Table of Contents

• Kernel based automatic clustering using modified particle swarm optimization algorithmAjith Abraham, Swagatam Das, Amit Konar. 2-9 [doi]
• ACOhg: dealing with huge graphsEnrique Alba, J. Francisco Chicano. 10-17 [doi]
• Informative performance metrics for dynamic optimisation problemsStefan Bird, Xiaodong Li. 18-25 [doi]
• Modelling danger and anergy in artificial immune systemsSteve Cayzer, Julie Sullivan. 26-32 [doi]
• On the runtime analysis of the 1-ANT ACO algorithmBenjamin Doerr, Frank Neumann, Dirk Sudholt, Carsten Witt. 33-40 [doi]
• Binary ant algorithmCarlos Fernandes, Agostinho C. Rosa, Vitorino Ramos. 41-48 [doi]
• Dendritic cells for SYN scan detectionJulie Greensmith, Uwe Aickelin. 49-56 [doi]
• An artificial immune system with partially specified antibodiesRamin Halavati, Saeed Bagheri Shouraki, Mojdeh Jalali Heravi, Bahareh Jafari Jashmi. 57-62 [doi]
• A particle swarm algorithm for symbols detection in wideband spatial multiplexing systemsAdnan Ahmed Khan, Muhammad Naeem, Syed Ismail Shah. 63-69 [doi]
• A particle swarm optimization approach for estimating parameter confidence regionsPraveen Koduru, Stephen Welch, Sanjoy Das. 70-77 [doi]
• A multimodal particle swarm optimizer based on fitness Euclidean-distance ratioXiaodong Li. 78-85 [doi]
• Finding interesting thingsSean Luke, Deepankar Sharma, Gabriel Catalin Balan. 86-93 [doi]
• ACO vs EAs for solving a real-world frequency assignment problem in GSM networksFrancisco Luna, Christian Blum, Enrique Alba, Antonio J. Nebro. 94-101 [doi]
• Vulnerability analysis and security framework (BeeSec) for nature inspired MANET routing protocolsNauman Mazhar, Muddassar Farooq. 102-109 [doi]
• Earthquake classifying neural networks trained with random dynamic neighborhood PSOsArvind S. Mohais, Rosemarie Mohais, Christopher Ward, Christian Posthoff. 110-117 [doi]
• Geometric particle swarm optimization for the sudoku puzzleAlberto Moraglio, Julian Togelius. 118-125 [doi]
• A discrete differential evolution algorithm for the permutation flowshop scheduling problemQuan-Qe Pan, Mehmet Fatih Tasgetiren, Yun-Chia Liang. 126-133 [doi]
• Exact analysis of the sampling distribution for the canonical particle swarm optimiser and its convergence during stagnationRiccardo Poli, David Broomhead. 134-141 [doi]
• Markov chain models of bare-bones particle swarm optimizersRiccardo Poli, William B. Langdon. 142-149 [doi]
• Analyzing heuristic performance with response surface models: prediction, optimization and robustnessEnda Ridge, Daniel Kudenko. 150-157 [doi]
• A discrete particle swarm optimization algorithm for the generalized traveling salesman problemMehmet Fatih Tasgetiren, Ponnuthurai N. Suganthan, Quan-Qe Pan. 158-167 [doi]
• Adaptive particle swarm optimizer with nonextensive scheduleAristoklis D. Anastasiadis, George D. Georgoulas, George D. Magoulas, Anthony Tzes. 168 [doi]
• Honey bee foraging algorithm for multimodal & dynamic optimization problemsAbdul Rauf Baig, M. Rashid. 169 [doi]
• The effectiveness of dynamic ant colony tuningAdrian A. de Freitas, Christopher B. Mayer. 170 [doi]
• Suppression based immune mechanism to find arepresentative training set in data classification tasksGrazziela Patrocinio Figueredo, Nelson F. F. Ebecken, Helio J. C. Barbosa. 171 [doi]
• Binary particle swarm optimization: a forma analysis approachTao Gong, Andrew Tuson. 172 [doi]
• A heuristic particle swarm optimizationHoang Thanh Lam, Popova Nina Nicolaevna, Nguyen Thoi Minh Quan. 174 [doi]
• Multi-objective pso for interplanetary trajectory designMichèle Lavagna. 175 [doi]
• Solving optimal pump control problem using max-min ant systemManuel López-Ibáñez, T. Devi Prasad, Ben Paechter. 176 [doi]
• MRPSO: MapReduce particle swarm optimizationAndrew W. McNabb, Christopher K. Monson, Kevin D. Seppi. 177 [doi]
• Quick-and-dirty ant colony optimizationPaola Pellegrini, Elena Moretti. 178 [doi]
• Scalability of particle swarm algorithmsSébastien Piccand, Michael O Neill, Jacqueline Walker. 179 [doi]
• Screening the parameters affecting heuristic performanceEnda Ridge, Daniel Kudenko. 180 [doi]
• Keyword extraction using an artificial immune systemAndres Romero, Fernando Niño. 181 [doi]
• Extended thymus action for reducing false positives in ais based network intrusion detection systemsM. Zubair Shafiq, Mehrin Kiani, Bisma Hashmi, Muddassar Farooq. 182 [doi]
• A strongly interacting dynamic particle swarm optimizational methodJan A. Snyman, Schalk Kok. 183 [doi]
• Swarming with logicRobert L. Stewart, Michael Kirley. 184 [doi]
• Hybrid quantum particle swarm optimization algorithm for combinatorial optimization problemJiahai Wang, Yalan Zhou. 185 [doi]
• SwarmArchitect: a swarm framework for collaborative constructionYifeng Zeng, Jorge Cordero Hernandez, Dennis Plougman Buus. 186 [doi]
• Robust scheduling method based on workflow simulation model and biological immune principleXingquan Zuo. 187 [doi]
• Synthesis of analog filters on an evolvable hardware platform using a genetic algorithmJoachim Becker, Stanis Trendelenburg, Fabian Henrici, Yiannos Manoli. 190-197 [doi]
• Action-selection and crossover strategies for self-modeling machinesJosh C. Bongard. 198-205 [doi]
• A biologically inspired solution for an evolved simulated agentEdgar Bermudez Contreras. 206-213 [doi]
• Exploiting multiple robots to accelerate self-modelingJosh C. Bongard. 214-221 [doi]
• The effect of learning on life history evolutionJohn A. Bullinaria. 222-229 [doi]
• EcoPS: a particle swarm algorithm to model group-foragingCecilia Di Chio, Riccardo Poli, Paolo Di Chio. 230-237 [doi]
• Dynamical blueprints: exploiting levels of system-environment interactionNicolás S. Estévez, Hod Lipson. 238-244 [doi]
• Reducing the number of transistors in digital circuits using gate-level evolutionary designZbysek Gajda, Lukás Sekanina. 245-252 [doi]
• Stochastic training of a biologically plausible spino-neuromuscular system modelStanley Phillips Gotshall, Terence Soule. 253-260 [doi]
• Procreating V-detectors for nonself recognition: an application to anomaly detection in power systemsMin Gui, Sanjoy Das, Anil Pahwa. 261-268 [doi]
• Coevolution of intelligent agents using cartesian genetic programmingGul Muhammad Khan, Julian Francis Miller, David M. Halliday. 269-276 [doi]
• Hill climbing on discrete HIFF: exploring the role of DNA transposition in long-term artificial evolutionSusan Khor. 277-284 [doi]
• A quantitative analysis of memory requirement and generalization performance for robotic tasksDaeEun Kim. 285-292 [doi]
• Using group selection to evolve leadership in populations of self-replicating digital organismsDavid B. Knoester, Philip K. McKinley, Charles Ofria. 293-300 [doi]
• Artificial ecosystems for creative discoveryJon McCormack. 301-307 [doi]
• Takeover times on scale-free topologiesJoshua L. Payne, Margaret J. Eppstein. 308-315 [doi]
• Division blocks and the open-ended evolution of development, form, and behaviorLee Spector, Jon Klein, Mark Feinstein. 316-323 [doi]
• Nonlinear dynamics modelling for controller evolutionJulian Togelius, Renzo De Nardi, Hugo Gravato Marques, Richard Newcombe, Simon M. Lucas, Owen Holland. 324-333 [doi]
• Bee SLAM: a probabilistic framework for studying orientation flights in bees and waspsBart Baddeley, Andrew Philippides. 334 [doi]
• Balancing quality and quantity in evolving agent systemsGuszti Eiben, Joeri Bekker, Robert Griffioen, Evert Haasdijk. 335 [doi]
• A framework for the emergence of intra-species mutual recognition patternsNicolas Brodu. 336 [doi]
• The effects of lifetime learning on the diversity and fitness of populationsDara Curran, Colm O Riordan, Humphrey Sorensen. 337 [doi]
• Self-adaptation of cultural learning parametersDara Curran, Colm O Riordan, Humphrey Sorensen. 338 [doi]
• Fuzzy cognitive map learning based on multi-objectiveparticle swarm optimizationHengjie Song, Chunyan Miao, Zhiqi Shen. 339 [doi]
• A simulation of evolved autotrophic reproductionCorrey Allen Kowall, Brian J. Krent. 340 [doi]
• Evolving virtual creatures revisitedPeter Krcah. 341 [doi]
• Collective specialization in multi-rover systemsGeoff Nitschke, Martijn C. Schut, A. E. Eiben. 342 [doi]
• Preliminary investigations into the evolution of cooperative strategies in a minimally spatial modelSimon T. Powers, Richard A. Watson. 343 [doi]
• An evolutionary online adaptation method for modern computer games based on imitationSteffen Priesterjahn, Alexander Weimer. 344-345 [doi]
• Automated alphabet reduction method with evolutionary algorithms for protein structure predictionJaume Bacardit, Michael Stout, Jonathan D. Hirst, Kumara Sastry, Xavier Llorà, Natalio Krasnogor. 346-353 [doi]
• Discrimination of metabolic flux profiles using a hybrid evolutionary algorithmStefan Bleuler, Eckart Zitzler. 354-360 [doi]
• Parsimonious regularization using genetic algorithms applied to the analysis of analytical ultracentrifugation experimentsEmre H. Brookes, Borries Demeler. 361-368 [doi]
• Initial results from the use of learning classifier systems to control ::::in vitro:::: neuronal networksLarry Bull, Ivan S. Uroukov. 369-376 [doi]
• TFBS identification by position- and consensus-led genetic algorithm with local filteringTak-Ming Chan, Kwong-Sak Leung, Kin-Hong Lee. 377-384 [doi]
• A multi-objective approach to discover biclusters in microarray dataFederico Divina, Jesús S. Aguilar-Ruiz. 385-392 [doi]
• A novel ab-initio genetic-based approach for protein folding predictionSergio Raul Duarte Torres, David Camilo Becerra Romero, Luis Fernando Niño Vasquez, Yoan José Pinzón Ardila. 393-400 [doi]
• Evolutionary selection of minimum number of features for classification of gene expression data using genetic algorithmsAlper Küçükural, Reyyan Yeniterzi, Süveyda Yeniterzi, Osman Ugur Sezerman. 401-406 [doi]
• Clustering gene expression data via mining ensembles of classification rules evolved using mosesMoshe Looks, Ben Goertzel, Lúcio de Souza Coelho, Mauricio Mudado, Cassio Pennachin. 407-414 [doi]
• A platform for the selection of genes in DNA microarraydata using evolutionary algorithmsMiguel Rocha, Rui Mendes, Paulo Maia, Daniel Glez-Peña, Florentino Fdez-Riverola. 415-423 [doi]
• Ab initio protein structure prediction with a dipeptide-assembly evolutionary algorithmAndrea Bazzoli, Giorgio Colombo, Andrea Tettamanzi. 424 [doi]
• Evolutionary approach to protein structure prediction with hydrophobic interactionsTelma Woerle de Lima, Rodrigo Antonio Faccioli, Paulo Henrique Ribeiro Gabriel, Alexandre C. B. Delbem, Ivan Nunes da Silva. 425 [doi]
• Hebbian learning in a simple gene circuitChrisantha Thomas Fernando, Jon Rowe. 426 [doi]
• A comparison of PSO and GA approaches for gene selection and classification of microarray dataJosé García-Nieto, Enrique Alba, Laetitia Jourdan, El-Ghazali Talbi. 427 [doi]
• Gene finding and rule discovery with a multi-objective neural-genetic hybridEd Keedwell, Ajit Narayanan. 428 [doi]
• Using DNA to generate 3D organic art formsWilliam H. Latham, Miki Shaw, Stephen Todd, Frederic F. Leymarie. 429 [doi]
• Understanding microarray data through applying competent program evolutionMoshe Looks, Ben Goertzel, Lúcio de Souza Coelho, Mauricio Mudado, Cassio Pennachin. 430 [doi]
• Induction of fuzzy rules with artificial immune systems in acgh based er status breast cancer characterizationFilippo Menolascina, Roberto Teixeira Alves, Stefania Tommasi, Patrizia Chiarappa, Myriam Regattieri Delgado, Giuseppe Mastronardi, Angelo Paradiso, Alex Alves Freitas, Vitoantonio Bevilacqua. 431 [doi]
• Towards human-human-computer interaction for biologically-inspired problem-solving in human geneticsJason H. Moore, Nate Barney, Bill C. White. 432-433 [doi]
• Thoughts on solution conceptsAnthony Bucci, Jordan B. Pollack. 434-439 [doi]
• Objective fitness correlationEdwin D. de Jong. 440-447 [doi]
• Optimal nesting of species for exact cover of resources: two against manyJeffrey Horn. 448-455 [doi]
• Hybrid coevolutionary algorithms vs. SVM algorithmsRui Li, Bir Bhanu, Krzysztof Krawiec. 456-463 [doi]
• Pareto-coevolutionary genetic programming for problem decomposition in multi-class classificationPeter Lichodzijewski, Malcolm I. Heywood. 464-471 [doi]
• Towards the coevolution of cellular automata controllers for chemical computing with the B-Z reactionChristopher Stone, Rita Toth, Andrew Adamatzky, Ben de Lacy Costello, Larry Bull. 472-478 [doi]
• A comparison of evaluation methods in coevolutionTing-Shuo Yo, Edwin D. de Jong. 479-487 [doi]
• Guided mutations in cooperative coevolutionary algorithms for function optimizationChun-Kit Au, Ho-Fung Leung. 488 [doi]
• On the number of subpopulations in coevolutionary computation: a database applicationRui Li, Bir Bhanu, Krzysztof Krawiec. 489 [doi]
• A spatial model of the red queen effectJules Ottino-Loffler, William Rand, Uri Wilensky. 490-491 [doi]
• SDR: a better trigger for adaptive variance scaling in normal EDAsPeter A. N. Bosman, Jörn Grahl, Franz Rothlauf. 492-499 [doi]
• Adaptive variance scaling in continuous multi-objective estimation-of-distribution algorithmsPeter A. N. Bosman, Dirk Thierens. 500-507 [doi]
• Addressing sampling errors and diversity loss in UMDAJürgen Branke, Clemens Lode, Jonathan L. Shapiro. 508-515 [doi]
• Convergence phases, variance trajectories, and runtime analysis of continuous EDAsJörn Grahl, Peter A. N. Bosman, Stefan Minner. 516-522 [doi]
• Analyzing probabilistic models in hierarchical BOA on traps and spin glassesMark Hauschild, Martin Pelikan, Cláudio F. Lima, Kumara Sastry. 523-530 [doi]
• ECGA vs. BOA in discovering stock market trading expertsPiotr Lipinski. 531-538 [doi]
• Scalable estimation-of-distribution program evolutionMoshe Looks. 539-546 [doi]
• Hybrid evolutionary algorithms on minimum vertex cover for random graphsMartin Pelikan, Rajiv Kalapala, Alexander K. Hartmann. 547-554 [doi]
• Order or not: does parallelization of model building in hBOA affect its scalability?Martin Pelikan, James D. Laury Jr.. 555-561 [doi]
• Estimation of fitness landscape contours in EAsPetr Posík, Vojtech Franc. 562-569 [doi]
• An estimation of distribution algorithm with guided mutation for a complex flow shop scheduling problemAbdellah Salhi, José Antonio Vázquez Rodríguez, Qingfu Zhang. 570-576 [doi]
• Towards billion-bit optimization via a parallel estimation of distribution algorithmKumara Sastry, David E. Goldberg, Xavier Llorà. 577-584 [doi]
• An application of EDA and GA to dynamic pricingSiddhartha Shakya, Fernando Oliveira, Gilbert Owusu. 585-592 [doi]
• Multiobjective real-coded bayesian optimization algorithmrevisited: diversity preservationChang Wook Ahn, Rudrapatna S. Ramakrishna. 593-600 [doi]
• Population sizing for entropy-based model building in discrete estimation of distribution algorithmsTian-Li Yu, Kumara Sastry, David E. Goldberg, Martin Pelikan. 601-608 [doi]
• Cross entropy and adaptive variance scaling in continuous EDAYunpeng Cai, Xiaomin Sun, Hua Xu, Peifa Jia. 609-616 [doi]
• Global multiobjective optimization via estimation of distribution algorithm with biased initialization and crossoverAimin Zhou, Qingfu Zhang, Yaochu Jin, Bernhard Sendhoff, Edward P. K. Tsang. 617-623 [doi]
• Why is parity hard for estimation of distribution algorithms?David Jonathan Coffin, Robert Elliott Smith. 624 [doi]
• Investigation of mutation operators for the bayesian optimization algorithmEric Martin Heien, Tomoyuki Hiroyasu, Noriyuki Fujimoto. 625 [doi]
• Meta-optimizing semantic evolutionary searchMoshe Looks. 626 [doi]
• Variance reduction in meta-EDAVolker Nannen, A. E. Eiben. 627 [doi]
• Obtaining ground states of ising spin glasses via optimizing bonds instead of spinsMartin Pelikan, Alexander K. Hartmann. 628 [doi]
• Dependency trees, permutations, and quadratic assignment problemMartin Pelikan, Shigeyoshi Tsutsui, Rajiv Kalapala. 629 [doi]
• A synthesis of optimal stopping time in compact genetic algorithm based on real options approachSunisa Rimcharoen, Daricha Sutivong, Prabhas Chongstitvatana. 630 [doi]
• Migration of probabilistic models for island-based bivariate EDA algorithmJosef Schwarz, Jirí Jaros, Jiri Ocenasek. 631 [doi]
• Parameter cross-validation and early-stopping in univariate marginal distribution algorithmHao Wu, Jonathan L. Shapiro. 632-633 [doi]
• On the use of evolution strategies for optimising certain positive definite quadratic formsDirk V. Arnold. 634-641 [doi]
• Estimating the spectral sensitivity of a digital sensor using calibration targetsMarc Ebner. 642-649 [doi]
• Particle swarm guided evolution strategyChang-Tai Hsieh, Chih-Ming Chen, Ying-Ping Chen. 650-657 [doi]
• Reducing the space-time complexity of the CMA-ESJames N. Knight, Monte Lunacek. 658-665 [doi]
• Sex and death: towards biologically inspired heuristics for constraint handlingOliver Kramer, Stephan Brügger, Dejan Lazovic. 666-673 [doi]
• An experimental analysis of evolution strategies and particle swarm optimisers using design of experimentsOliver Kramer, Bartek Gloger, Andreas Goebels. 674-681 [doi]
• A hybrid evolutionary programming algorithm for spread spectrum radar polyphase codes designÁngel M. Pérez-Bellido, Sancho Salcedo-Sanz, Emilio G. Ortíz-García, Antonio Portilla-Figueras. 682-688 [doi]
• An extended mutation concept for the local selection based differential evolution algorithmJani Rönkkönen, Jouni Lampinen. 689-696 [doi]
• Two adaptive mutation operators for optima tracking in dynamic optimization problems with evolution strategiesClaudio Rossi, Antonio Barrientos, Jaime del Cerro. 697-704 [doi]
• Performance analysis of niching algorithms based on derandomized-ES variantsOfer M. Shir, Thomas Bäck. 705-712 [doi]
• The second harmonic generation case-study as a gateway for es to quantum control problemsOfer M. Shir, Thomas Bäck. 713-721 [doi]
• Using evolution strategies for automatic extraction of parameters for stellar population synthesis of galaxy spectra from sdssJuan Carlos Gomez, Olac Fuentes. 722 [doi]
• Strengths and weaknesses of FSA representationPavel Petrovic. 723-725 [doi]
• Two-level of nondominated solutions approach to multiobjective particle swarm optimizationM. A. Abido. 726-733 [doi]
• An informed convergence accelerator for evolutionary multiobjective optimiserSalem F. Adra, Ian Griffin, Peter J. Fleming. 734-740 [doi]
• COSMO: a correlation sensitive mutation operator for multi-objective optimizationVarun Aggarwal, Una-May O Reilly. 741-748 [doi]
• An evolutionary multiobjective approach to design highly non-linear Boolean functionsHernán E. Aguirre, Hiroyuki Okazaki, Yasushi Fuwa. 749-756 [doi]
• Alternative techniques to solve hard multi-objective optimization problemsRicardo Landa Becerra, Carlos A. Coello Coello, Alfredo García Hernández-Díaz, Rafael Caballero, Julián Molina Luque. 757-754 [doi]
• Do additional objectives make a problem harder?Dimo Brockhoff, Tobias Friedrich, Nils Hebbinghaus, Christian Klein, Frank Neumann, Eckart Zitzler. 765-772 [doi]
• Techniques for highly multiobjective optimisation: some nondominated points are better than othersDavid W. Corne, Joshua D. Knowles. 773-780 [doi]
• Interactive evolutionary multi-objective optimization and decision-making using reference direction methodKalyanmoy Deb, Abhishek Kumar. 781-788 [doi]
• Methodology to select solutions from the pareto-optimal set: a comparative studyJ. C. Ferreira, Carlos M. Fonseca, António Gaspar-Cunha. 789-796 [doi]
• Approximating covering problems by randomized search heuristics using multi-objective modelsTobias Friedrich, Nils Hebbinghaus, Frank Neumann, Jun He, Carsten Witt. 797-804 [doi]
• Exploring the behavior of building blocks for multi-objective variation operator design using predator-prey dynamicsChristian Grimme, Joachim Lepping, Alexander Papaspyrou. 805-812 [doi]
• Uniform sampling of local pareto-optimal solution curves by pareto path following and its applications in multi-objective GAKen Harada, Jun Sakuma, Shigenobu Kobayashi, Isao Ono. 813-820 [doi]
• Using evolutionary computation and local search to solve multi-objective flexible job shop problemsNhu Binh Ho, Joc Cing Tay. 821-828 [doi]
• Effects of the use of non-geometric binary crossover on evolutionary multiobjective optimizationHisao Ishibuchi, Yusuke Nojima, Noritaka Tsukamoto, Ken Ohara. 829-836 [doi]
• SNDL-MOEA: stored non-domination level MOEAMatt D. Johnson, Daniel R. Tauritz, Ralph W. Wilkerson. 837-844 [doi]
• An analysis of the effects of population structure on scalable multiobjective optimization problemsMichael Kirley, Robert L. Stewart. 845-852 [doi]
• Multi-objective hybrid PSO using µ-fuzzy dominancePraveen Koduru, Sanjoy Das, Stephen Welch. 853-860 [doi]
• Multiobjective clustering with automatic k-determination for large-scale dataNobukazu Matake, Tomoyuki Hiroyasu, Mitsunori Miki, Tomoharu Senda. 861-868 [doi]
• Multi-objective particle swarm optimization on computer gridsSanaz Mostaghim, Jürgen Branke, Hartmut Schmeck. 869-875 [doi]
• Optimal antenna placement using a new multi-objective chc algorithmAntonio J. Nebro, Enrique Alba, Guillermo Molina, J. Francisco Chicano, Francisco Luna, Juan José Durillo. 876-883 [doi]
• Guided hyperplane evolutionary algorithmCorina Rotar, D. Dumitrescu, Rodica Ioana Lung. 884-891 [doi]
• Convergence of stochastic search algorithms to gap-free pareto front approximationsOliver Schütze, Marco Laumanns, Emilia Tantar, Carlos A. Coello Coello, El-Ghazali Talbi. 892-901 [doi]
• Interactive multi-objective design of long-span trussesBreanna W. Bailey, Anne M. Raich. 902 [doi]
• An EC-memory based method for the multi-objective TSPIstván Borgulya. 903 [doi]
• Discussion of parallel model of multi-objective genetic algorithms on heterogeneous computational resourcesTomoyuki Hiroyasu, Kengo Yoshii, Mitsunori Miki. 904 [doi]
• Multiobjective evolutionary algorithms for designing capacitated network centric communicationsMark P. Kleeman, Gary B. Lamont, Kenneth M. Hopkinson, Scott R. Graham. 905 [doi]
• Parallel skeleton for multi-objective optimizationCoromoto León, Gara Miranda, Carlos Segura. 906 [doi]
• Performance measures and particle swarm methods for dynamic multi-objective optimization problemsXiaodong Li, Jürgen Branke, Michael Kirley. 907 [doi]
• A framework of quantum-inspired multi-objective evolutionary algorithms and its convergence conditionZhiyong Li, Günter Rudolph. 908 [doi]
• Bicriteria two-machine flowshop scheduling using metaheuristicsS. Afshin Mansouri, S. Hamed Hendizadeh, Nasser Salmasi. 909 [doi]
• Incremental refinement of solutions for multiple objective optimization problemsCarlos Eduardo Mariano, Victor Hugo Alcocer, Eduardo F. Morales. 910 [doi]
• A cumulative evidential stopping criterion for multiobjective optimization evolutionary algorithmsLuis Martí, Jesús García, Antonio Berlanga, José Manuel Molina. 911 [doi]
• Hybrid multiobjective optimization genetic algorithms for graph drawingDana Vrajitoru. 912 [doi]
• Dimensionality reduction in evolutionary multiobjective design: case studyPiotr Wozniak. 913-915 [doi]
• Continuous lunches are free!Anne Auger, Olivier Teytaud. 916-922 [doi]
• Enhanced forma analysis of permutation problemsTao Gong, Andrew Tuson. 923-930 [doi]
• Stability in the self-organized evolution of networksThomas Jansen, Madeleine Theile. 931-938 [doi]
• Analysis of evolutionary algorithms for the longest common subsequence problemThomas Jansen, Dennis Weyland. 939-946 [doi]
• Evolutionary algorithms and matroid optimization problemsJoachim Reichel, Martin Skutella. 947-954 [doi]
• DCMA: yet another derandomization in covariance-matrix-adaptationOlivier Teytaud, Sylvain Gelly. 955-963 [doi]
• The channel capacity of evolutionChristopher J. C. H. Watkins. 964-965 [doi]
• Using feedback to regulate gene expression in a developmental control architectureKester Clegg, Susan Stepney, Tim Clarke. 966-973 [doi]
• A novel generative encoding for exploiting neural network sensor and output geometryDavid B. D Ambrosio, Kenneth O. Stanley. 974-981 [doi]
• Robust multi-cellular developmental designAlexandre Devert, Nicolas Bredeche, Marc Schoenauer. 982-989 [doi]
• Supplementing evolutionary developmental systems with abstract models of neurogenesisKeith L. Downing. 990-996 [doi]
• Generating large-scale neural networks through discovering geometric regularitiesJason Gauci, Kenneth O. Stanley. 997-1004 [doi]
• Methods for open-box analysis in artificial developmentAdrian Grajdeanu. 1005-1012 [doi]
• Achieving a simple development model for 3D shapes: are chemicals necessary?Pauline C. Haddow, Johan Hoye. 1013-1020 [doi]
• Self-modifying cartesian genetic programmingSimon Harding, Julian Francis Miller, Wolfgang Banzhaf. 1021-1028 [doi]
• A common genetic encoding for both direct and indirect encodings of networksYohannes Kassahun, Mark Edgington, Jan Hendrik Metzen, Gerald Sommer, Frank Kirchner. 1029-1036 [doi]
• Environment as a spatial constraint on the growth of structural formTaras Kowaliw, Peter Grogono, Nawwaf N. Kharma. 1037-1044 [doi]
• Acquiring evolvability through adaptive representationsJoseph Reisinger, Risto Miikkulainen. 1045-1052 [doi]
• Learning recursive programs with cooperative coevolution of genetic code mapping and genotypeGarnett Carl Wilson, Malcolm I. Heywood. 1053-1061 [doi]
• Use of a genetic algorithm to evolve an extended artificial regulatory network for cell pattern generationArturo Chavoya, Yves Duthen. 1062 [doi]
• Growing form-filling tensegrity structures using map L-systemsJohn Rieffel, Hod Lipson, Francisco J. Valero Cuevas. 1063 [doi]
• Growth and development of continuous structuresOr Yogev, Erik K. Antonsson. 1064-1065 [doi]
• Finding safety errors with ACOEnrique Alba, J. Francisco Chicano. 1066-1073 [doi]
• Automatic mutation test input data generation via ant colonyKamel Ayari, Salah Bouktif, Giuliano Antoniol. 1074-1081 [doi]
• One-test-at-a-time heuristic search for interaction test suitesRenée C. Bryce, Charles J. Colbourn. 1082-1089 [doi]
• Search-based testing of service level agreementsMassimiliano Di Penta, Gerardo Canfora, Gianpiero Esposito, Valentina Mazza, Marcello Bruno. 1090-1097 [doi]
• A multi-objective approach to search-based test data generationKiran Lakhotia, Mark Harman, Phil McMinn. 1098-1105 [doi]
• Pareto optimal search based refactoring at the design levelMark Harman, Laurence Tratt. 1106-1113 [doi]
• Getting the most from search-based refactoringMark Kent O Keeffe, Mel Ó Cinnéide. 1114-1120 [doi]
• Applying particle swarm optimization to software testingAndreas Windisch, Stefan Wappler, Joachim Wegener. 1121-1128 [doi]
• The multi-objective next release problemYuanyuan Zhang, Mark Harman, S. Afshin Mansouri. 1129-1137 [doi]
• Generalized extremal optimization: an attractive alternative for test data generationBruno T. de Abreu, Eliane Martins, Fabiano Luis de Sousa. 1138 [doi]
• Optimizing the V&V process for critical systemsJames D. Kiper, Martin S. Feather, Julian Richardson. 1139 [doi]
• Investigating data-flow coverage of classes using evolutionary algorithmsKonstantinos Liaskos, Marc Roper, Murray Wood. 1140 [doi]
• Fitness calculation approach for nested if-else construct in evolutionary testingXiyang Liu, Lei Wang, Xiubin Zhu, Zhiwen Bai, Miao Zhang, Hehui Liu. 1141 [doi]
• The defined cliffs variant in dynamic environments: a case study using the shaky ladder hyperplane-defined functionsAbir Alharbi, William Rand, Rick L. Riolo. 1158-1164 [doi]
• Learning and anticipation in online dynamic optimization with evolutionary algorithms: the stochastic casePeter A. N. Bosman, Han La Poutré. 1165-1172 [doi]
• GARS: an improved genetic algorithm with reserve selection for global optimizationYang Chen, Jinglu Hu, Kotaro Hirasawa, Songnian Yu. 1173-1178 [doi]
• Nonlinearity linkage detection for financial time series analysisTheodore Chiotis, Christopher D. Clack. 1179-1186 [doi]
• Self-adaptive simulated binary crossover for real-parameter optimizationKalyanmoy Deb, Karthik Sindhya, Tatsuya Okabe. 1187-1194 [doi]
• Improving global numerical optimization using a search-space reduction algorithmVinicius Veloso de Melo, Alexandre C. B. Delbem, Dorival Leao Pinto Junior, Fernando Marques Federson. 1195-1202 [doi]
• Adjacency list matchings: an ideal genotype for cycle coversBenjamin Doerr, Daniel Johannsen. 1203-1210 [doi]
• Genetic algorithms for large join query optimizationHongbin Dong, Yiwen Liang. 1211-1218 [doi]
• Rigorous analyses of simple diversity mechanismsTobias Friedrich, Nils Hebbinghaus, Frank Neumann. 1219-1225 [doi]
• A memetic algorithm for the low autocorrelation binary sequence problemJosé E. Gallardo, Carlos Cotta, Antonio J. Fernández. 1226-1233 [doi]
• Analyzing the effects of module encapsulation on search space biasOzlem O. Garibay, Annie S. Wu. 1234-1241 [doi]
• Genetic multi-step search in interpolation and extrapolation domainYoshiko Hanada, Tomoyuki Hiroyasu, Mitsunori Miki. 1242-1249 [doi]
• A self-adaptive multiagent evolutionary algorithm for electrical machine designJean-Laurent Hippolyte, Christelle Bloch, Pascal Chatonnay, Christophe Espanet, Didier Chamagne. 1250-1255 [doi]
• Overcoming hierarchical difficulty by hill-climbing the building block structureDavid Iclanzan, Dan Dumitrescu. 1256-1263 [doi]
• A chain-model genetic algorithm for Bayesian network structure learningRatiba Kabli, Frank Herrmann, John McCall. 1264-1271 [doi]
• A doubly distributed genetic algorithm for network codingMinkyu Kim, Varun Aggarwal, Una-May O Reilly, Muriel Médard. 1272-1279 [doi]
• Learning building block structure from crossover failureZhenhua Li, Erik D. Goodman. 1280-1287 [doi]
• A study on metamodeling techniques, ensembles, and multi-surrogates in evolutionary computationDudy Lim, Yew-Soon Ong, Yaochu Jin, Bernhard Sendhoff. 1288-1295 [doi]
• A new evolutionary model for detecting multiple optimaRodica Ioana Lung, D. Dumitrescu. 1296-1303 [doi]
• Center of mass encoding: a self-adaptive representation with adjustable redundancy for real-valued parametersClaudio Mattiussi, Peter Dürr, Dario Floreano. 1304-1311 [doi]
• Variable discrimination of crossover versus mutation using parameterized modular structureRob Mills, Richard A. Watson. 1312-1319 [doi]
• Some novel locality results for the blob code spanning tree representationTim Paulden, David K. Smith. 1320-1327 [doi]
• A gestalt genetic algorithm: less details for better searchChristophe Philemotte, Hugues Bersini. 1328-1334 [doi]
• Fitness-proportional negative slope coefficient as a hardness measure for genetic algorithmsRiccardo Poli, Leonardo Vanneschi. 1335-1342 [doi]
• Finding critical backbone structures with genetic algorithmsAdam Prügel-Bennett. 1343-1348 [doi]
• Hardware acceleration of multi-deme genetic algorithm for the application of DNA codeword searchingQinru Qiu, Daniel J. Burns, Prakash Mukre, Qing Wu. 1349-1356 [doi]
• ExGA II: an improved exonic genetic algorithm for the multiple knapsack problemPhilipp Rohlfshagen, John A. Bullinaria. 1357-1364 [doi]
• A genetic algorithm with exon shuffling crossover for hard bin packing problemsPhilipp Rohlfshagen, John A. Bullinaria. 1365-1371 [doi]
• A genetic algorithm for privacy preserving combinatorial optimizationJun Sakuma, Shigenobu Kobayashi. 1372-1379 [doi]
• Let s get ready to rumble redux: crossover versus mutation head to head on exponentially scaled problemsKumara Sastry, David E. Goldberg. 1380-1387 [doi]
• Empirical analysis of ideal recombination on random decomposable problemsKumara Sastry, Martin Pelikan, David E. Goldberg. 1388-1395 [doi]
• Analysis of noisy time-series signals with GA involving viral infection with tropismYuji Sato, Yuta Yasuda, Ryuji Goto. 1396-1403 [doi]
• A study of mutational robustness as the product of evolutionary computationJustin Schonfeld. 1404-1411 [doi]
• Credit assignment in adaptive memetic algorithmsJ. E. Smith. 1412-1419 [doi]
• Disburdening the species conservation evolutionary algorithm of arguing with radiiCatalin Stoean, Mike Preuss, Ruxandra Stoean, Dumitru Dumitrescu. 1420-1427 [doi]
• Differential evolution and non-separability: using selective pressure to focus searchAndrew M. Sutton, Monte Lunacek, L. Darrell Whitley. 1428-1435 [doi]
• On the relativity in the assessment of blind optimization algorithms and the problem-algorithm coevolutionCarlos D. Toledo-Suárez, Manuel Valenzuela-Rendón, Hugo Terashima-Marín, Eduardo Uresti-Charre. 1436-1443 [doi]
• Distribution replacement: how survival of the worst can out perform survival of the fittestHoward Tripp, Phil Palmer. 1444-1451 [doi]
• A building-block royal road where crossover is provably essentialRichard A. Watson, Thomas Jansen. 1452-1459 [doi]
• Peer-to-peer evolutionary algorithms with adaptive autonomous selectionW. R. M. U. K. Wickramasinghe, Maarten van Steen, A. E. Eiben. 1460-1467 [doi]
• Another investigation on tournament selection: modelling and visualisationHuayang Xie, Mengjie Zhang, Peter Andreae. 1468-1475 [doi]
• Effects of passenger s arrival distribution to double-deck elevator group supervisory control systems using genetic network programmingLu Yu, Jin Zhou, Shingo Mabu, Kotaro Hirasawa, Jinglu Hu, Sandor Markon. 1476-1483 [doi]
• Extended probe method for linkage discovery over high-cardinality alphabetsShude Zhou, Zengqi Sun, Robert B. Heckendorn. 1484-1491 [doi]
• Parallel genetic algorithm: assessment of performance in multidimensional scalingAntanas Zilinskas, Julius Zilinskas. 1492-1501 [doi]
• A fuzzy genetic algorithm for the dynamic cell formation problemMenouar Boulif, Karim Atif. 1502 [doi]
• Trading rules on stock markets using genetic network programming with sarsa learningYan Chen, Shingo Mabu, Kotaro Hirasawa, Jinglu Hu. 1503 [doi]
• On the roles of redundancy and neutrality in evolutionary optimization: an experimental studyMarisol B. Correia, Carlos M. Fonseca. 1504 [doi]
• The effects of solution density in the search space on finding spatially robust solutionsGrzegorz Drzadzewski, Mark Wineberg. 1505 [doi]
• Autonomous selection in evolutionary algorithmsA. E. Eiben, Marc Schoenauer, D. W. F. van Krevelen, M. C. Hobbelman, M. A. ten Hagen, R. C. van het Schip. 1506 [doi]
• Generating classification trees for small disjuncts using incremental gasMagda Bahaa Eldin Fayek, Amira Samy Talaat, Nevin Mahmoud Darwish. 1507 [doi]
• How and why a bit-wise neutrality with and without locality affects evolutionary searchEdgar Galván López, Riccardo Poli. 1508 [doi]
• Towards understanding the effects of neutrality on the sudoku problemEdgar Galván López, Julian Togelius, Simon M. Lucas. 1509 [doi]
• Using metaheuristic algorithms remotely via ROSJosé García-Nieto, Enrique Alba, J. Francisco Chicano. 1510 [doi]
• Collaborative evolutionary algorithms for combinatorial optimizationAnca Gog, D. Dumitrescu, Béat Hirsbrunner. 1511 [doi]
• Genetic network programming with parallel processing for association rule mining in large and dense databasesEloy Gonzales, Kaoru Shimada, Shingo Mabu, Kotaro Hirasawa, Jinglu Hu. 1512 [doi]
• Linear selectionMario Graff, Riccardo Poli, Alberto Moraglio. 1513 [doi]
• A simple genetic algorithm for reducible complexityLee K. Graham, Steffen Christensen, Franz Oppacher. 1514 [doi]
• Symbiotic tabu searchRamin Halavati, Saeed Bagheri Shouraki, Bahareh Jafari Jashmi, Mojdeh Jalali Heravi. 1515 [doi]
• Adaptive Markov recombinationArvid Halma, Remi Turk. 1516 [doi]
• Efficient priority optimization in complex distributed embedded systems through search space adaptationArne Hamann, Rolf Ernst. 1517 [doi]
• A comparison of GAs using penalizing infeasible solutions and repairing infeasible solutions on restrictive capacity knapsack problemJun He, Yuren Zhou. 1518 [doi]
• Is the island model fault tolerant?José Ignacio Hidalgo, Francisco Fernández de Vega, Juan Lanchares, Daniel Lombraña Gonzalez. 1519 [doi]
• Overcoming barriers by a cluster-moving genetic algorithmInwook Hwang, Yong-Hyuk Kim, Byung Ro Moon. 1520 [doi]
• A genetic algorithm for coverage problemsColin G. Johnson. 1521 [doi]
• On repair by binary interpolationSteven Orla Kimbrough, David Harlan Wood. 1522 [doi]
• Self-adaptive partially mapped crossoverOliver Kramer, Patrick Koch. 1523 [doi]
• Niching techniques: a study on the cluster geometry optimization problemTiago Leitão, Francisco Baptista Pereira, Jorge Tavares, Ernesto Costa. 1524 [doi]
• Genetic parameter tuning for reliable segmentation of colored visual tagsAudrey J. W. Mbogho, Lori L. Scarlatos. 1525 [doi]
• Who is the best connected EC researcher?Juan Julián Merelo Guervós, Carlos Cotta. 1526 [doi]
• Quality time tradeoff operator for designing efficient multi level genetic algorithmsGeorge G. Mitchell, Barry McMullin, James Decraene, Ciaran Kelly. 1527 [doi]
• Why your mates shouldn t dateJoshua L. Payne, Margaret J. Eppstein. 1528 [doi]
• Analysis of greedy heuristics and weight-coded eas for multidimensional knapsack problems and multi-unit combinatorial auctionsJella Pfeiffer, Franz Rothlauf. 1529 [doi]
• VMEA: studies on replacing strategies and diversity in dynamic environmentsAnabela Simões, Ernesto Costa. 1530 [doi]
• Evolution of non-uniform cellular automata using a genetic algorithm: diversity and computationForrest Sondahl, William Rand. 1531 [doi]
• Homogeneous genetic algorithmsAlexander Stanoyevitch. 1532 [doi]
• Adopting dynamic operators in a genetic algorithmKhadiza Tahera, Raafat N. Ibrahim, Paul B. Lochert. 1533 [doi]
• An improved genetic algorithm for task allocation in distributed embedded systemsAllan Tengg, Andreas Klausner, Bernhard Rinner. 1534 [doi]
• Multi-parent extension of edge recombinationChuan-Kang Ting. 1535 [doi]
• Efficiency updates for the restricted growth function GA for grouping problemsAllan Tucker, Stephen Swift, Jason Crampton. 1536 [doi]
• A unified model of optimisation problemsCristina C. Vieira, Carlos M. Fonseca. 1537 [doi]
• Reference frame and scale invariant real-parameter genetic and differential evolution algorithmsDaniel N. Wilke, Schalk Kok, Albert A. Groenwold. 1538 [doi]
• Geometric crossovers for real-code representationYourim Yoon, Yong-Hyuk Kim, Alberto Moraglio, Byung Ro Moon. 1539 [doi]
• Matrix interpretation of generalized embedded landscapeShude Zhou, Zengqi Sun. 1540-1542 [doi]
• Evolving controllers for simulated car racing using object oriented genetic programmingAlexandros Agapitos, Julian Togelius, Simon M. Lucas. 1543-1550 [doi]
• The roles of diversity preservation and mutation in preventing population collapse in multiobjective genetic programmingKhaled M. S. Badran, Peter Rockett. 1551-1558 [doi]
• Automatic heuristic generation with genetic programming: evolving a jack-of-all-trades or a master of oneEdmund K. Burke, Matthew R. Hyde, Graham Kendall, John Woodward. 1559-1565 [doi]
• A data parallel approach to genetic programming using programmable graphics hardwareDarren M. Chitty. 1566-1573 [doi]
• Solving the artificial ant on the Santa Fe trail problem in 20, 696 fitness evaluationsSteffen Christensen, Franz Oppacher. 1574-1579 [doi]
• A new crossover technique for Cartesian genetic programmingJanet Clegg, James Alfred Walker, Julian Francis Miller. 1580-1587 [doi]
• Generalisation of the limiting distribution of program sizes in tree-based genetic programming and analysis of its effects on bloatStephen Dignum, Riccardo Poli. 1588-1595 [doi]
• Mixing independent classifiersJan Drugowitsch, Alwyn Barry. 1596-1603 [doi]
• Evolving Lucene search queries for text classificationLaurence Hirsch, Robin Hirsch, Masoud Saeedi. 1604-1611 [doi]
• Hierarchical genetic programming based on test input subsetsDavid Jackson. 1612-1619 [doi]
• Genetic programming for cross-task knowledge sharingWojciech Jaskowski, Krzysztof Krawiec, Bartosz Wieloch. 1620-1627 [doi]
• Unwitting distributed genetic programming via asynchronous JavaScript and XMLJon Klein, Lee Spector. 1628-1635 [doi]
• On the behavioral diversity of random programsMoshe Looks. 1636-1642 [doi]
• The genetic programming collaboration network and its communitiesLeslie Luthi, Marco Tomassini, Mario Giacobini, William B. Langdon. 1643-1650 [doi]
• Context-aware mutation: a modular, context aware mutation operator for genetic programmingHammad Majeed, Conor Ryan. 1651-1658 [doi]
• On the constructiveness of context-aware crossoverHammad Majeed, Conor Ryan. 1659-1666 [doi]
• Best SubTree genetic programmingOana Muntean, Laura Diosan, Mihai Oltean. 1667-1673 [doi]
• Comparison of tree and graph encodings as function of problem complexityMichael Schmidt, Hod Lipson. 1674-1679 [doi]
• Learning noiseMichael D. Schmidt, Hod Lipson. 1680-1685 [doi]
• Graph structured program evolutionShinichi Shirakawa, Shintaro Ogino, Tomoharu Nagao. 1686-1693 [doi]
• Improving the human readability of features constructed by genetic programmingMatthew Smith, Larry Bull. 1694-1701 [doi]
• Evolving kernels for support vector machine classificationKeith Sullivan, Sean Luke. 1702-1707 [doi]
• Novel ways of improving cooperation and performance in ensemble classifiersRussell Thomason, Terence Soule. 1708-1715 [doi]
• The reliability of confidence intervals for computational effort comparisonsMatthew Walker, Howard Edwards, Chris H. Messom. 1716-1723 [doi]
• Solving real-valued optimisation problems using cartesian genetic programmingJames Alfred Walker, Julian Francis Miller. 1724-1730 [doi]
• Genetic evolution of hierarchical behavior structuresBrian G. Woolley, Gilbert L. Peterson. 1731-1738 [doi]
• An analysis of constructive crossover and selection pressure in genetic programmingHuayang Xie, Mengjie Zhang, Peter Andreae. 1739-1748 [doi]
• A GP-based hyper-heuristic framework for evolving 3-SAT heuristicsMohamed Bahy Bader-El-Den, Riccardo Poli. 1749 [doi]
• Discovering structures in gene regulatory networks using genetic programming and particle swarmsXinye Cai, Stephen Welch, Praveen Koduru, Sanjoy Das. 1750 [doi]
• StreamGP: tracking evolving GP ensembles in distributed data streams using fractal dimensionGianluigi Folino, Clara Pizzuti, Giandomenico Spezzano. 1751 [doi]
• Dynamic populations and length evolution: key factors for analyzing fault tolerance on parallel genetic programmingDaniel Lombraña Gonzalez, Francisco Fernández de Vega. 1752 [doi]
• Linear genetic programming of metaheuristicsRobert E. Keller, Riccardo Poli. 1753 [doi]
• Program search with simulated annealingMitsunori Miki, Masafumi Hashimoto, Yoshihisa Fujita, Tomoyuki Hiroyasu, Masaru Shibata. 1754 [doi]
• Seeding methods for run transferable librariesGearoid Murphy, Conor Ryan. 1755 [doi]
• Genetic programming approach for positron collisions with alkali-metal atomAmr M. Radi, Salah Yaseen El-Bakry. 1756 [doi]
• Towards an optimal restart strategy for genetic programmingMichael Solano, Istvan Jonyer. 1757 [doi]
• Association rule mining for continuous attributes using genetic network programmingKarla Taboada, Kaoru Shimada, Shingo Mabu, Kotaro Hirasawa, Jinglu Hu. 1758 [doi]
• Multi-optimization improves genetic programming generalization abilityLeonardo Vanneschi, Denis Rochat, Marco Tomassini. 1759 [doi]
• Success effort for performance comparisonsMatthew Walker, Howard Edwards, Chris H. Messom. 1760 [doi]
• Numerical-node building block analysis of genetic programming with simplificationPhillip Lee-Ming Wong, Mengjie Zhang. 1761 [doi]
• Adaptive strategies for a semantically driven tree optimizer to control code growthBart Wyns, Luc Boullart. 1762 [doi]
• Limiting code growth to improve robustness in tree-based genetic programmingBart Wyns, Luc Boullart, Pieter Jan De Smedt. 1763 [doi]
• Heuristic speciation for evolving neural network ensembleShin Ando. 1766-1773 [doi]
• UCSpv: principled voting in UCS rule populationsGavin Brown, Tim Kovacs, James A. R. Marshall. 1774-1781 [doi]
• Ensemble learning for free with evolutionary algorithms?Christian Gagné, Michèle Sebag, Marc Schoenauer, Marco Tomassini. 1782-1789 [doi]
• Knowledge reuse in genetic programming applied to visual learningWojciech Jaskowski, Krzysztof Krawiec, Bartosz Wieloch. 1790-1797 [doi]
• Do not match, inherit: fitness surrogates for genetics-based machine learning techniquesXavier Llorà, Kumara Sastry, Tian-Li Yu, David E. Goldberg. 1798-1805 [doi]
• Support vector regression for classifier predictionDaniele Loiacono, Andrea Marelli, Pier Luca Lanzi. 1806-1813 [doi]
• Empirical analysis of generalization and learning in XCS with gradient descentPier Luca Lanzi, Martin V. Butz, David E. Goldberg. 1814-1821 [doi]
• Classifier systems that compute action mappingsPier Luca Lanzi, Daniele Loiacono. 1822-1829 [doi]
• Controlling overfitting with multi-objective support vector machinesIngo Mierswa. 1830-1837 [doi]
• Modeling XCS in class imbalances: population size and parameter settingsAlbert Orriols-Puig, David E. Goldberg, Kumara Sastry, Ester Bernadó-Mansilla. 1838-1845 [doi]
• Modeling selection pressure in XCS for proportionate and tournament selectionAlbert Orriols-Puig, Kumara Sastry, Pier Luca Lanzi, David E. Goldberg, Ester Bernadó-Mansilla. 1846-1853 [doi]
• Towards clustering with XCSKreangsak Tamee, Larry Bull, Ouen Pinngern. 1854-1860 [doi]
• XCSF with computed continuous actionTrung Hau Tran, Cédric Sanza, Yves Duthen, Thuc Dinh Nguyen. 1861-1869 [doi]
• Discovering rules in the poker hand datasetRobert Cattral, Franz Oppacher. 1870 [doi]
• Introducing fault tolerance to XCSHong-Wei Chen, Ying-Ping Chen. 1871 [doi]
• Feature selection and classification in noisy epistatic problems using a hybrid evolutionary approachDrew DeHaas, Jesse Craig, Colin Rickert, Margaret J. Eppstein, Paul Haake, Kirsten Stor. 1872 [doi]
• Genetically designed multiple-kernels for improving the SVM performanceLaura Diosan, Mihai Oltean, Alexandrina Rogozan, Jean-Pierre Pécuchet. 1873 [doi]
• Fused, multi-spectral automatic target recognition with XCSAvinash Gandhe, Ssu-Hsin Yu, Raman K. Mehra, Robert E. Smith. 1874 [doi]
• MDL-based fitness for feature constructionLeila Shila Shafti, Eduardo Pérez. 1875 [doi]
• XCS for adaptive user-interfacesAnil Shankar, Sushil J. Louis, Sergiu Dascalu, Ramona Houmanfar, Linda J. Hayes. 1876 [doi]
• MILCS: a mutual information learning classifier systemRobert Elliott Smith, Max Kun Jiang. 1877 [doi]
• Genetically generated double-level fuzzy controller with a fuzzy adjustment strategySofiane Achiche, Wei Wang, Zhun Fan, Ali Gürcan Özkil, Torben Sørensen, Jiachuan Wang, Erik D. Goodman. 1880-1887 [doi]
• Evolving distributed agents for managing air trafficAdrian K. Agogino, Kagan Tumer. 1888-1895 [doi]
• A genetic algorithm for dynamic modelling and prediction of activity in document streamsLourdes Araujo, Juan Julián Merelo Guervós. 1896-1903 [doi]
• Multiobjective network design for realistic traffic modelsNilanjan Banerjee, Rajeev Kumar. 1904-1911 [doi]
• Coupling EA and high-level metrics for the automatic generation of test blocks for peripheral coresLeticia Maria Veiras Bolzani, Ernesto Sánchez, Massimiliano Schillaci, Giovanni Squillero. 1912-1919 [doi]
• Real-coded ECGA for economic dispatchChao-Hong Chen, Ying-Ping Chen. 1920-1927 [doi]
• Simultaneous optimization of production planning and inspection planning for flexible manufacturing systemsJian-Hung Chen. 1928-1935 [doi]
• Automated red teaming: a proposed framework for military applicationChwee Seng Choo, Ching Lian Chua, Su-Han Victor Tay. 1936-1942 [doi]
• Graph-based sequence clustering through multiobjective evolutionary algorithms for web recommender systemsGül Nildem Demir, Sima Uyar, Sule Gündüz Ögüdücü. 1943-1950 [doi]
• A phenotypic analysis of GP-evolved team behavioursDarren Doherty, Colm O Riordan. 1951-1958 [doi]
• Electronic synthesis using genetic algorithms for automatic music transcriptionGustavo Reis, Francisco Fernandez Vega. 1959-1966 [doi]
• Evolutionary algorithms for reasoning in fuzzy description logics with fuzzy quantifiersMauro Dragoni, Andrea Tettamanzi. 1967-1974 [doi]
• Configuring an evolutionary tool for the inventory and transportation problemAnna Esparcia-Alcázar, Lidia Lluch-Revert, Manuel Cardós, Ken Sharman, Juan Julián Merelo Guervós. 1975-1982 [doi]
• Option pricing model calibration using a real-valued quantum-inspired evolutionary algorithmKai Fan, Anthony Brabazon, Conall O Sullivan, Michael O Neill. 1983-1990 [doi]
• Self-adaptive ant colony optimisation applied to function allocation in vehicle networksManuel Förster, Bettina Bickel, Bernd Hardung, Gabriella Kókai. 1991-1998 [doi]
• Discrimination of munitions and explosives of concern at F.E. warren afb using linear genetic programmingFrank D. Francone, Larry M. Deschaine, Jeffrey J. Warren. 1999-2006 [doi]
• Genetic optimization for yacht designPaolo Geremia, Mauro Poian, Silvia Poles. 2007-2012 [doi]
• Defining implicit objective functions for design problemsSean Hanna. 2013-2020 [doi]
• Revisiting the personal satellite assistant: neuroevolution with a modified enforced sub-populations algorithmBoye Annfelt Hoverstad. 2021-2028 [doi]
• Characteristic determination for solid state devices with evolutionary computation: a case studyPing-Chu Hung, Ying-Ping Chen, Hsiao Wen Zan. 2029-2036 [doi]
• An evolutionary approach to collective communication schedulingJirí Jaros, Milos Ohlídal, Vaclav Dvorak. 2037-2044 [doi]
• A hybrid GA for a supply chain production planning problemMasoud Jenabi, S. Ali Torabi, S. Afshin Mansouri. 2045-2052 [doi]
• High quality offset printing: an evolutionary approachRalf Joost, Ralf Salomon. 2053-2058 [doi]
• Variable selection for wind power prediction using particle swarm optimizationRené Jursa. 2059-2065 [doi]
• Mining breast cancer data with XCSFaten Kharbat, Larry Bull, Mohammed Odeh. 2066-2073 [doi]
• Automated synthesis of a fixed-length loaded symmetric dipole antenna whose gain exceeds that of a commercial antenna and matches the theoretical maximumJohn R. Koza, Sameer H. Al-Sakran, Lee W. Jones, Greg Manassero. 2074-2081 [doi]
• Using code metric histograms and genetic algorithms to perform author identification for software forensicsRobert Charles Lange, Spiros Mancoridis. 2082-2089 [doi]
• An evolutionary keystroke authentication based on ellipsoidal hypothesis spaceJae-Wook Lee, Sung-Soon Choi, Byung Ro Moon. 2090-2097 [doi]
• Towards better than human capability in diagnosing prostate cancer using infrared spectroscopic imagingXavier Llorà, Rohith Reddy, Brian Matesic, Rohit Bhargava. 2098-2105 [doi]
• Evolving explicit opponent models in game playingAlan J. Lockett, Charles L. Chen, Risto Miikkulainen. 2106-2113 [doi]
• Using genetic algorithms for naval subsystem damage assessment and design improvementsChristopher McCubbin, David Scheidt, Oliver Bandte, Steven Marshall, Iavor Trifonov. 2114-2121 [doi]
• ICSPEA: evolutionary five-axis milling path optimisationJörn Mehnen, Rajkumar Roy, Petra Kersting, Tobias Wagner. 2122-2128 [doi]
• A multi-objective approach for the prediction of loan defaultsOluwarotimi Odeh, Praveen Koduru, Sanjoy Das, Allen M. Featherstone, Stephen Welch. 2129-2136 [doi]
• Targeted filter evolution for improved image reconstruction resolutionMichael R. Peterson, Gary B. Lamont, Frank Moore, Patrick Marshall. 2137-2144 [doi]
• A NSGA-II, web-enabled, parallel optimization framework for NLP and MINLPDavid J. Powell, Joel K. Hollingsworth. 2145-2150 [doi]
• Interactive evolution of XUL user interfacesJuan C. Quiroz, Sushil J. Louis, Sergiu M. Dascalu. 2151-2158 [doi]
• Inducing a generative expressive performance model using a sequential-covering genetic algorithmRafael Ramirez, Amaury Hazan. 2159-2166 [doi]
• A destructive evolutionary process: a pilot implementationJoe Sullivan, Conor Ryan. 2167-2173 [doi]
• An improved restricted growth function genetic algorithm for the consensus clustering of retinal nerve fibre dataStephen Swift, Allan Tucker, Jason Crampton, David Garway-Heath. 2174-2181 [doi]
• Comparing two models to generate hyper-heuristics for the 2d-regular bin-packing problemHugo Terashima-Marín, Cláudia J. Farías Zárate, Peter Ross, Manuel Valenzuela-Rendón. 2182-2189 [doi]
• An online implementable differential evolution tuned optimal guidance lawRaghunathan Thangavelu, S. Pradeep. 2190-2194 [doi]
• On the design of optimisers for surface reconstructionTobias Wagner, Thomas Michelitsch, Alexei Sacharow. 2195-2202 [doi]
• A genetic algorithm for resident physician scheduling problemChi-Way Wang, Lei-Ming Sun, Ming-Hui Jin, Chung-Jung Fu, Li Liu, Chen-hsiung Chan, Cheng-Yan Kao. 2203-2210 [doi]
• A multi-objective imaging scheduling approach for earth observing satellitesJun Wang, Ning Jing, Jun Li, Zhong Hui Chen. 2211-2218 [doi]
• Peptide detectability following ESI mass spectrometry: prediction using genetic programmingDavid C. Wedge, Simon J. Gaskell, Simon J. Hubbard, Douglas B. Kell, King Wai Lau, Claire Eyers. 2219-2225 [doi]
• Diverse committees vote for dependable profitsWei Yan, Christopher D. Clack. 2226-2233 [doi]
• Evolving robust GP solutions for hedge fund stock selection in emerging marketsWei Yan, Christopher D. Clack. 2234-2241 [doi]
• Multi-objective univariate marginal distribution optimisation of mixed analogue-digital signal circuitsLyudmila Zinchenko, Matthias Radecker, Fabio Bisogno. 2242-2251 [doi]
• Automated trading on financial instruments with evolved neural networksAntonia Azzini, Andrea Tettamanzi. 2252 [doi]
• Automatic generation of benchmarks for plagiarism detection tools using grammatical evolutionManuel Cebrián, Manuel Alfonseca, Alfonso Ortega. 2253 [doi]
• Towards models of user preferences in interactive musical evolutionDan Costelloe, Conor Ryan. 2254 [doi]
• Using genetic programming for information retrieval: local and global query expansionRonan Cummins, Colm O Riordan. 2255 [doi]
• Optimal design of ad hoc injection networks by using genetic algorithmsGrégoire Danoy, Enrique Alba, Pascal Bouvry, Matthias R. Brust. 2256 [doi]
• Using an evolutionary agent-based simulation to explore hedging pressure in futures marketsJonathan Duke, Christopher D. Clack. 2257 [doi]
• Evolutionary computation-based kernel optimal component analysis for pattern recognitionJason C. Isaacs, Simon Y. Foo, Anke Meyer-Bäse. 2258 [doi]
• On quality performance of heuristic and evolutionary algorithms for biobjective minimum spanning treesRajeev Kumar, Pramod Kumar Singh. 2259 [doi]
• An effective genetic algorithm for improving wireless sensor network lifetimeChih-Chung Lai, Chuan-Kang Ting, Ren-Song Ko. 2260 [doi]
• Automatic analog IC layout generation based on a evolutionary computation approachNuno C. Lourenço, Nuno C. Horta. 2261 [doi]
• Volatility forecasting using time series data mining and evolutionary computation techniquesIrwin Ma, Tony Wong, Thiagas Sankar. 2262 [doi]
• Genetic network programming with actor-critic and its application to stock trading modelShingo Mabu, Yan Chen, Kotaro Hirasawa, Jinglu Hu. 2263 [doi]
• Evolutionary synthesis of photographic artwork using human fitness function derived from web-based social networksDavid Oranchak. 2264 [doi]
• Controller design based on genetic programmingIvan Sekaj, Juraj Perkacz, Tomas Palenik. 2265 [doi]
• On the scalability of evolution strategies in the optimization of dynamic molecular alignmentOfer M. Shir, Thomas Bäck, Marc J. J. Vrakking. 2266 [doi]
• Genetic algorithms for water quality management in an urban watershedMohammad Tufail, Lindell E. Ormsbee. 2267 [doi]
• An experimental evaluation of genetic process miningChris J. Turner, Ashutosh Tiwari. 2268 [doi]
• Carbon-friendly travel plan construction using an evolutionary algorithmNeil Urquhart. 2269 [doi]