}, title = {Automated modeling of stochastic reactions with large measurement time-gaps}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {307--314}, keywords = {Bioinformatics, computational, systems, and synthetic biology}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001619}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Many systems, particularly in biology and chemistry, involve the interaction of discrete quantities, such as individual elements or molecules. When the total number of elements in the system is low, the impact of individual reactions becomes non-negligible and modelling requires the simulation of exact sequences of reactions. In this paper, we introduce an algorithm that can infer an exact stochastic reaction model based on sparse measurements of an evolving system of discrete quantities. The algorithm is based on simulating a candidate model to maximise the likelihood of the data. When the likelihood is too small to provide a search gradient, the algorithm uses the distance of the data to the model's estimated distribution. Results show that this method infers stochastic models reliably with both short time gaps between measurements of the system, and long time gaps where the system state has evolved qualitatively far between each measurement. Furthermore, the proposed metric outperforms optimising on likelihood or distance components alone. Traits measured on the search novelty, age, and bloat suggest that this algorithm scales well to increasingly complex systems.}, notes = {Also known as \cite{2001619} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Kubalik:2011:GECCO, author = {Jiri Kubalik}, title = {Evolutionary-based iterative local search algorithm for the shortest common supersequence problem}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {315--322}, keywords = {Bioinformatics, computational, systems, and synthetic biology}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001620}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The Shortest Common Supersequence (SCS) problem is a well-known hard combinatorial optimisation problem with applications in many areas. This paper presents two extensions of recently proposed evolutionary-based iterative local search algorithm called POEMS for solving the SCS problem. Both extensions improve scalability of the algorithm. The first one improves the efficiency of the evaluation procedure and the second one further improves optimization capabilities of the algorithm by intensifying the search towards short supersequence already during the process of constructing the valid supersequence. A moderate size benchmark was used for the proof-of-concept experiments while two very large biological benchmarks were used to demonstrate the capability of the proposed approach. The proposed algorithm performs very well on all of the benchmarks. Moreover, it produces significantly better solutions than the baseline Deposition and Reduction algorithm on the two challenging large benchmarks.}, notes = {Also known as \cite{2001620} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Zaki:2011:GECCO, author = {Nazar Zaki and Fadi Sibai and Piers Campbell}, title = {Conotoxin protein classification using pairwise comparison and amino acid composition: toxin-aam}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {323--330}, keywords = {Bioinformatics, computational, systems, and synthetic biology}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001621}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Conotoxin classification could assist in the study of the structure function relationship of ion-channels and receptors as well as identifying potential therapeutics in the treatment of a wide variety of diseases such as schizophrenia, chronic pain, cardiovascular and bladder dysfunction. In this study, we introduce a novel method (Toxin-AAM) for conotoxin superfamily classification. Toxin-AAM incorporates evolutionary information using a powerful means of pairwise sequence comparison and amino acid composition knowledge. The combination of the sequential model and the discrete model has made the Toxin-AAM method exceptional in classifying conotoxin superfamily, when compared to other state-of-the-art techniques.}, notes = {Also known as \cite{2001621} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Santana:2011:GECCO, author = {Roberto Santana and Concha Bielza and Pedro Larranaga}, title = {Affinity propagation enhanced by estimation of distribution algorithms}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {331--338}, keywords = {Bioinformatics, computational, systems, and synthetic biology}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001622}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Tumour classification based on gene expression data can be applied to set appropriate medical treatment according to the specific tumor characteristics. In this paper we propose the use of estimation of distribution algorithms (EDAs) to enhance the performance of affinity propagation (AP) in classification problems. AP is an efficient clustering algorithm based on message-passing methods and which automatically identifies exemplars of each cluster. We introduce an EDA-based procedure to compute the preferences used by the AP algorithm. Our results show that AP performance can be notably improved by using the introduced approach. Furthermore, we present evidence that classification of new data is improved by employing previously identified exemplars with only minor decrease in classification accuracy.}, notes = {Also known as \cite{2001622} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Marcozzi:2011:GECCO, author = {Micha\"{e}l Marcozzi and Federico Divina and Jes\'{u}s S. Aguilar-Ruiz and Wim Vanhoof}, title = {A novel probabilistic encoding for EAs applied to biclustering of microarray data}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {339--346}, keywords = {Bioinformatics, computational, systems, and synthetic biology}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001623}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper we propose a novel representation scheme, called probabilistic encoding. In this representation, each gene of an individual represents the probability that a certain trait of a given problem has to belong to the solution. This allows to deal with uncertainty that can be present in an optimisation problem, and grant more exploration capability to an evolutionary algorithm. With this encoding, the search is not restricted to points of the search space. Instead, whole regions are searched, with the aim of individuating a promising region, i.e., a region that contains the optimal solution. This implies that a strategy for searching the individuated region has to be adopted. In this paper we incorporate the probabilistic encoding into a multi-objective and multi-modal evolutionary algorithm. The algorithm returns a promising region, which is then searched by using simulated annealing. We apply our proposal to the problem of discovering biclusters in microarray data. Results confirm the validity of our proposal.}, notes = {Also known as \cite{2001623} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Zaki:2011:GECCOh, author = {Nazar Zaki and Salah Bouktif and Sanja Lazarova-Molnar}, title = {A genetic algorithm to enhance transmembrane helices prediction}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {347--354}, keywords = {Bioinformatics, computational, systems, and synthetic biology}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001624}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {A transmembrane helix (TMH) topology prediction is becoming a central problem in bioinformatics because the structure of TM proteins is difficult to determine by experimental means. Therefore, methods which could predict the TMHs topologies computationally are highly desired. In this paper we introduce TMHindex, a method for detecting TMH segments solely by the amino acid sequence information. Each amino acid in a protein sequence is represented by a Compositional Index deduced from a combination of the difference in amino acid appearances in TMH and non-TMH segments in training protein sequences and the amino acid composition information. Furthermore, genetic algorithm was employed to find the optimal threshold value to separate TMH segments from non-TMH segments. The method successfully predicted 376 out of the 378 TMH segments in 70 testing protein sequences. The level of accuracy achieved using TMHindex in comparison to recent methods for predicting the topology of TM proteins is a strong argument in favour of our method.}, notes = {Also known as \cite{2001624} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Krawiec:2011:GECCOn, author = {Krzysztof Krawiec and Marcin Grzegorz Szubert}, title = {Learning n-tuple networks for othello by coevolutionary gradient search}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {355--362}, keywords = {Digital entertainment technologies and arts}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001626}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We propose Coevolutionary Gradient Search, a blueprint for a family of iterative learning algorithms that combine elements of local search and population-based search. The approach is applied to learning Othello strategies represented as n-tuple networks, using different search operators and modes of learning. We focus on the interplay between the continuous, directed, gradient-based search in the space of weights, and fitness-driven, combinatorial, coevolutionary search in the space of entire n-tuple networks. In an extensive experiment, we assess both the objective and relative performance of algorithms, concluding that the hybridisation of search techniques improves the convergence. The best algorithms not only learn faster than constituent methods alone, but also produce top ranked strategies in the online Othello League.}, notes = {Also known as \cite{2001626} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Raffe:2011:GECCO, author = {William L. Raffe and Fabio Zambetta and Xiaodong Li}, title = {Evolving patch-based terrains for use in video games}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {363--370}, keywords = {Digital entertainment technologies and arts}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001627}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Procedurally generating content for video games is gaining interest as an approach to mitigate rising development costs and meet users' expectations for a broader range of experiences. This paper explores the use of evolutionary algorithms to aid in the content generation process, especially the creation of three-dimensional terrain. We outline a prototype for the generation of in-game terrain by compiling smaller height-map patches that have been extracted from sample maps. Evolutionary algorithms are applied to this generation process by using crossover and mutation to evolve the layout of the patches. This paper demonstrates the benefits of an interactive two-level parent selection mechanism as well as how to seamlessly stitch patches of terrain together. This unique patch-based terrain model enhances control over the evolution process, allowing for terrain to be refined more intuitively to meet the user's expectations.}, notes = {Also known as \cite{2001627} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Karpov:2011:GECCO, author = {Igor V. Karpov and Vinod K. Valsalam and Risto Miikkulainen}, title = {Human-assisted neuroevolution through shaping, advice and examples}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {371--378}, keywords = {Digital entertainment technologies and arts}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001628}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Many different methods for combining human expertise with machine learning in general, and evolutionary computation in particular, are possible. Which of these methods work best, and do they outperform human design and machine design alone? In order to answer this question, a human-subject experiment for comparing human-assisted machine learning methods was conducted. Three different approaches, i.e. advice, shaping, and demonstration, were employed to assist a powerful machine learning technique (neuroevolution) on a collection of agent training tasks, and contrasted with both a completely manual approach (scripting) and a completely hands-off one (neuroevolution alone). The results show that, (1) human-assisted evolution outperforms a manual scripting approach, (2) unassisted evolution performs consistently well across domains, and (3) different methods of assisting neuroevolution outperform unassisted evolution on different tasks. If done right, human-assisted neuroevolution can therefore be a powerful technique for constructing intelligent agents.}, notes = {Also known as \cite{2001628} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Gauci:2011:GECCO, author = {Jason Gauci and Kenneth O. Stanley}, title = {Evolving neural networks for geometric game-tree pruning}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {379--386}, keywords = {Digital entertainment technologies and arts}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001629}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Abstract Game-tree search is the engine behind many computer game opponents. Traditional game-tree search algorithms decide which move to make based on simulating actions, evaluating future board states, and then applying the evaluations to estimate optimal play by all players. Yet the limiting factor of such algorithms is that the search space increases exponentially with the number of actions taken (i.e. the depth of the search). More recent research in game-tree search has revealed that even more important than evaluating future board states is effective pruning of the search space. Accordingly, this paper discusses Geometric Game-Tree Pruning (GGTP), a novel evolutionary method that learns to prune game trees based on geometric properties of the game board. The experiment compares Cake, a minimax-based game-tree search algorithm, with HyperNEAT-Cake, the original Cake algorithm combined with an indirectly encoded, evolved GGTP algorithm. The results show that HyperNEAT-Cake wins significantly more games than regular Cake playing against itself.}, notes = {Also known as \cite{2001629} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Hoover:2011:GECCO, author = {Amy K. Hoover and Paul A. Szerlip and Kenneth O. Stanley}, title = {Interactively evolving harmonies through functional scaffolding}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {387--394}, keywords = {Digital entertainment technologies and arts}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001630}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {While the real-time focus of today's automated accompaniment generators can benefit instrumentalists and vocalists in their practice, improvisation, or performance, an opportunity remains specifically to assist novice composers. This paper introduces a novel such approach based on evolutionary computation called functional scaffolding for musical composition (FSMC), which helps the user explore potential accompaniments for existing musical pieces, or scaffolds. The key idea is to produce accompaniment as a function of the scaffold, thereby inheriting from its inherent style and texture. To implement this idea, accompaniments are represented by a special type of neural network called a compositional pattern producing network (CPPN), which produces harmonies by elaborating on and exploiting regularities in pitches and rhythms found in the scaffold. This paper focuses on how inexperienced composers can personalise accompaniments by first choosing any MIDI scaffold, then selecting which parts (e.g. the piano, guitar, or bass guitar) the CPPN can hear, and finally customising and refining the computer-generated accompaniment through an interactive process of selection and mutation of CPPNs called interactive evolutionary computation (IEC). The potential of this approach is demonstrated by following the evolution of a specific accompaniment and studying whether listeners appreciate the results.}, notes = {Also known as \cite{2001630} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Cardamone:2011:GECCO, author = {Luigi Cardamone and Daniele Loiacono and Pier Luca Lanzi}, title = {Interactive evolution for the procedural generation of tracks in a high-end racing game}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {395--402}, keywords = {Digital entertainment technologies and arts}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001631}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We present a framework for the procedural generation of tracks for a high-end car racing game (TORCS) using interactive evolution. The framework maintains multiple populations and allow users to work both on their own population (in single-user mode) or to collaborate with other users on a shared population. Our architecture comprises a web frontend and an evolutionary backend. The former manages the interaction with users (e.g., logs registered and anonymous users, collects evaluations, provides access to all the evolved populations) and maintains the database server that stores all the present/past populations. The latter runs all the tasks related to evolution (selection, recombination and mutation) and all the tasks related to the target racing game (e.g., the track generation). We performed two sets of experiments involving five human subjects to evolve racing tracks alone (in a single-user mode) or cooperatively. Our preliminary results on five human subjects show that, in all the experiments, there is an increase of users' satisfaction as the evolution proceeds. Users stated that they perceived improvements in the quality of the individuals between subsequent populations and that, at the end, the process produced interesting tracks.}, notes = {Also known as \cite{2001631} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{McDermott:2011:GECCO, author = {James McDermott and Una-May O'Reilly}, title = {An executable graph representation for evolutionary generative music}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {403--410}, keywords = {genetic algorithms, genetic programming, grammatical evolution, Digital entertainment technologies and arts}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001632}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We focus on a representation for evolutionary music based on executable graphs in which nodes execute arithmetic functions. Input nodes supply time variables and abstract control variables, and multiple output nodes are mapped to MIDI data. The motivation is that multiple outputs from a single graph should tend to behave in related ways, a key characteristic of good music. While the graph itself determines the short-term behaviour of the music, the control variables can be used to specify large-scale musical structure. This separation of music into form and content enables novel compositional techniques well-suited to writing for games and film, as well as for standalone pieces. A mapping from integer-array genotypes to executable graph phenotypes means that evolution, both interactive and non-interactive, can be applied. Experiments with and without human listeners support several specific claims concerning the system's benefits.}, notes = {Also known as \cite{2001632} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Vatolkin:2011:GECCO, author = {Igor Vatolkin and Mike Preu\ss and G\"{u}nter Rudolph}, title = {Multi-objective feature selection in music genre and style recognition tasks}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {411--418}, keywords = {Digital entertainment technologies and arts}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001633}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Feature selection is an important prerequisite for music classification which in turn is becoming more and more ubiquitous since entering the digital music age. Automated classification into genres or even personal categories is currently envisioned even for standard mobile devices. However, classifiers often fail to work well with all available features, and simple greedy methods often fail to select good feature sets, making feature selection for music classification a natural field of application for evolutionary approaches in general, and multi-objective evolutionary algorithms in particular. In this work, we study the potential of applying such a multi-objective evolutionary optimisation algorithm for feature selection with different objective sets. The result is promising, thus calling for deeper investigations of this approach.}, notes = {Also known as \cite{2001633} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{deFreitas:2011:GECCO, author = {Alan R.R. {de Freitas} and Frederico Gadelha Guimaraes}, title = {Originality and diversity in the artificial evolution of melodies}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {419--426}, keywords = {Digital entertainment technologies and arts}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001634}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {One of the greatest problems when using genetic algorithms to evolve melodies is creating an aesthetically conscious measure of fitness. In this paper, we describe a new approach with a minimum measure of fitness in which a set of good individuals is returned at the end of the process. Details about the implementation of a population of measures and some genetic operators are described in this work before an implicit way to evaluate fitness is given. We define a Takeover Matrix to measure the relationship between different generations and its compromise between originality and diversity. By means of this Takeover Matrix, the evolutionary process itself can be used as a criterion instead of using only ordinary individual measures of fitness. The results show the implications of using the proposed approach and demonstrate that the proposed algorithm is able to generate good sets of melodies. The algorithm can be used not only for developing new ideas but also to extend earlier created melodies with influence from the initial population.}, notes = {Also known as \cite{2001634} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{denHeijer:2011:GECCO, author = {Eelco {den Heijer} and Agoston Endre Eiben}, title = {Evolving art with scalable vector graphics}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {427--434}, keywords = {genetic algorithms, genetic programming, grammatical evolution, Digital entertainment technologies and arts}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001635}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper we introduce the use of Scalable Vector Graphics (SVG) as a representation for evolutionary art. We describe the technical aspects of using SVG in evolutionary art, and explain the genetic operators mutation and crossover. Furthermore, we compare the use of SVG with existing representations in evolutionary art. We performed a number of experiments in an unsupervised evolutionary art system using two aesthetic measures as fitness functions, and compared the outcome of the different experiments with each other and with previous work with symbolic expressions as the representation. All images and SVG code examples in this paper are available at http://www.few.vu.nl/~eelco}, notes = {Also known as \cite{2001635} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Qaurooni:2011:GECCO, author = {Dan Qaurooni}, title = {A memetic algorithm for course timetabling}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {435--442}, keywords = {Evolutionary combinatorial optimization and metaheuristics}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001637}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Course timetabling consists in scheduling a sequence of lectures while satisfying various constraints. In this paper, we develop and study the performance of a algorithm, designed to solve a variant of the course timetabling problem. Our aim here is twofold: to develop a competitive algorithm, and to investigate, more generally, the applicability of evolutionary algorithms to timetabling. To this end, an algorithm is first introduced and tested using a benchmark set. Comparison with other algorithms shows that our algorithm achieves better results in some, but not all instances, signifying strong and weak points. Subsequently, more comprehensive analyses are performed in relation with another evolutionary algorithm that uses strictly group-based operators. Ultimately, empirical results and analyses lead us to question the exclusive use of group-based evolutionary operators for timetabling problems.}, notes = {Also known as \cite{2001637} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Toledo:2011:GECCO, author = {Claudio Fabiano Motta Toledo and M\'{a}rcio da Silva Arantes and Paulo Morelato Fran\c{c}a}, title = {Tabu search to solve the synchronized and integrated two-level lot sizing and scheduling problem}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {443--448}, keywords = {Evolutionary combinatorial optimization and metaheuristics}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001638}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper proposes a tabu search approach to solve the Synchronized and Integrated Two-Level Lot Sizing and Scheduling Problem (SITLSP). It is a real-world problem, often found in soft drink companies, where the production process has two integrated levels with decisions concerning raw material storage and soft drink bottling. Lot sizing and scheduling of raw materials in tanks and products in bottling lines must be simultaneously determined. Real data provided by a soft drink company is used to make comparisons with a previous genetic algorithm. Computational results have demonstrated that tabu search outperformed genetic algorithm in all instances.}, notes = {Also known as \cite{2001638} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Bicalho:2011:GECCO, author = {Lu\'{\i}s H.C. Bicalho and Andr\'{e} G. {dos Santos} and Jos\'{e} E.C. Arroyo}, title = {Metaheuristic for parallel machines scheduling with resource-assignable sequence dependent setup times}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {449--456}, keywords = {Evolutionary combinatorial optimization and metaheuristics}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001639}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper, we describe and show the results of a combination of two metaheuristics to solve an unrelated parallel machines scheduling problem in which the setup times depend not only on the machine and job sequence, but also on the amount of resource assigned. This problem has been proposed recently on the literature and since then a couple of metaheuristics have been used to address it. The one proposed here, called GTS, consists of two phases: initially, some solutions are generated by the GRASP metaheuristic; subsequently, the Tabu Search (TS) is applied in the best solution found by GRASP. The numerical experiments show that the GTS heuristic was able to improve the results in 70percent (251 out of 360) of the larger instances available in the literature.}, notes = {Also known as \cite{2001639} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Mouhoub:2011:GECCO, author = {Malek Mouhoub and Bahareh Jafari}, title = {Heuristic techniques for variable and value ordering in CSPs}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {457--464}, keywords = {Evolutionary combinatorial optimization and metaheuristics}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001640}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {A Constraint Satisfaction Problem (CSP) is a powerful framework for representing and solving constraint problems. When solving a CSP using a backtrack search method, one important factor that reduces the size of the search space drastically is the order in which variables and values are examined. Many heuristics for static and dynamic variable ordering have been proposed and the most popular and powerful are those that gather information about the failures during the constraint propagation phase, in the form of constraint weights. These later heuristics are called conflict driven heuristics. In this paper, we propose two of these heuristics respectively based on Hill Climbing (HC) and Ant Colony Optimisation (ACO) for weighing constraints. In addition, we propose two new value ordering techniques, respectively based on HC and ACO, that rank the values based on their ability to satisfy the constraints attached to their corresponding variables. Several experiments were conducted on various types of problems including random, quasi random and patterned problems. The results show that the proposed variable ordering heuristics, are successful especially in the case of hard random problems. Also, when using the proposed value and variable ordering together, we can improve the performance particularly in the case of random problems.}, notes = {Also known as \cite{2001640} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Chalupa:2011:GECCO, author = {David Chalupa}, title = {Population-based and learning-based metaheuristic algorithms for the graph coloring problem}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {465--472}, keywords = {Evolutionary combinatorial optimization and metaheuristics}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001641}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper, two new metaheuristic algorithms for the graph colouring problem are introduced. The first one is a population-based multiagent evolutionary algorithm (MEA), using a multiagent system, where an agent represents a tabu search procedure. Rather than using a single long-term local search procedure, it uses more agents representing short term local search procedures. Instead of a specific crossover, MEA uses relatively general mechanisms from artificial life, such as lifespans and elite list [3, 4]. We are introducing and investigating a new parametrisation system, along with a mechanism of reward and punishment for agents according to change in their fitness. The second algorithm is a pseudo-reactive tabu search (PRTS), introducing a new online learning strategy to balance its own parameter settings. Basically, it is inspired by the idea to learn tabu tenure parameters instead of using constants. Both algorithms empirically outperform basic tabu search algorithm TabuCol [8] on the well-established DIMACS instances [10]. However, they achieve this by using different strategies. This indeed shows a difference in potential of population-based and learning-based graph coloring metaheuristics.}, notes = {Also known as \cite{2001641} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{JinKim:2011:GECCO, author = {Jin Kim and Inwook Hwang and Yong-Hyuk Kim and Byung-Ro Moon}, title = {Genetic approaches for graph partitioning: a survey}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {473--480}, keywords = {Evolutionary combinatorial optimization and metaheuristics}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001642}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The graph partitioning problem occurs in numerous applications such as circuit placement, matrix factorization, load balancing, and community detection. For this problem, genetic algorithm is a representative approach with competitive performance with many related papers being published. Although there are a number of surveys on graph partitioning, none of them deals with genetic algorithms in much detail. In this survey, a number of problem-specific issues in applying genetic algorithms to the graph partitioning problem are discussed; the issues include encoding, crossover, normalisation, and balancing.}, notes = {Also known as \cite{2001642} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{PetricaPop:2011:GECCO, author = {Petrica C. Pop and Serban Iordache}, title = {A hybrid heuristic approach for solving the generalized traveling salesman problem}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {481--488}, keywords = {Evolutionary combinatorial optimization and metaheuristics}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001643}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The generalised travelling salesman problem (GTSP) is an NP-hard problem that extends the classical travelling salesman problem by partitioning the nodes into clusters and looking for a minimum Hamiltonian tour visiting exactly one node from each cluster. In this paper, we combine the consultant-guided search technique with a local-global approach in order to solve efficiently the generalised traveling salesman problem. We use candidate lists in order to reduce the search space and we introduce efficient variants of 2-opt and 3-opt local search in order to improve the solutions. The resulting algorithm is applied to Euclidean GTSP instances derived from the TSPLIB library. The experimental results show that our algorithm is able to compete with the best existing algorithms in terms of solution quality and running time.}, notes = {Also known as \cite{2001643} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Ruiz-Torrubiano:2011:GECCO, author = {Ruben Ruiz-Torrubiano and Alberto Suarez}, title = {The TransRAR crossover operator for genetic algorithms with set encoding}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {489--496}, keywords = {Evolutionary combinatorial optimization and metaheuristics}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001644}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This work introduces a new crossover operator specially designed to be used in genetic algorithms (GAs) that encode candidate solutions as sets of fixed cardinality. The Transmitting Random Assortment Recombination (TransRAR) operator proceeds by taking elements from a multiset, which is built by the union of the parent chromosomes, allowing repeated elements. If an element that is present in both parents is drawn, it is accepted with probability 1. Elements that belong to only one of the parents are accepted with a probability p, smaller than 1. The performance of this novel crossover operator is assessed in synthetic and real-world problems. In these problems, GAs that employ this type of crossover outperform those that use alternative operators for sets, such as Random Assortment Recombination (RAR), Random Respectful Recombination (R3) or Random Transmitting Recombination (RTR). Furthermore, TransRAR can be implemented very efficiently and is faster than RAR, its closest competitor in terms of overall performance.}, notes = {Also known as \cite{2001644} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Kafafy:2011:GECCO, author = {Ahmed Kafafy and Ahmed Bounekkar and St\'{e}phane Bonnevay}, title = {A hybrid evolutionary metaheuristics (HEMH) applied on 0/1 multiobjective knapsack problems}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {497--504}, keywords = {Evolutionary combinatorial optimization and metaheuristics}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001645}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Handling Multiobjective Optimisation Problems (MOOP) using Hybrid Metaheuristics represents a promising and interest area of research. In this paper, a Hybrid Evolutionary Metaheuristics (HEMH) is presented. It combines different metaheuristics integrated with each other to enhance the search capabilities. It improves both of intensification and diversification toward the preferred solutions and concentrates the search efforts to investigate the promising regions in the search space. In the proposed HEMH, the search process is divided into two phases. In the first one, the DM-GRASP is applied to obtain an initial set of high quality solutions dispersed along the Pareto front. Then, the search efforts are intensified on the promising regions around these solutions through the second phase. The greedy randomised path-relinking with local search or reproduction operators are applied to improve the quality and to guide the search to explore the non discovered regions in the search space. The two phases are combined with a suitable evolutionary framework supporting the integration and cooperation. Moreover, the efficient solutions explored over the search are collected in an external archive. The HEMH is verified and tested against some of the state of the art MOEAs using a set of MOKSP instances commonly used in the literature. The experimental results indicate that the HEMH is highly competitive and can be considered as a viable alternative.}, notes = {Also known as \cite{2001645} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Fatima:2011:GECCO, author = {Shaheen Fatima and Ahmed Kattan}, title = {Evolving optimal agendas for package deal negotiation}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {505--512}, keywords = {genetic algorithms, genetic programming, Evolutionary combinatorial optimization and metaheuristics}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001646}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper presents a hyper GA system to evolve optimal agendas for package deal negotiation. The proposed system uses a Surrogate Model based on Radial Basis Function Networks (RBFNs) to speed up the evolution. The negotiation scenario is as follows. There are two negotiators/agents (a and b) and m issues/items available for negotiation. But from these m issues, the agents must choose g issues and negotiate on them. The g issues thus chosen form the agenda. The agenda is important because the outcome of negotiation depends on it. Furthermore, a and b will, in general, get different utilities/profits from different agendas. Thus, for competitive negotiation (i.e., negotiation where each agent wants to maximise its own utility), each agent wants to choose an agenda that maximizes its own profit. However, the problem of determining an agent's optimal agenda is complex, as it requires combinatorial search. To overcome this problem, we present a hyper GA method that uses a Surrogate Model based on Radial Basis Function Networks (RBFNs) to find an agent's optimal agenda. The performance of the proposed method is evaluated experimentally. The results of these experiments demonstrate that the surrogate assisted algorithm, on average, performs better than standard GA and random search.}, notes = {Also known as \cite{2001646} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Mehdi:2011:GECCO, author = {Malika Mehdi and Jean-Claude Charr and Nouredine Melab and El-Ghazali Talbi and Pascal Bouvry}, title = {A cooperative tree-based hybrid GA-B\&\#38;B approach for solving challenging permutation-based problems}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {513--520}, keywords = {Evolutionary combinatorial optimization and metaheuristics}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001647}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The issue addressed in this paper is how to build low-level hybrid cooperative optimisation methods that combine a Genetic Algorithm (GA) with a Branch-and-Bound algorithm (B&B). The key challenge is to provide a common solution and search space coding and associated transformation operators enabling an efficient cooperation between the two algorithms. The tree-based coding is traditionally used in exact optimisation methods such as B&B. In this paper, we explore the idea of using such coding in Genetic Algorithms. Following this idea, we propose a pioneering approach hybridising a GA with a B&B algorithm. The information (solutions and search sub-spaces) exchange between the two algorithms is performed at low-level and during the exploration process. From the implementation point of view, the common coding has facilitated the low-level coupling of two software frameworks: ParadisEO and BOB++ used to implement respectively the GA and the B&B algorithms. The proposed approach has been experimented on the 3D Quadratic Assignment Problem. In order to support the CPU cost of the hybridisation mechanism, hierarchical parallel computing is used together with grid computing.}, notes = {Also known as \cite{2001647} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Abbasian:2011:GECCO, author = {Reza Abbasian and Malek Mouhoub}, title = {An efficient hierarchical parallel genetic algorithm for graph coloring problem}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {521--528}, keywords = {Evolutionary combinatorial optimization and metaheuristics}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001648}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Graph colouring problems (GCPs) are constraint optimisation problems with various applications including scheduling, time tabling, and frequency allocation. The GCP consists in finding the minimum number of colours for colouring the graph vertices such that adjacent vertices have distinct colors. We propose a parallel approach based on Hierarchical Parallel Genetic Algorithms (HPGAs) to solve the GCP. We also propose a new extension to PGA, that is Genetic Modification (GM) operator designed for solving constraint optimisation problems by taking advantage of the properties between variables and their relations. Our proposed GM for solving the GCP is based on a novel Variable Ordering Algorithm (VOA). In order to evaluate the performance of our new approach, we have conducted several experiments on GCP instances taken from the well known DIMACS website. The results show that the proposed approach has a high performance in time and quality of the solution returned in solving graph coloring instances taken from DIMACS website. The quality of the solution is measured here by comparing the returned solution with the optimal one.}, notes = {Also known as \cite{2001648} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Whitley:2011:GECCO, author = {Darrell Whitley and Gabriela Ochoa}, title = {Partial neighborhoods of the traveling salesman problem}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {529--536}, keywords = {Evolutionary combinatorial optimization and metaheuristics}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001649}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The Travelling Salesman Problem (TSP) is known to display an elementary landscape under all k-opt move operators. Previous work has also shown that partial neighborhoods may exist that retain some properties characteristic of elementary landscapes. For a tour of n cities, we show that the 2-opt neighbourhood can be decomposed into n/2-1 partial neighborhoods. While this paper focuses on the TSP, it also introduces a more formal treatment of partial neighborhoods which applies to all elementary landscapes. Tracking partial neighborhood averages in elementary landscapes requires partitioning the cost matrix. After every move in the search space, the relevant partitions must be updated. However, just as the evaluation function allows a partial update for the TSP, there also exists a partial update for the cost matrix partitions. By only looking at a subset of the partial neighborhoods we can further reduce the cost of updating the cost matrix partitions.}, notes = {Also known as \cite{2001649} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Tairan:2011:GECCO, author = {Nasser Tairan and Qingfu Zhang}, title = {P-GLS-II: an enhanced version of the population-based guided local search}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {537--544}, keywords = {Evolutionary combinatorial optimization and metaheuristics}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001650}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We have recently proposed a Population-based Guided Local Search (P-GLS) framework for solving difficult combinatorial optimisation problems. In P-GLS, several agents of guided local search (GLS) procedures are run in a parallel way. These agents exchange information acquired from their previous search to make their further search more rational. We suggested based on the well-known proximate optimality principle (POP) that the shared features between the current agents' local optimal solutions are more likely to be part of the best solution to the problem; therefore these features should not be penalised. However, sometimes some of these common features may not exhibit in a global optimal solution. In this paper, a new framework is proposed to improve the performance as well as overcome the limitations in P-GLS. It applies two new different penalisation strategies that increase favouring common features based on their occurrences in the agents' local optimal solutions during the search. The performance of the new algorithm, examined on the Travelling Salesman Problem (TSP), is investigated and evaluated in terms of solution quality and the speed. The experimental results demonstrate that the new algorithm outperforms the parallel GLS algorithm without collaboration and other state-of-the-art algorithms.}, notes = {Also known as \cite{2001650} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Qin:2011:GECCO, author = {A. K. Qin and Florence Forbes}, title = {Harmony search with differential mutation based pitch adjustment}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {545--552}, keywords = {Evolutionary combinatorial optimization and metaheuristics}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001651}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Harmony search (HS), as an emerging metaheuristic technique mimicking the improvisation behaviour of musicians, has demonstrated strong efficacy in solving various numerical and real-world optimisation problems. This work presents a harmony search with differential mutation based pitch adjustment (HSDM) algorithm, which improves the original pitch adjustment operator of HS using the self-referential differential mutation scheme that features differential evolution - another celebrated metaheuristic algorithm. In HSDM, the differential mutation based pitch adjustment can dynamically adapt the properties of the landscapes being explored at different searching stages. Meanwhile, the pitch adjustment operator's execution probability is allowed to vary randomly between 0 and 1, which can maintain both wild and fine exploitation throughout the searching course. HSDM has been evaluated and compared to the original HS and two recent HS variants using 16 numerical test problems of various searching landscape complexities at 10 and 30 dimensions. HSDM almost always demonstrates superiority on all test problems.}, notes = {Also known as \cite{2001651} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{ShuLiu:2011:GECCO, author = {Shu Liu and Hitoshi Iba}, title = {Imitation tendencies of local search schemes in baldwinian evolution}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {553--560}, keywords = {Evolutionary combinatorial optimization and metaheuristics}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001652}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Baldwinian evolution is a type of hybridisation of population-based global search and individual local search. The individuals take local refining processes, then in selection benefit from the improved fitness, but do not pass on the refined traits the data in to the offspring. The lost information of the refined phenotype implies that the inheritance encoded in genotypes is not directly benefit traits, but the traits having potential to achieve high fitness through the lifetime interaction with the environment. As the result, it is necessary to study how learning works comparing to the previous generation, in addition to how much it improves on the current population. The children may imitate what their parents performed and catch up with them, or alternatively, explore elsewhere and have no idea of where the parents arrived. In this paper, the trade-off is investigated, and it is revealed that in Baldwinian learning, the capability to follow the parents' footprints benefits. With higher imitation tendency, the evolving population can maintain a greater scale of learning potential, and the search results in better speed and convergence.}, notes = {Also known as \cite{2001652} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Aleti:2011:GECCO, author = {Aldeida Aleti and Irene Moser}, title = {Predictive parameter control}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {561--568}, keywords = {Evolutionary combinatorial optimization and metaheuristics}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001653}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In stochastic optimisation, all currently employed algorithms have to be parametrised to perform effectively. Users have to rely on approximate guidelines or, alternatively, undertake extensive prior tuning. This study introduces a novel method of parameter control, i.e. the dynamic and automated variation of values for parameters used in approximate algorithms. The method uses an evaluation of the recent performance of previously applied parameter values and predicts how likely each of the parameter values is to produce optimal outcomes in the next cycle of the algorithm. The resulting probability distribution is used to determine the parameter values for the following cycle. The results of our experiments show a consistently superior performance of two very different EA algorithms when they are parameterised using the predictive parameter control method.}, notes = {Also known as \cite{2001653} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Jui-TingLee:2011:GECCO, author = {Jui-Ting Lee and Kai-Chun Fan and Tian-Li Yu}, title = {The essence of real-valued characteristic function for pairwise relation in linkage learning for EDAs}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {569--576}, keywords = {Estimation of distribution algorithms}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001655}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Existing EDAs learn linkages starting from pairwise interactions. The characteristic function which indicates the relations among variables are binary. In other words, the characteristic function indicates that there exist or not interactions among variables. Empirically, it can occur that two variables should be sometimes related but sometimes not. This paper introduces a real-valued characteristic function to illustrate this property of fuzziness. We examine all the possible binary models and real-valued models on a test problem. The results show that the optimal real-valued model is better than all the binary models. This paper also proposes a crossover method which is able to use the real-valued information. Experiments show that the proposed crossover could reduce the number of function evaluations up to four times. Moreover, this paper proposes an effective method to find a threshold for entropy based interaction-detection metric and a method to learn real-valued models. Experiments show that the proposed crossover with the learnt real-valued models works well.}, notes = {Also known as \cite{2001655} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Posik:2011:GECCO, author = {Petr Po\v{s}\'{\i}k and Stanislav Van\'{\i}\v{c}ek}, title = {Parameter-less local optimizer with linkage identification for deterministic order-k decomposable problems}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {577--584}, keywords = {Estimation of distribution algorithms}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001656}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {A simple parameter-less local optimiser able to solve deterministic problems with building blocks of bounded order is proposed in this article. The algorithm is able to learn and use linkage information during the run. The algorithm is algorithmically simple, easy to implement and with the exception of termination condition, it is completely parameter-free---there is thus no need to tune the population size and other parameters to the problem at hand. An empirical comparison on 3 decomposable functions, each with uniformly scaled building blocks of size 5 and 8, was carried out. The algorithm exhibits quadratic scaling with the problem dimensionality, but the comparison with the extended compact genetic algorithm and Bayesian optimisation algorithm shows that it needs lower or comparable number of fitness function evaluations on the majority of functions for the tested problem dimensionalities. The results also suggest that the efficiency of the local optimizer compared to both the estimation-of-distribution algorithms should be better for problem sizes under at least a few hundreds of bits.}, notes = {Also known as \cite{2001656} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Salinas-Gutierrez:2011:GECCO, author = {Rogelio Salinas-Gutierrez and Arturo Hernandez-Aguirre and Enrique R. Villa-Diharce}, title = {Dependence trees with copula selection for continuous estimation of distribution algorithms}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {585--592}, keywords = {Estimation of distribution algorithms}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001657}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper, a new Estimation of Distribution Algorithm (EDA) is presented. The proposed algorithm employs a dependency tree as a graphical model and bivariate copula functions for modelling relationships between pairwise variables. By selecting copula functions it is possible to build a very flexible joint distribution as a probabilistic model. The experimental results show that the proposed algorithm has a better performance than EDAs based on Gaussian assumptions.}, notes = {Also known as \cite{2001657} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Santana:2011:GECCOk, author = {Roberto Santana and Hossein Karshenas and Concha Bielza and Pedro Larranaga}, title = {Regularized k-order markov models in {EDAs}}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {593--600}, keywords = {Estimation of distribution algorithms}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001658}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {k-order Markov models have been introduced to estimation of distribution algorithms (EDAs) to solve a particular class of optimisation problems in which each variable depends on its previous k variables in a given, fixed order. In this paper we investigate the use of regularisation as a way to approximate k-order Markov models when $k$ is increased. The introduced regularised models are used to balance the complexity and accuracy of the k-order Markov models. We investigate the behaviour of the EDAs in several instances of the hydrophobic-polar (HP) protein problem, a simplified protein folding model. Our preliminary results show that EDAs that use regularized approximations of the k-order Markov models offer a good compromise between complexity and efficiency, and could be an appropriate choice when the number of variables is increased.}, notes = {Also known as \cite{2001658} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{XiannengLi:2011:GECCO, author = {Xianneng Li and Shingo Mabu and Kotaro Hirasawa}, title = {Use of infeasible individuals in probabilistic model building genetic network programming}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {601--608}, keywords = {genetic algorithms, genetic programming, genetic network programming, Estimation of distribution algorithms}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001659}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Classical EDAs generally use truncation selection to estimate the distribution of the feasible (good) individuals while ignoring the infeasible (bad) ones. However, various research in EAs reported that the infeasible individuals may affect and help the problem solving. This paper proposed a new method to use the infeasible individuals by studying the sub-structures rather than the entire individual structures to solve Reinforcement Learning (RL) problems, which generally factorise their entire solutions to the sequences of state-action pairs. This work was studied in a recent graph-based EDA named Probabilistic Model Building Genetic Network Programming (PMBGNP) which can solve RL problems successfully. The effectiveness of this work is verified in a RL problem, i.e., robot control, comparing with some other related work.}, notes = {Also known as \cite{2001659} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Ceberio:2011:GECCO, author = {Josu Ceberio and Alexander Mendiburu and Jose Antonio Lozano}, title = {A preliminary study on EDAs for permutation problems based on marginal-based models}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {609--616}, keywords = {Estimation of distribution algorithms}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001660}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Estimation of Distribution Algorithms are a class of evolutionary algorithms characterised by the use of probabilistic models. These algorithms have been applied successfully to a wide set of artificial and real-world problems, achieving competitive results in most scenarios. Nevertheless, there are some problems whose solutions can be naturally represented as a permutation, for which EDAs have not been extensively developed. Although some work has been done in this area, most of the approaches are adaptations of EDAs designed for problems based on integer or real domains, and only a few algorithms have been specifically designed to deal with permutation-based problems. In this paper, we present an EDA that learns probability distributions over permutations. Particularly, our approach is based on the use of k-order marginals. In addition, we carry out some preliminary experiments over classical permutation-based problems in order to study the performance of the proposed k-order marginals EDA.}, notes = {Also known as \cite{2001660} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Thierens:2011:GECCO, author = {Dirk Thierens and Peter A.N. Bosman}, title = {Optimal mixing evolutionary algorithms}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {617--624}, keywords = {Estimation of distribution algorithms}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001661}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {A key search mechanism in Evolutionary Algorithms is the mixing or juxtaposing of partial solutions present in the parent solutions. In this paper we look at the efficiency of mixing in genetic algorithms (GAs) and estimation-of-distribution algorithms (EDAs). We compute the mixing probabilities of two partial solutions and discuss the effect of the covariance build-up in GAs and EDas. Moreover, we propose two new Evolutionary Algorithms that maximise the juxtaposing of the partial solutions present in the parents: the Recombinative Optimal Mixing Evolutionary Algorithm (ROMEA) and the Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA).}, notes = {Also known as \cite{2001661} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Hauschild:2011:GECCO, author = {Mark Hauschild and Martin Pelikan}, title = {Advanced neighborhoods and problem difficulty measures}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {625--632}, keywords = {Estimation of distribution algorithms}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001662}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {While different measures of problem difficulty of fitness landscapes have been proposed, recent studies have shown that many of the common ones do not closely correspond to the actual difficulty of problems when solved by evolutionary algorithms. One of the reasons for this is that most problem difficulty measures are based on neighbourhood structures that are quite different from those used in most evolutionary algorithms. This paper examines several ways to increase the accuracy of problem difficulty measures by including linkage information in the measure to more accurately take into account the advanced neighborhoods explored by some evolutionary algorithms. The effects of these modifications of problem difficulty are examined in the context of several simple and advanced evolutionary algorithms. The results are then discussed and promising areas for future research are proposed.}, notes = {Also known as \cite{2001662} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Iclanzan:2011:GECCO, author = {David Iclanzan}, title = {Hierarchical allelic pairwise independent functions}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {633--640}, keywords = {Estimation of distribution algorithms}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001663}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Current multivariate EDAs rely on computationally efficient pairwise linkage detection mechanisms to identify higher order linkage blocks. Historical attempts to exemplify the potential disadvantage of this computational shortcut were scarcely successful. In this paper we introduce a new class of test functions to exemplify the inevitable weakness of the simplified linkage learning techniques. Specifically, we show that presently employed EDAs are not able to efficiently mix and decide between building-blocks with pairwise allelic independent components. These problems can be solved by EDAs only at the expense of exploring a vastly larger search space of multivariable linkages.}, notes = {Also known as \cite{2001663} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Ulrich:2011:GECCO, author = {Tamara Ulrich and Lothar Thiele}, title = {Maximizing population diversity in single-objective optimization}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {641--648}, keywords = {Evolutionary multiobjective optimization}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001665}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Typically, optimisation attempts to find a solution which minimises the given objective function. But often, it might also be useful to obtain a set of structurally very diverse solutions which all have acceptable objective values. With such a set, a decision maker would be given a choice of solutions to select from. In addition, he can learn about the optimisation problem at hand by inspecting the diverse close-to-optimal solutions. This paper proposes NOAH, an evolutionary algorithm which solves a mixed multi-objective problem: Determine a maximally diverse set of solutions whose objective values are below a provided objective barrier. It does so by iteratively switching between objective value and set-diversity optimization while automatically adapting a constraint on the objective value until it reaches the barrier. Tests on an nk-Landscapes problem and a 3-Sat problem as well as on a more realistic bridge construction problem show that the algorithm is able to produce high quality solutions with a significantly higher structural diversity than standard evolutionary algorithms.}, notes = {Also known as \cite{2001665} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Ishibuchi:2011:GECCO, author = {Hisao Ishibuchi and Naoya Akedo and Yusuke Nojima}, title = {A many-objective test problem for visually examining diversity maintenance behavior in a decision space}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {649--656}, keywords = {Evolutionary multiobjective optimization}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001666}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Recently distance minimisation problems in a two-dimensional decision space have been used as many-objective test problems to visually examine the behaviour of evolutionary multi-objective optimisation (EMO) algorithms. Such a test problem is usually defined by a single polygon where the distance from a solution to each vertex is minimised in the decision space. We can easily generate different test problems from different polygons. We can also easily generate test problems with multiple equivalent Pareto optimal regions using multiple polygons of the same shape and the same size. Whereas these test problems have a number of advantages, they have no clear relevance to real-world situations since they are artificially generated unrealistic test problems. In this paper, we generate a distance minimisation problem from a real-world map. Our test problem has four objectives, which are to minimise the distances to the nearest elementary school, junior high school, railway station, and convenience store. Using our test problem, we examine the behaviour of well-known and frequently-used EMO algorithms in terms of their diversity maintenance ability in the two-dimensional decision space.}, notes = {Also known as \cite{2001666} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Tagawa:2011:GECCO, author = {Kiyoharu Tagawa and Hidehito Shimizu and Hiroyuki Nakamura}, title = {Indicator-based differential evolution using exclusive hypervolume approximation and parallelization for multi-core processors}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {657--664}, keywords = {Evolutionary multiobjective optimization}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001667}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {A new Multi-Objective Evolutionary Algorithm (MOEA) based on Differential Evolution (DE), i.e., Indicator-Based DE (IBDE) is proposed. IBDE employs a strategy of DE for generating a series of offspring. In order to evaluate the quality of each individual in the population, IBDE uses the exclusive hypervolume as an indicator function. A fast algorithm called Incremental Hypervolume by Slicing Objectives (IHSO) has been reported for calculating the exclusive hypervolume. However, the computational time spent by IHSO increases exponentially with the number of objectives and considered individuals. Therefore, an exclusive hypervolume approximation, in which IHSO can be also used effectively, is proposed. Furthermore, it is proved that the proposed exclusive hypervolume approximation gives an upper bound of the accurate exclusive hypervolume. The procedure of IHSO is parallelled by using the multiple threads of the Java language. By using the parallelled IHSO, not only the exclusive hypervolume but also the exclusive hypervolume approximation can be calculated concurrently on a multi-core processor. By the results of numerical experiments and statistical tests conducted on test problems, the usefulness of the proposed approach is demonstrated.}, notes = {Also known as \cite{2001667} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Zhong:2011:GECCO, author = {Jing-hui Zhong and Jun Zhang}, title = {Adaptive multi-objective differential evolution with stochastic coding strategy}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {665--672}, keywords = {Evolutionary multiobjective optimization}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001668}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Many real-world applications can be modelled as multi-objective optimisation problems (MOPs). Applying differential evolution (DE) to MOPs is a promising research topic and has drawn a lot of attention in recent years. To search high-quality solutions for MOPs, this paper presents a robust adaptive DE (termed AS-MODE) with following two features. First, a stochastic coding strategy is used to improve the solution quality. This coding strategy represents each individual by a stochastic region, which enables the algorithm to fine-tune solutions efficiently. Second, a probability-based adaptive control strategy is used to reduce the influence of parameter settings. The adaptive control strategy associates each parameter with a candidate value set. Better candidate values would have higher selection probabilities to generate new individuals. The performance of the proposed AS-MODE is compared with several highly regarded multi-objective evolutionary algorithms. Simulation results on ten benchmark test functions with different characteristics reveal that AS-MODE yields very promising performance.}, notes = {Also known as \cite{2001668} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Montgomery:2011:GECCO, author = {James Montgomery and Marcus Randall and Andrew Lewis}, title = {Differential evolution for RFID antenna design: a comparison with ant colony optimisation}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {673--680}, keywords = {Evolutionary multiobjective optimization}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001669}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Differential evolution (DE) has been traditionally applied to solving benchmark continuous optimisation functions. To enable it to solve a combinatorially oriented design problem, such as the construction of effective radio frequency identification antennas, requires the development of a suitable encoding of the discrete decision variables in a continuous space. This study introduces an encoding that allows the algorithm to construct antennas of varying complexity and length. The DE algorithm developed is a multiobjective approach that maximises antenna efficiency and minimises resonant frequency. Its results are compared with those generated by a family of ant colony optimisation (ACO) metaheuristics that have formed the standard in this area. Results indicate that DE can work well on this problem and that the proposed solution encoding is suitable. On small antenna grid sizes (hence, smaller solution spaces) DE performs well in comparison to ACO, while as the solution space increases its relative performance decreases. However, as the ACO employs a local search operator that the DE currently does not, there is scope for further improvement to the DE approach.}, notes = {Also known as \cite{2001669} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Ripon:2011:GECCO, author = {Kazi Shah Nawaz Ripon and Kashif Nizam Khan and Kyree Glette and Mats Hovin and Jim Torresen}, title = {Using pareto-optimality for solving multi-objective unequal area facility layout problem}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {681--688}, keywords = {Evolutionary multiobjective optimization}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001670}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {A lot of optimal and heuristic algorithms for solving facility layout problem (FLP) have been developed in the past few decades. The majority of these approaches adopt a problem formulation known as the quadratic assignment problem (QAP) that is particularly suitable for equal area facilities. Unequal area FLP comprises a class of extremely difficult and widely applicable optimisation problems arising in many diverse areas to meet the requirements for real-world applications. Unfortunately, most of these approaches are based on a single objective. While, the real-world FLPs are multi-objective by nature. Only very recently have meta-heuristics been designed and used in multi-objective FLP. They most often use the weighted sum method to combine the different objectives and thus, inherit the well-known problems of this method. As of now, there is no formal approach published for the unequal area multi-objective FLP to consider several objectives simultaneously. This paper presents an evolutionary approach for solving multi-objective unequal area FLP using multi-objective genetic algorithm that presents the layout as a set of Pareto-optimal solutions optimising multiple objectives simultaneously. The experimental results show that the proposed approach performs well in dealing with multi-objective unequal area FLPs which better reflects the real-world scenario.}, notes = {Also known as \cite{2001670} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Minisci:2011:GECCO, author = {Edmondo Minisci and Massimiliano Vasile}, title = {Robust design of a re-entry unmanned space vehicle by multi-fidelity evolution control}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {689--696}, keywords = {Evolutionary multiobjective optimization}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001671}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper addresses the preliminary robust design of a small-medium scale re-entry unmanned space vehicle. A hybrid optimisation technique is proposed that couples an evolutionary multi-objective algorithm with a direct transcription method for optimal control problems. Uncertainties on the aerodynamic forces and vehicle mass are integrated in the design process and the hybrid algorithm searches for geometries that minimise the mean value of the maximum heat flux, the mean value of the maximum achievable distance, and the variance of the maximum heat flux. The evolutionary part handles the system design parameters of the vehicle and the uncertain functions, while the direct transcription method generates optimal control profiles for the re-entry trajectory of each individual of the population. During the optimisation process, artificial neural networks are used to approximate the aerodynamic forces required by the direct transcription method. The artificial neural networks are trained and updated by means of a multi-fidelity, evolution control approach.}, notes = {Also known as \cite{2001671} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Harris:2011:GECCO, author = {Irina Harris and Christine L. Mumford and Mohamed M. Naim}, title = {An evolutionary bi-objective approach to the capacitated facility location problem with cost and CO_2 emissions}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {697--704}, keywords = {Evolutionary multiobjective optimization}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001672}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {It is strategically important to design efficient and environmentally friendly distribution networks. In this paper we propose a new methodology for solving the capacitated facility location problem (CFLP) based on combining an evolutionary multi-objective algorithm with Lagrangian Relaxation where financial costs and CO2 emissions are considered simultaneously. Two levels of decision making are required: 1) which facilities to open from a set of potential sites, and 2) which customers to assign to which open facilities without violating their capacity. We choose SEAMO2 (Simple Evolutionary Multi-objective Optimisation 2) as our multi-objective evolutionary algorithm to determine which facilities to open, because of its fast execution speed. For the allocation of customers to open facilities we use a Lagrangian Relaxation technique. We test our approach on large problem instances with realistic qualities, and validate solution quality by comparison with extreme solutions obtained using CPLEX.}, notes = {Also known as \cite{2001672} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Soares-Brasil:2011:GECCO, author = {Christiane Regina {Soares Brasil} and Alexandre Cl\'{a}udio {Botazzo Delbem} and Daniel Rodrigo {Ferraz Bonetti}}, title = {Investigating relevant aspects of MOEAs for protein structures prediction}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {705--712}, keywords = {Evolutionary multiobjective optimization}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001673}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Several computational models have been developed in the context of the Protein Structure Prediction (PSP) problem. These methods involve a combinatorial problem and can be solved using optimising algorithms in order to search for a global minimum energy. Genetic Algorithms (GAs) have produced relevant results in this area. Several energies in the protein are known to be directly responsible for the stabilisation of their structures. These energies can represent each objective of multiobjective evolutionary algorithms. Many techniques, as the NSGA-II, are used to deal with the multi-objective approach for proteins, however they are not adequate for the PSP problem. New strategies have been sought with multiple criteria. In this context, this paper introduces the application of multiobjective evolutionary algorithm on tables algorithm to the PSP problem. In order to evaluate this approach, we compare it with the well-known NSGA-II algorithm. The new approach investigated for PSP can generate protein structures with energies significantly smaller than those generated by the NSGA-II.}, notes = {Also known as \cite{2001673} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Soh:2011:GECCO, author = {Harold Soh and Yiannis Demiris}, title = {Evolving policies for multi-reward partially observable markov decision processes (MR-POMDPs)}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {713--720}, keywords = {Evolutionary multiobjective optimization}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001674}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Plans and decisions in many real-world scenarios are made under uncertainty and to satisfy multiple, possibly conflicting, objectives. In this work, we contribute the multi-reward partially-observable Markov decision process (MR-POMDP) as a general modelling framework. To solve MR-POMDPs, we present two hybrid (memetic) multi-objective evolutionary algorithms that generate non-dominated sets of policies (in the form of stochastic finite state controllers). Performance comparisons between the methods on multi-objective problems in robotics (with 2, 3 and 5 objectives), web-advertising (with 3, 4 and 5 objectives) and infectious disease control (with 3 objectives), revealed that memetic variants outperformed their original counterparts. We anticipate that the MR-POMDP along with multi-objective evolutionary solvers will prove useful in a variety of theoretical and real-world applications.}, notes = {Also known as \cite{2001674} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Gong:2011:GECCO, author = {Maoguo Gong and Fang Liu and Wei Zhang and Licheng Jiao and Qingfu Zhang}, title = {Interactive MOEA/D for multi-objective decision making}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {721--728}, keywords = {Evolutionary multiobjective optimization}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001675}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper, an interactive version of the decomposition based multiobjective evolutionary algorithm (iMOEA/D) is proposed for interaction between the decision maker (DM) and the algorithm. In MOEA/D, a multi-objective problem (MOP) can be decomposed into several single-objective sub-problems. Thus, the preference incorporation mechanism in our algorithm is implemented by selecting the preferred sub-problems rather than the preferred region in the objective space. At each interaction, iMOEA/D offers a set of current solutions and asks the DM to choose the most preferred one. Then, the search will be guided to the neighbourhood of the selected. iMOEA/D is tested on some benchmark problems, and various utility functions are used to simulate the DM's responses. The experimental studies show that iMOEA/D can handle the preference information very well and successfully converge to the expected preferred regions.}, notes = {Also known as \cite{2001675} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Sun:2011:GECCO, author = {Jing Sun and Dunwei Gong and Xiaoyan Sun}, title = {Solving interval multi-objective optimization problems using evolutionary algorithms with preference polyhedron}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {729--736}, keywords = {Evolutionary multiobjective optimization}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001676}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Multi-objective optimisation (MOO) problems with interval parameters are popular and important in real-world applications. Previous evolutionary optimisation methods aim to find a set of well-converged and evenly-distributed Pareto-optimal solutions. We present a novel evolutionary algorithm (EA) that interacts with a decision maker (DM) during the optimisation process to obtain the DM's most preferred solution. First, the theory of a preference polyhedron for an optimisation problem with interval parameters is built up. Then, an interactive evolutionary algorithm (IEA) for MOO problems with interval parameters based on the above preference polyhedron is developed. The algorithm periodically provides a part of non-dominated solutions to the DM, and a preference polyhedron, based on which optimal solutions are ranked, is constructed with the worst solution chosen by the DM as the vertex. Finally, our method is tested on two bi-objective optimisation problems with interval parameters using two different value function types to emulate the DM's responses. The experimental results show its simplicity and superiority to the posteriori method.}, notes = {Also known as \cite{2001676} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Tusar:2011:GECCO, author = {Tea Tu\v{s}ar and Bogdan Filipi\v{c}}, title = {Visualizing 4D approximation sets of multiobjective optimizers with prosections}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {737--744}, keywords = {Evolutionary multiobjective optimization}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001677}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In ideal multiobjective optimisation, the result produced by an optimiser is a set of nondominated solutions approximating the Pareto optimal front. Visualisation of this approximation set can help assess its quality as well as present various features of the problem. Most often, scatter plots are used to visualise 2D and 3D approximation sets, while no scatter plot equivalent exists for visualisation in higher dimensions. This paper presents a method for visualising 4D approximation sets which performs dimension reduction using prosections (projections of a section). The method yields a prosection matrix---a matrix of intuitive 3D scatter plots that well reproduce the shape, range and distribution of vectors in the observed approximation set. The performance of visualisation with prosections is analysed theoretically and demonstrated on two examples with approximation sets of state-of-the-art test optimisation problems.}, notes = {Also known as \cite{2001677} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)}, } @inproceedings{Bringmann:2011:GECCO, author = {Karl Bringmann and Tobias Friedrich}, title = {Convergence of hypervolume-based archiving algorithms I: effectiveness}, booktitle = {GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0557-0}, pages = {745--752}, keywords = {Evolutionary multiobjective optimization}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001576.2001678}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The core of hypervolume-based multi-objective evolutionary algorithms is an archiving algorithm which performs the environmental selection. A (mu+lambda)-archiving algorithm defines how to choose mu children from mu parents and lambda offspring together. We study theoretically (mu+lambda)-archiving algorithms which never decrease the hypervolume from one generation to the next. Zitzler, Thiele, and Bader (IEEE Trans. Evolutionary Computation, 14:58--79, 2010) proved that all (mu+1)-archiving algorithms are ineffective, which means there is an initial population such that independent of the used reproduction rule, a set with maximum hypervolume cannot be reached. We extend this and prove that for lambda