Table of Contents

• Evolutionary form-finding of tensegrity structuresChandana Paul, Hod Lipson, Francisco J. Valero Cuevas. 3-10 [doi]
• Constructing good learners using evolved pattern generatorsVinod K. Valsalam, James A. Bednar, Risto Miikkulainen. 11-18 [doi]
• A study of evolutionary robustness in stochastically tiled polyominosJustin Schonfeld, Daniel A. Ashlock. 19-26 [doi]
• Optimization with constraints using a cultured differential evolution approachRicardo Landa Becerra, Carlos A. Coello Coello. 27-34 [doi]
• Predicting population dynamics and evolutionary trajectories based on performance evaluations in alife simulationsMatthias Scheutz, Paul W. Schermerhorn. 35-42 [doi]
• The predictive basis of situated and embodied artificial intelligenceKeith L. Downing. 43-50 [doi]
• Emergence of communication in competitive multi-agent systems: a pareto multi-objective approachMichelle McPartland, Stefano Nolfi, Hussein A. Abbass. 51-58 [doi]
• The impact of cellular representation on finite state agents for prisoner s dilemmaDaniel A. Ashlock, Eun-Youn Kim. 59-66 [doi]
• Multiplex PCR primer design for gene family using genetic algorithmHong-Long Liang, Chungnan Lee, Jain-Shing Wu. 67-74 [doi]
• Comparing multicast and newscast communication in evolving agent societiesA. E. Eiben, Martijn C. Schut, T. Toma. 75-81 [doi]
• Bias and scalability in evolutionary developmentTimothy G. W. Gordon, Peter J. Bentley. 83-90 [doi]
• Evolutionary computation and the c-value paradoxSean Luke. 91-97 [doi]
• Automated assembly as situated development: using artificial ontogenies to evolve buildable 3-D objectsJohn Rieffel, Jordan B. Pollack. 99-106 [doi]
• Using a genetic algorithm to evolve behavior in multi dimensional cellular automata: emergence of behaviorRon Breukelaar, Thomas Bäck. 107-114 [doi]
• Evolving visually guided agents in an ambiguous virtual worldEhud Schlessinger, Peter J. Bentley, R. Beau Lotto. 115-120 [doi]
• Autonomous navigation system applied to collective robotics with ant-inspired communicationRenato Reder Cazangi, Fernando J. Von Zuben, Maurício F. Figueiredo. 121-128 [doi]
• Agent-based modelling of product inventionAnthony Brabazon, Arlindo Silva, Tiago Ferra de Sousa, Michael O Neill, Robin Matthews, Ernesto Costa. 129-136 [doi]
• Validation of evolutionary activity metrics for long-term evolutionary dynamicsAndrew Stout, Lee Spector. 137-142 [doi]
• Neighboring crossover to improve GA-based Q-learning method for multi-legged robot controlTadahiko Murata, Masatoshi Yamaguchi. 145-146 [doi]
• Evolution of multi-loop controllers for fixed morphology with a cyclic genetic algorithmGary B. Parker, Ramona Georgescu. 147-148 [doi]
• Evolutionary models for maternal effects in simulated developmental systemsArtur Matos, Reiji Suzuki, Takaya Arita. 149-150 [doi]
• BeeAdHoc: an energy efficient routing algorithm for mobile ad hoc networks inspired by bee behaviorHorst Wedde, Muddassar Farooq, Thorsten Pannenbaecker, Bjoern Vogel, Christian Mueller, Johannes Meth, Rene Jeruschkat. 153-160 [doi]
• Breeding swarms: a GA/PSO hybridMatthew Settles, Terence Soule. 161-168 [doi]
• Exploring extended particle swarms: a genetic programming approachRiccardo Poli, Cecilia Di Chio, William B. Langdon. 169-176 [doi]
• Improving particle swarm optimization with differentially perturbed velocitySwagatam Das, Amit Konar, Uday Kumar Chakraborty. 177-184 [doi]
• Breeding swarms: a new approach to recurrent neural network trainingMatthew Settles, Paul Nathan, Terence Soule. 185-192 [doi]
• Bayesian optimization models for particle swarmsChristopher K. Monson, Kevin D. Seppi. 193-200 [doi]
• Dynamic-probabilistic particle swarmsJames Kennedy. 201-207 [doi]
• Constrained optimization via particle evolutionary swarm optimization algorithm (PESO)Angel E. Muñoz Zavala, Arturo Hernández Aguirre, Enrique R. Villa Diharce. 209-216 [doi]
• Evolving agent swarms for clustering and sortingVegard Hartmann. 217-224 [doi]
• Promising infeasibility and multiple offspring incorporated to differential evolution for constrained optimizationEfrén Mezura-Montes, Jesús Velázquez-Reyes, Carlos A. Coello Coello. 225-232 [doi]
• Scale invariant pareto optimality: a meta--formalism for characterizing and modeling cooperativity in evolutionary systemsMark Fleischer. 233-240 [doi]
• Exposing origin-seeking bias in PSOChristopher K. Monson, Kevin D. Seppi. 241-248 [doi]
• Ant colony optimization for power plant maintenance scheduling optimizationWai-Kuan Foong, Holger R. Maier, Angus R. Simpson. 249-256 [doi]
• An effective use of crowding distance in multiobjective particle swarm optimizationCarlo R. Raquel, Prospero C. Naval Jr.. 257-264 [doi]
• MeSwarm: memetic particle swarm optimizationBo-Fu Liu, Hung-Ming Chen, Jian-Hung Chen, Shiow-Fen Hwang, Shinn-Ying Ho. 267-268 [doi]
• Factors governing the behavior of multiple cooperating swarmsMohammed El-Abd, Mohamed Kamel. 269-270 [doi]
• Solving geometric TSP with antsThang Nguyen Bui, Mufit Colpan. 271-272 [doi]
• Simulating swarm intelligence in honey bees: foraging in differently fluctuating environmentsThomas Schmickl, Ronald Thenius, Karl Crailsheim. 273-274 [doi]
• A model based on ant colony system and rough set theory to feature selectionRafael Bello, Ann Nowé, Yaile Caballero, Yudel Gómez, Peter Vrancx. 275-276 [doi]
• A modified particle swarm optimization predicted by velocityZhihua Cui, Jianchao Zeng. 277-278 [doi]
• Estimating the detector coverage in a negative selection algorithmZhou Ji, Dipankar Dasgupta. 281-288 [doi]
• An artificial immune network for multimodal function optimization on dynamic environmentsFabrício Olivetti de França, Fernando J. Von Zuben, Leandro Nunes de Castro. 289-296 [doi]
• Discriminating and visualizing anomalies using negative selection and self-organizing mapsFabio A. González, Juan Carlos Galeano, Diego Alexander Rojas, Angélica Veloza-Suan. 297-304 [doi]
• Sufficiency verification of HIV-1 pathogenesis based on multi-agent simulationZaiyi Guo, Hann Kwang Han, Joc Cing Tay. 305-312 [doi]
• On the contribution of gene libraries to artificial immune systemsPeter Spellward, Tim Kovacs. 313-319 [doi]
• Is negative selection appropriate for anomaly detection?Thomas Stibor, Philipp H. Mohr, Jonathan Timmis, Claudia Eckert. 321-328 [doi]
• Artificial immune system for solving generalized geometric problems: a preliminary resultsJui-Yu Wu, Yun-Kung Chung. 329-336 [doi]
• An evolutionary algorithm to generate hyper-ellipsoid detectors for negative selectionJoseph M. Shapiro, Gary B. Lamont, Gilbert L. Peterson. 337-344 [doi]
• Applying both positive and negative selection to supervised learning for anomaly detectionXiaoshu Hang, Honghua Dai. 345-352 [doi]
• The application of antigenic search techniques to time series forecastingIan Nunn, Tony White. 353-360 [doi]
• A comparative analysis of artificial immune network modelsJuan Carlos Galeano, Angélica Veloza-Suan, Fabio A. González. 361-368 [doi]
• RABNET: a real-valued antibody network for data clusteringHelder Knidel, Leandro Nunes de Castro, Fernando J. Von Zuben. 371-372 [doi]
• Performance assessment of an artificial immune system multiobjective optimizer by two improved metricsMaoguo Gong, Licheng Jiao, Haifeng Du, Ronghua Shang, Bin Lu. 373-374 [doi]
• A hybrid genetic algorithm with pattern search for finding heavy atoms in protein crystalsJoshua L. Payne, Margaret J. Eppstein. 377-384 [doi]
• An efficient genetic algorithm for predicting protein tertiary structures in the 2D HP modelThang Nguyen Bui, Gnanasekaran Sundarraj. 385-392 [doi]
• A co-evolutionary hybrid algorithm for multi-objective optimization of gene regulatory network modelsPraveen Koduru, Sanjoy Das, Stephen Welch, Judith L. Roe, Zenaida P. Lopez-Dee. 393-399 [doi]
• Discovering biological motifs with genetic programmingRolv Seehuus, Amund Tveit, Ole Edsberg. 401-408 [doi]
• Using evolutionary computation methods to support analytical models for the evolution and maintenance of conditional strategies in ::::chthamalus anisopoma::::Gloria Childress Townsend, Wade N. Hazel, Rick Smock. 409-414 [doi]
• A GA for maximum likelihood phylogenetic inference using neighbour-joining as a genotype to phenotype mappingLeon Poladian. 415-422 [doi]
• A multi-objective evolutionary approach to peptide structure redesign and stabilizationTim Hohm, Daniel Hoffmann. 423-429 [doi]
• Particle swarm optimization for analysis of mass spectral serum profilesHabtom W. Ressom, Rency S. Varghese, Daniel Saha, Eduard Orvisky, Lenka Goldman, Emanuel F. Petricoin, Thomas P. Conrads, Timothy D. Veenstra, Mohamed Abdel-Hamid, Christopher A. Loffredo, Radoslav Goldman. 431-438 [doi]
• Inference of gene regulatory networks using s-system and differential evolutionNasimul Noman, Hitoshi Iba. 439-446 [doi]
• MDGA: motif discovery using a genetic algorithmDongsheng Che, Yinglei Song, Khaled Rasheed. 447-452 [doi]
• Extraction of informative genes from microarray dataTopon Kumar Paul, Hitoshi Iba. 453-460 [doi]
• Epileptic seizure detection by means of genetically programmed artificial featuresHiram A. Firpi, Erik D. Goodman, Javier Echauz. 461-466 [doi]
• Identifying valid solutions for the inference of regulatory networksChristian Spieth, Felix Streichert, Nora Speer, Andreas Zell. 469-470 [doi]
• Evolving an improved axial structure for fibrillar collagenDavid E. Cairns, G. J. Cameron, T. J. Wess. 471-472 [doi]
• GA-based approach to discover meaningful biclustersJesús S. Aguilar-Ruiz, Federico Divina. 473-474 [doi]
• Primer design for multiplex PCR using a genetic algorithmFeng-Mao Lin, Hsien-Da Huang, Hsi-Yuan Huang, Jorng-Tzong Horng. 475-476 [doi]
• A multiple objective evolutionary algorithm for multiple sequence alignmentPasut Seeluangsawat, Prabhas Chongstitvatana. 477-478 [doi]
• The impact of pseudorandom number quality on ::::P-RnaPredict::::, a parallel genetic algorithm for RNA secondary structure predictionKay C. Wiese, Andrew Hendriks, Alain Deschênes, Belgacem Ben Youssef. 479-480 [doi]
• The MaxSolve algorithm for coevolutionEdwin D. de Jong. 483-489 [doi]
• Co-evolving recurrent neurons learn deep memory POMDPsFaustino J. Gomez, Jürgen Schmidhuber. 491-498 [doi]
• Monotonic solution concepts in coevolutionSevan G. Ficici. 499-506 [doi]
• Understanding cooperative co-evolutionary dynamics via simple fitness landscapesElena Popovici, Kenneth A. De Jong. 507-514 [doi]
• Intransitivity revisited coevolutionary dynamics of numbers gamesPablo Funes, Enrique Pujals. 515-521 [doi]
• Investigating the success of spatial coevolutionNathan Williams, Melanie Mitchell. 523-530 [doi]
• Managed challenge alleviates disengagement in co-evolutionary system identificationJosh C. Bongard, Hod Lipson. 531-538 [doi]
• On identifying global optima in cooperative coevolutionAnthony Bucci, Jordan B. Pollack. 539-544 [doi]
• Tracking extrema in dynamic environments using a coevolutionary agent-based model of genotype editionChien-Feng Huang, Luis Mateus Rocha. 545-552 [doi]
• The emulation of social institutions as a method of coevolutionDeborah Vakas Duong, John J. Grefenstette. 555-556 [doi]
• Shape nesting by coevolving speciesJeffrey Horn. 557-558 [doi]
• Intrinsic emergence boosts adaptive capacityChristophe Philemotte, Hugues Bersini. 559-560 [doi]
• Evolutionary algorithms for the self-organized evolution of networksKatharina Anna Lehmann, Michael Kaufmann. 563-570 [doi]
• On the analysis of the approximation capability of simple evolutionary algorithms for scheduling problemsChristian Gunia. 571-578 [doi]
• Maximally rugged NK landscapes contain the highest peaksBenjamin Skellett, Benjamin Cairns, Nicholas Geard, Bradley Tonkes, Janet Wiles. 579-584 [doi]
• The blob code is competitive with edge-sets in genetic algorithms for the minimum routing cost spanning tree problemBryant A. Julstrom. 585-590 [doi]
• Coordinating multi-rover systems: evaluation functions for dynamic and noisy environmentsKagan Tumer, Adrian K. Agogino. 591-598 [doi]
• Transition models as an incremental approach for problem solving in evolutionary algorithmsAnne Defaweux, Tom Lenaerts, Jano I. van Hemert, Johan Parent. 599-606 [doi]
• Greedy, genetic, and greedy genetic algorithms for the quadratic knapsack problemBryant A. Julstrom. 607-614 [doi]
• Towards a self-stopping evolutionary algorithm using coupling from the pastGerman Hernandez, Kenneth Wilder, Fernando Niño, Julian Garcia. 615-620 [doi]
• Solving large scale combinatorial optimization using PMA-SLSJing Tang, Meng-Hiot Lim, Yew-Soon Ong, Meng Joo Er. 621-628 [doi]
• An evolutionary lagrangian method for the 0/1 multiple knapsack problemYourim Yoon, Yong-Hyuk Kim, Byung Ro Moon. 629-635 [doi]
• Hyper-heuristics and classifier systems for solving 2D-regular cutting stock problemsHugo Terashima-Marín, E. J. Flores-Álvarez, Peter Ross. 637-643 [doi]
• Water distribution systems optimal design using cross entropyLina Perelman, Avi Ostfeld. 647-648 [doi]
• A hybrid evolutionary algorithm for the p-median problemIstván Borgulya. 649-650 [doi]
• Harmony search for structural designZong Woo Geem, Kang Seok Lee, Chung-Li Tseng. 651-652 [doi]
• Extracted global structure makes local building block processing effective in XCSMartin V. Butz, Martin Pelikan, Xavier Llorà, David E. Goldberg. 655-662 [doi]
• Multiobjective hBOA, clustering, and scalabilityMartin Pelikan, Kumara Sastry, David E. Goldberg. 663-670 [doi]
• Sub-structural niching in estimation of distribution algorithmsKumara Sastry, Hussein A. Abbass, David E. Goldberg, D. D. Johnson. 671-678 [doi]
• Not all linear functions are equally difficult for the compact genetic algorithmStefan Droste. 679-686 [doi]
• Learned mutation strategies in genetic programming for evolution and adaptation of simulated snakebotIvan Tanev. 687-694 [doi]
• On the convergence of an estimation of distribution algorithm based on linkage discovery and factorizationAlden H. Wright, Sandeep Pulavarty. 695-702 [doi]
• Real-coded crossover as a role of kernel density estimationJun Sakuma, Shigenobu Kobayashi. 703-710 [doi]
• Population-based incremental learning with memory scheme for changing environmentsShengxiang Yang. 711-718 [doi]
• On the importance of diversity maintenance in estimation of distribution algorithmsBo Yuan, Marcus Gallagher. 719-726 [doi]
• Using a Markov network model in a univariate EDA: an empirical cost-benefit analysisSiddhartha Shakya, John A. W. McCall, Deryck F. Brown. 727-734 [doi]
• Combining competent crossover and mutation operators: a probabilistic model building approachCláudio F. Lima, Kumara Sastry, David E. Goldberg, Fernando G. Lobo. 735-742 [doi]
• Genetic drift in univariate marginal distribution algorithmYi Hong, Qingsheng Ren, Jin Zeng. 745-746 [doi]
• Learning computer programs with the bayesian optimization algorithmMoshe Looks, Ben Goertzel, Cassio Pennachin. 747-748 [doi]
• Multiobjective shape optimization with constraints based on estimation distribution algorithms and correlated informationSergio Ivvan Valdez Peña, Salvador Botello Rionda, Arturo Hernández Aguirre. 749-750 [doi]
• A comparative study of probability collectives based multi-agent systems and genetic algorithmsChien-Feng Huang, Stefan Bieniawski, David H. Wolpert, Charlie E. M. Strauss. 751-752 [doi]
• Exploiting gradient information in numerical multi--objective evolutionary optimizationPeter A. N. Bosman, Edwin D. de Jong. 755-762 [doi]
• Minimum spanning trees made easier via multi-objective optimizationFrank Neumann, Ingo Wegener. 763-769 [doi]
• A multi-objective genetic algorithm for robust design optimizationMian Li, Shapour Azarm, Vikrant Aute. 771-778 [doi]
• Fitness inheritance for noisy evolutionary multi-objective optimizationLam Thu Bui, Hussein A. Abbass, Daryl Essam. 779-785 [doi]
• Comparison of evolutionary multiobjective optimization with rference solution-based single-objective approachHisao Ishibuchi, Kaname Narukawa. 787-794 [doi]
• Evolving optimal feature extraction using multi-objective genetic programming: a methodology and preliminary study on edge detectionYang Zhang, Peter Rockett. 795-802 [doi]
• Minimizing total flowtime and maximum earliness on a single machine using multiple measures of fitnessMary E. Kurz, Sarah Canterbury. 803-809 [doi]
• A scalable parallel genetic algorithm for x-ray spectroscopic analysisKai Xu, Sushil J. Louis, Roberto C. Mancini. 811-816 [doi]
• An empirical study on the handling of overlapping solutions in evolutionary multiobjective optimizationHisao Ishibuchi, Kaname Narukawa, Yusuke Nojima. 817-824 [doi]
• Using predators and preys in evolution strategiesKarlheinz Schmitt, Jörn Mehnen, Thomas Michelitsch. 827-828 [doi]
• The effectiveness of multiobjective optimizer in single-objective optimization enviromentShinya Watanabe, Kazutoshi Sakakibara. 829-830 [doi]
• On the impact of objective function transformations on evolutionary and black-box algorithmsTobias Storch. 833-840 [doi]
• Theoretical analysis of a mutation-based evolutionary algorithm for a tracking problem in the latticeThomas Jansen, Ulf Schellbach. 841-848 [doi]
• Rigorous runtime analysis of a (µ+1)ES for the sphere functionJens Jägersküpper, Carsten Witt. 849-856 [doi]
• Local and global order 3/2 convergence of a surrogate evolutionary algorithmAnne Auger, Marc Schoenauer, Olivier Teytaud. 857-864 [doi]
• Counteracting genetic drift and disruptive recombination in (µ::, :::::+:::lambda)-EA on multimodal fitness landscapesMike Preuss, Lutz Schönemann, Michael Emmerich. 865-872 [doi]
• Efficient differential evolution using speciation for multimodal function optimizationXiaodong Li. 873-880 [doi]
• A differential evolution based incremental training method for RBF networksJunhong Liu, Jouni Lampinen. 881-888 [doi]
• Simple addition of ranking method for constrained optimization in evolutionary algorithmsPei Yee Ho, Kazuyuki Shimizu. 889-896 [doi]
• Morphing methods in evolutionary design optimizationMichael Nashvili, Markus Olhofer, Bernhard Sendhoff. 897-904 [doi]
• Evolutionary strategies for multi-scale radial basis function kernels in support vector machinesTanasanee Phienthrakul, Boonserm Kijsirikul. 905-911 [doi]
• Niching in evolution strategiesOfer M. Shir, Thomas Bäck. 915-916 [doi]
• A mutation operator for evolution strategies to handle constrained problemsOliver Kramer, Chuan-Kang Ting, Hans Kleine Büning. 917-918 [doi]
• Using gene deletion and gene duplication in evolution strategiesKarlheinz Schmitt. 919-920 [doi]
• Comparative evaluation of parallelization strategies for evolutionary and stochastic heuristicsSadiq M. Sait, Syed Sanaullah, Ali Mustafa Zaidi, Mustafa I. Ali. 921-922 [doi]
• Optimal number of evolution strategies mutation step sizes in dynamic environmentsLutz Schönemann. 923-924 [doi]
• Evolutionary computation applied to the tuning of MEMS gyroscopesDidier Keymeulen, Wolfgang Fink, Michael I. Ferguson, Chris Peay, Boris Oks, Richard Terrile, Karl Yee. 927-932 [doi]
• Evolving analog controllers for correcting thermoacoustic instability in real hardwareSaranyan Vigraham, John C. Gallagher, Sanjay K. Boddhu. 933-940 [doi]
• Multiple-level concatenated coding in embryonics: a dependability analysisLucian Prodan, Mihai Udrescu, Mircea Vladutiu. 941-948 [doi]
• A hardware pipeline for function optimization using genetic algorithmsMalay Kumar Pakhira, Rajat K. De. 949-956 [doi]
• Toward evolved flightRusty Hunt, Gregory Hornby, Jason D. Lohn. 957-964 [doi]
• Enhancing differential evolution performance with local search for high dimensional function optimizationNasimul Noman, Hitoshi Iba. 967-974 [doi]
• The enhanced evolutionary tabu search and its application to the quadratic assignment problemJohn F. McLoughlin III, Walter Cedeño. 975-982 [doi]
• Evolutionary rule-based system for IPO underpricing predictionDavid Quintana, Cristóbal Luque del Arco-Calderón, Pedro Isasi. 983-989 [doi]
• Two improved differential evolution schemes for faster global searchSwagatam Das, Amit Konar, Uday Kumar Chakraborty. 991-998 [doi]
• A low-level hybridization between memetic algorithm and VNS for the max-cut problemAbraham Duarte, Ángel Sánchez, Felipe Fernández, Raúl Cabido. 999-1006 [doi]
• Hybrid multiobjective genetic algorithm with a new adaptive local search processSalem F. Adra, Ian Griffin, Peter J. Fleming. 1009-1010 [doi]
• Evolutionary testing of state-based programsPhil McMinn, Mike Holcombe. 1013-1020 [doi]
• Stress testing real-time systems with genetic algorithmsLionel C. Briand, Yvan Labiche, Marwa Shousha. 1021-1028 [doi]
• An empirical study of the robustness of two module clustering fitness functionsMark Harman, Stephen Swift, Kiarash Mahdavi. 1029-1036 [doi]
• Improving network applications security: a new heuristic to generate stress testing dataConcettina Del Grosso, Giuliano Antoniol, Massimiliano Di Penta, Philippe Galinier, Ettore Merlo. 1037-1043 [doi]
• Search-based improvement of subsystem decompositionsOlaf Seng, Markus Bauer, Matthias Biehl, Gert Pache. 1045-1051 [doi]
• Using evolutionary algorithms for the unit testing of object-oriented softwareStefan Wappler, Frank Lammermann. 1053-1060 [doi]
• Search-based mutation testing for ::::Simulink:::: modelsYuan Zhan, John A. Clark. 1061-1068 [doi]
• An approach for QoS-aware service composition based on genetic algorithmsGerardo Canfora, Massimiliano Di Penta, Raffaele Esposito, Maria Luisa Villani. 1069-1075 [doi]
• Hybridizing evolutionary algorithms and clustering algorithms to find source-code clonesAndrew Sutton, Huzefa H. Kagdi, Jonathan I. Maletic, L. Gwenn Volkert. 1079-1080 [doi]
• Generating feasible input sequences for extended finite state machines (EFSMs) using genetic algorithmsKarnig Derderian, Robert M. Hierons, Mark Harman, Qiang Guo. 1081-1082 [doi]
• Benefits of software measures for evolutionary white-box testingFrank Lammermann, Stefan Wappler. 1083-1084 [doi]
• GA-based parameter tuning for multi-agent systemsJoseph Haas, Maxim Peysakhov, Spiros Mancoridis. 1085-1086 [doi]
• Memory-based immigrants for genetic algorithms in dynamic environmentsShengxiang Yang. 1115-1122 [doi]
• Advanced models of cellular genetic algorithms evaluated on SATEnrique Alba, Hugo Alfonso, Bernabé Dorronsoro. 1123-1130 [doi]
• Unbiased tournament selectionArtem Sokolov, Darrell Whitley. 1131-1138 [doi]
• Feature influence for evolutionary learningRaúl Giráldez, Jesús S. Aguilar-Ruiz. 1139-1145 [doi]
• On the stationary distribution of GAs with fixed crossover probabilityU. Chandimal de Silva, Joe Suzuki. 1147-1151 [doi]
• A theoretical analysis of the HIFF problemNicholas Freitag McPhee, Ellery Fussell Crane. 1153-1160 [doi]
• Crossover is provably essential for the ising model on treesDirk Sudholt. 1161-1167 [doi]
• Computing the epistasis variance of large-scale traveling salesman problemsDong Il Seo, Byung Ro Moon. 1169-1176 [doi]
• On favoring positive correlations between form and quality of candidate solutions via the emergence of genomic self-similarityIvan I. Garibay, Annie S. Wu, Ozlem O. Garibay. 1177-1184 [doi]
• Improving GA search reliability using maximal hyper-rectangle analysisChongshan Zhang, Khaled Rasheed. 1185-1192 [doi]
• A genetic algorithm encoding for a class of cardinality constraintsHelio J. C. Barbosa, Afonso C. C. Lemonge. 1193-1200 [doi]
• On the complexity of hierarchical problem solvingEdwin D. de Jong, Richard A. Watson, Dirk Thierens. 1201-1208 [doi]
• Measuring mobility and the performance of global search algorithmsMonte Lunacek, L. Darrell Whitley, James N. Knight. 1209-1216 [doi]
• Linkage learning, overlapping building blocks, and systematic strategy for scalable recombinationTian-Li Yu, Kumara Sastry, David E. Goldberg. 1217-1224 [doi]
• Automatic feature selection in neuroevolutionShimon Whiteson, Peter Stone, Kenneth O. Stanley, Risto Miikkulainen, Nate Kohl. 1225-1232 [doi]
• EA models and population fixed-points versus mutation rates for functions of unitationJ. Neal Richter, John Paxton, Alden H. Wright. 1233-1240 [doi]
• Phase transition in a random NK landscape modelSung-Soon Choi, Kyomin Jung, Jeong Han Kim. 1241-1248 [doi]
• Behavior of finite population variable length genetic algorithms under random selectionHal Stringer, Annie S. Wu. 1249-1255 [doi]
• Improvements to penalty-based evolutionary algorithms for the multi-dimensional knapsack problem using a gene-based adaptive mutation approachSima Uyar, Gülsen Eryigit. 1257-1264 [doi]
• Statistical analysis of heuristics for evolving sorting networksLee K. Graham, Hassan Masum, Franz Oppacher. 1265-1270 [doi]
• Fitness uniform deletion: a simple way to preserve diversityShane Legg, Marcus Hutter. 1271-1278 [doi]
• Designing resilient networks using a hybrid genetic algorithm approachAbdullah Konak, Alice E. Smith. 1279-1285 [doi]
• Information landscapes and the analysis of search algorithmsYossi Borenstein, Riccardo Poli. 1287-1294 [doi]
• The influence of migration sizes and intervals on island modelsZbigniew Skolicki, Kenneth A. De Jong. 1295-1302 [doi]
• Walsh transforms, balanced sum theorems and partition coefficients over multary alphabetsM. Teresa Iglesias, Bart Naudts, Alain Verschoren, Concepcion Vidal. 1303-1308 [doi]
• Efficient credit assignment through evaluation function decompositionAdrian K. Agogino, Kagan Tumer, Risto Miikkulainen. 1309-1316 [doi]
• Preservation of genetic redundancy in the existence of developmental error and fitness assignment errorAyse S. Yilmaz, Annie S. Wu. 1317-1324 [doi]
• From supervised ranking to evolving behaviours of a robotic teamKai Wing Tang, Ray A. Jarvis. 1325-1332 [doi]
• Takeover time curves in random and small-world structured populationsMario Giacobini, Marco Tomassini, Andrea Tettamanzi. 1333-1340 [doi]
• Genetic algorithms using low-discrepancy sequencesShuhei Kimura, Koki Matsumura. 1341-1346 [doi]
• Latent variable crossover for k-tablet structures and its application to lens design problemsJun Sakuma, Shigenobu Kobayashi. 1347-1354 [doi]
• Pricing the free lunch of meta-evolutionAlexei V. Samsonovich, Kenneth A. De Jong. 1355-1362 [doi]
• Combating user fatigue in iGAs: partial ordering, support vector machines, and synthetic fitnessXavier Llorà, Kumara Sastry, David E. Goldberg, Abhimanyu Gupta, Lalitha Lakshmi. 1363-1370 [doi]
• Applying price s equation to survival selectionJeffrey K. Bassett, Mitchell A. Potter, Kenneth A. De Jong. 1371-1378 [doi]
• Evolving neural network ensembles for control problemsDavid Pardoe, Michael Ryoo, Risto Miikkulainen. 1379-1384 [doi]
• Evolution of Voronoi based fuzzy recurrent controllersCarlos Kavka, Patricia Roggero, Marc Schoenauer. 1385-1392 [doi]
• New topologies for genetic search spaceYong-Hyuk Kim, Byung Ro Moon. 1393-1399 [doi]
• Schema disruption in tree-structured chromosomesWilliam A. Greene. 1401-1408 [doi]
• Some theoretical results about the computation time of evolutionary algorithmsLixin X. Ding, Jinghu Yu. 1409-1415 [doi]
• Adaptive isolation model using data clustering for multimodal function optimizationShin Ando, Jun Sakuma, Shigenobu Kobayashi. 1417-1424 [doi]
• Information landscapes and problem hardnessYossi Borenstein, Riccardo Poli. 1425-1431 [doi]
• Towards an analysis of dynamic environmentsJürgen Branke, Erdem Salihoglu, Sima Uyar. 1433-1440 [doi]
• Multi-level genetic algorithm (MLGA) for the construction of clock binary treeGuofang Nan, Minqiang Li, Jisong Kou. 1441-1445 [doi]
• Parallel genetic algorithms on line topology of heterogeneous computing resourcesYiyuan Gong, Morikazu Nakamura, Shiro Tamaki. 1447-1454 [doi]
• Quality-time analysis of multi-objective evolutionary algorithmsJian-Hung Chen, Shinn-Ying Ho, David E. Goldberg. 1455-1462 [doi]
• Terrain generation using genetic algorithmsTeongJoo Ong, Ryan Saunders, John Keyser, John J. Leggett. 1463-1470 [doi]
• Improving EAX with restricted 2-optChen-hsiung Chan, Sheng-An Lee, Cheng-Yan Kao, Huai-Kuang Tsai. 1471-1476 [doi]
• Application of genetic algorithm to optimize burnable poison placement in pressurized water reactorsSerkan Yilmaz, Kostadin Ivanov, Samuel Levine. 1477-1483 [doi]
• A comparison study between genetic algorithms and bayesian optimize algorithms by novel indicesNaoki Mori, Masayuki Takeda, Keinosuke Matsumoto. 1485-1492 [doi]
• The problem with a self-adaptative mutation rate in some environments: a case study using the shaky ladder hyperplane-defined functionsWilliam Rand, Rick L. Riolo. 1493-1500 [doi]
• Flight midcourse guidance control based on genetic algorithmZhao-hua Yang, Jian-cheng Fang, Zhen-qiang Qi. 1501-1506 [doi]
• Subproblem optimization by gene correlation with singular value decompositionJacob G. Martin. 1507-1514 [doi]
• Information landscapesYossi Borenstein, Riccardo Poli. 1515-1522 [doi]
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