Table of Contents

• Evaluating evolution and monte carlo for controlling air traffic flowAdrian Agogino. 1957-1962 [doi]
• A new multi-objective algorithm, pareto archived DDSMasoud Asadzadeh, Bryan A. Tolson. 1963-1966 [doi]
• Optimization of morphological data in numerical taxonomy analysis using genetic algorithms feature selection methodYasin Bakis, Osman Ugur Sezerman, M. Tekin Babaç, Cem Meydan. 1967-1970 [doi]
• A comparison of selection, recombination, and mutation parameter importance over a set of fifteen optimization tasksEdwin Roger Banks, Paul Agarwal, Marshall McBride, Claudette Owens. 1971-1976 [doi]
• Lessons learned in application of evolutionary computation to a set of optimization tasksEdwin Roger Banks, Paul Agarwal, Marshall McBride, Claudette Owens. 1977-1982 [doi]
• Toward a universal operator encoding for genetic programmingEdwin Roger Banks, Paul Agarwal, Marshall McBride, Claudette Owens. 1983-1986 [doi]
• Exploring an evolutionary medical analytic walletAaron K. Baughman, Mweene Monze, Christian Eggenberger, Peter Malkin, Neil Katz, Chris Dawson, Barry Graham. 1987-1992 [doi]
• PEPPA: a project for evolutionary predator prey algorithmsHendrik Blom, Christiane Küch, Katja Losemann, Chris Schwiegelshohn. 1993-1998 [doi]
• Multiobjective optimization of technical market indicatorsDiego J. Bodas-Sagi, Pablo Fernández, José Ignacio Hidalgo, Francisco J. Soltero, José Luis Risco-Martín. 1999-2004 [doi]
• The degree of dynamism for workforce scheduling problem with stochastic task durationYossi Borenstein, Abdullah Alsheddy, Edward P. K. Tsang, Nazaraf Shah. 2005-2010 [doi]
• Learning in the time-dependent minority gameDavid Catteeuw, Bernard Manderick. 2011-2016 [doi]
• A concurrent evolutionary approach for rich combinatorial optimizationTeodor Gabriel Crainic, Gloria Cerasela Crisan, Michel Gendreau, Nadia Lahrichi, Walter Rei. 2017-2022 [doi]
• Efficient trade execution using a genetic algorithm in an order book based artificial stock marketWei Cui, Anthony Brabazon, Michael O Neill. 2023-2028 [doi]
• Improving SMT performance: an application of genetic algorithms to configure resizable cachesJosefa Díaz, José Ignacio Hidalgo, Francisco Fernández, Oscar Garnica, Sonia López. 2029-2034 [doi]
• Grisland: a parallel genetic algorithm for finding near optimal solutions to the traveling salesman problemJonatan Gómez, Roberto Poveda, Elizabeth Leon. 2035-2040 [doi]
• Using GAs to balance technical indicators on stock picking for financial portfolio compositionAntónio Gorgulho, Rui Neves, Nuno Horta. 2041-2046 [doi]
• Opposition based initialization in particle swarm optimization (O-PSO)Hajira Jabeen, Zunera Jalil, Abdul Rauf Baig. 2047-2052 [doi]
• A data-based coding of candidate strings in the closest string problemBryant A. Julstrom. 2053-2058 [doi]
• Multi-objective evolutionary optimization of 3D differentiated sensor network deploymentChih-Wei Kang, Jian-Hung Chen. 2059-2064 [doi]
• Evolving biochemical reaction networks with stochastic attributesThomas R. Kiehl. 2065-2070 [doi]
• Towards a "theory of mind" in simulated robotsKyung-Joong Kim, Hod Lipson. 2071-2076 [doi]
• Biasing evolving generations in learning classifier systems using information theoretic measuresKarthik Kuber, Chilukuri K. Mohan. 2077-2080 [doi]
• Evo_indent interactive evolution of GNU indent optionsWilliam B. Langdon. 2081-2084 [doi]
• Cancer classification using microarray and layered architecture genetic programmingJung-Yi Lin. 2085-2090 [doi]
• NEAT in increasingly non-linear control situationsMatthias J. Linhardt, Martin V. Butz. 2091-2096 [doi]
• Prisoner s dilemma on graphs with heterogeneous agentsLingzhi Luo, Nilanjan Chakraborty, Katia P. Sycara. 2097-2102 [doi]
• Improving classification accuracy using evolutionary fuzzy transformationHossein Moeinzadeh, Babak Nasersharif, Abdolazim Rezaee, Hossein Pazhoumand-dar. 2103-2108 [doi]
• Robust speech recognition using evolutionary class-dependent LDAHossein Moeinzadeh, Mehdi Mohammadi, Ahmad Akbari, Babak Nasersharif. 2109-2114 [doi]
• HS-ROBDD: an efficient variable order binary decision diagramMehdi Mohammadi, Hossein Pazhoumand-dar, Mohsen Soryani, Hossein Moeinzadeh. 2115-2118 [doi]
• An evolved neural controller for bipedal walking with dynamic balanceMichael E. Palmer, Daniel B. Miller. 2119-2124 [doi]
• Motion detection in complex environments by genetic programmingBrian Pinto, Andy Song. 2125-2130 [doi]
• Using simulated annealing for producing software architecturesOuti Räihä, Erkki Mäkinen, Timo Poranen. 2131-2136 [doi]
• An investigation into the structure of genomes within an evolution that uses embryogenesisAnthony M. Roy, Erik K. Antonsson, Andrew A. Shapiro. 2137-2142 [doi]
• Novel bio-inspired self-repair algorithm for evolvable fault tolerant hardware systemsMohammad Samie, Gabriel Dragffy, Anthony G. Pipe. 2143-2148 [doi]
• Solving iterated functions using genetic programmingMichael D. Schmidt, Hod Lipson. 2149-2154 [doi]
• Tree-structure-aware GP operators for automatic gait generation of quadruped robotKisung Seo, Soohwan Hyun, Erik D. Goodman. 2155-2160 [doi]
• Cryptanalysis of four-rounded DES using binary particleswarm optimizationWaseem Shahzad, Abdul Basit Siddiqui, Farrukh Aslam Khan. 2161-2166 [doi]
• An evolutionary approach to constructive induction for link discoveryTim Weninger, William H. Hsu, Jing Xia, Waleed Aljandal. 2167-2172 [doi]
• An entropy based heuristic model for predicting functional sub-type divisions of protein familiesDeniz Yörükoglu, Yasin Bakis, Osman Ugur Sezerman. 2173-2178 [doi]
• Self-reflection in evolutionary robotics: resilient adaptation with a minimum of physical explorationJuan Cristóbal Zagal, Hod Lipson. 2179-2188 [doi]
• Evolving human-competitive reusable 2D strip packing heuristicsMatthew R. Hyde, Edmund K. Burke, Graham Kendall. 2189-2192 [doi]
• A multi-level search framework for asynchronous cooperation of multiple hyper-heuristicsDjamila Ouelhadj, Sanja Petrovic, Ender Ozcan. 2193-2196 [doi]
• Using performance fronts for parameter setting of stochastic metaheuristicsJohann Dréo. 2197-2200 [doi]
• A greedy hyper-heuristic in dynamic environmentsEnder Özcan, A. Sima Etaner-Uyar, Edmund K. Burke. 2201-2204 [doi]
• Towards the decathlon challenge of search heuristicsEdmund K. Burke, Timothy Curtois, Graham Kendall, Matthew R. Hyde, Gabriela Ochoa, Jose A. Vazquez-Rodriguez. 2205-2208 [doi]
• Learning and using hyper-heuristics for variable and value ordering in constraint satisfaction problemsSean A. Bittle, Mark S. Fox. 2209-2212 [doi]
• Extreme: dynamic multi-armed bandits for adaptive operator selectionÁlvaro Fialho, Luís Da Costa, Marc Schoenauer, Michèle Sebag. 2213-2216 [doi]
• On benchmark properties for adaptive operator selectionDirk Thierens. 2217-2218 [doi]
• Mutation and crossover with abstract expression grammarsMichael F. Korns. 2219-2224 [doi]
• Multiobjective genetic programming approach for a smooth modeling of the release kinetics of a pheromone dispenserEva Alfaro-Cid, Anna Esparcia-Alcázar, Pilar Moya, Juan Julián Merelo Guervós, Beatriu Femenia-Ferrer, Ken Sharman, Jaime Primo. 2225-2230 [doi]
• Noiseless functions black-box optimization: evaluation of a hybrid particle swarm with differential operatorsJosé García-Nieto, Enrique Alba, Javier Apolloni. 2231-2238 [doi]
• BBOB: Nelder-Mead with resize and halfrunsBenjamin Doerr, Mahmoud Fouz, Martin Schmidt, Magnus Wahlström. 2239-2246 [doi]
• AMaLGaM IDEAs in noiseless black-box optimization benchmarkingPeter A. N. Bosman, Jörn Grahl, Dirk Thierens. 2247-2254 [doi]
• A memetic algorithm using local search chaining forblack-box optimization benchmarking 2009 for noise free functionsDaniel Molina, Manuel Lozano, Francisco Herrera. 2255-2262 [doi]
• Black-box optimization benchmarking for noiseless function testbed using an EDA and PSO hybridMohammed El-Abd, Mohamed S. Kamel. 2263-2268 [doi]
• Black-box optimization benchmarking for noiseless function testbed using particle swarm optimizationMohammed El-Abd, Mohamed S. Kamel. 2269-2274 [doi]
• Black-box optimization benchmarking for noiseless function testbed using PSO_boundsMohammed El-Abd, Mohamed S. Kamel. 2275-2280 [doi]
• Black-box optimization benchmarking: results for the BayEDAcG algorithm on the noiseless function testbedMarcus Gallagher. 2281-2286 [doi]
• A continuous variable neighbourhood search based on specialised EAs: application to the noiseless BBO-benchmark 2009Carlos García-Martínez, Manuel Lozano. 2287-2294 [doi]
• A stigmergy-based algorithm for black-box optimization: noiseless function testbedPeter Korosec, Jurij Silc. 2295-2302 [doi]
• Black-box optimization benchmarking of prototype optimization with evolved improvement steps for noiseless function testbedJirí Kubalík. 2303-2308 [doi]
• BBOB-benchmarking a simple estimation of distribution algorithm with cauchy distributionPetr Posik. 2309-2314 [doi]
• BBOB-benchmarking the DIRECT global optimization algorithmPetr Posik. 2315-2320 [doi]
• BBOB-benchmarking the generalized generation gap model with parent centric crossoverPetr Posik. 2321-2328 [doi]
• BBOB-benchmarking two variants of the line-search algorithmPetr Posik. 2329-2336 [doi]
• BBOB-benchmarking the Rosenbrock s local search algorithmPetr Posik. 2337-2342 [doi]
• Particle swarm hybridized with differential evolution: black box optimization benchmarking for noisy functionsJosé García-Nieto, Enrique Alba, Javier Apolloni. 2343-2350 [doi]
• AMaLGaM IDEAs in noisy black-box optimization benchmarkingPeter A. N. Bosman, Jörn Grahl, Dirk Thierens. 2351-2358 [doi]
• A memetic algorithm using local search chaining for black-box optimization benchmarking 2009 for noisy functionsDaniel Molina, Manuel Lozano, Francisco Herrera. 2359-2366 [doi]
• A continuous variable neighbourhood search based on specialised eas: application to the noisy BBO-benchmark 2009 testbedCarlos García-Martínez, Manuel Lozano. 2367-2374 [doi]
• A stigmergy-based algorithm for black-box optimization: noisy function testbedPeter Korosec, Jurij Silc. 2375-2382 [doi]
• Benchmarking a BI-population CMA-ES on the BBOB-2009 function testbedNikolaus Hansen. 2389-2396 [doi]
• Benchmarking a BI-population CMA-ES on the BBOB-2009 noisy testbedNikolaus Hansen. 2397-2402 [doi]
• Benchmarking the nelder-mead downhill simplex algorithm with many local restartsNikolaus Hansen. 2403-2408 [doi]
• Benchmarking the BFGS algorithm on the BBOB-2009 function testbedRaymond Ros. 2409-2414 [doi]
• Benchmarking the BFGS algorithm on the BBOB-2009 noisy testbedRaymond Ros. 2415-2420 [doi]
• Benchmarking the NEWUOA on the BBOB-2009 function testbedRaymond Ros. 2421-2428 [doi]
• Benchmarking the NEWUOA on the BBOB-2009 noisy testbedRaymond Ros. 2429-2434 [doi]
• Benchmarking sep-CMA-ES on the BBOB-2009 function testbedRaymond Ros. 2435-2440 [doi]
• Benchmarking sep-CMA-ES on the BBOB-2009 noisy testbedRaymond Ros. 2441-2446 [doi]
• Benchmarking the (1+1) evolution strategy with one-fifth success rule on the BBOB-2009 function testbedAnne Auger. 2447-2452 [doi]
• Benchmarking the (1+1)-ES with one-fifth success rule on the BBOB-2009 noisy testbedAnne Auger. 2453-2458 [doi]
• Benchmarking the (1+1)-CMA-ES on the BBOB-2009 function testbedAnne Auger, Nikolaus Hansen. 2459-2466 [doi]
• Benchmarking the (1+1)-CMA-ES on the BBOB-2009 noisy testbedAnne Auger, Nikolaus Hansen. 2467-2472 [doi]
• Application of a simple binary genetic algorithm to a noiseless testbed benchmarkMiguel Nicolau. 2473-2478 [doi]
• Benchmarking the pure random search on the BBOB-2009 testbedAnne Auger, Raymond Ros. 2479-2484 [doi]
• Benchmarking the pure random search on the BBOB-2009 noisy testbedAnne Auger, Raymond Ros. 2485-2490 [doi]
• Evaluating the cell broadband engine as a platform to run estimation of distribution algorithmsCarlos Perez-Miguel, José Miguel-Alonso, Alexander Mendiburu. 2491-2498 [doi]
• Evolving soft robotic locomotion in PhysXJohn Rieffel, Frank Saunders, Shilpa Nadimpalli, Harvey Zhou, Soha Hassoun, Jason Rife, Barry Trimmer. 2499-2504 [doi]
• Parallel latent semantic analysis using a graphics processing unitJoseph M. Cavanagh, Thomas E. Potok, Xiaohui Cui. 2505-2510 [doi]
• A fast high quality pseudo random number generator for nVidia CUDAWilliam B. Langdon. 2511-2514 [doi]
• Parallel multi-objective evolutionary algorithms on graphics processing unitsMan Leung Wong. 2515-2522 [doi]
• Solving quadratic assignment problems by genetic algorithms with GPU computation: a case studyShigeyoshi Tsutsui, Noriyuki Fujimoto. 2523-2530 [doi]
• Deployment of CPU and GPU-based genetic programming on heterogeneous devicesGarnett Carl Wilson, Wolfgang Banzhaf. 2531-2538 [doi]
• Classifying SSH encrypted traffic with minimum packet header features using genetic programmingRiyad Alshammari, Peter Lichodzijewski, Malcolm I. Heywood, A. Nur Zincir-Heywood. 2539-2546 [doi]
• Improved multiresolution analysis transforms for satellite image compression and reconstruction using evolution strategiesBrendan J. Babb, Frank W. Moore, Michael R. Peterson. 2547-2552 [doi]
• String- and permutation-coded genetic algorithms for the static weapon-target assignment problemBryant A. Julstrom. 2553-2558 [doi]
• Military network security using self organized multi-agent entangled hierarchiesGary B. Lamont, Eric M. Holloway. 2559-2566 [doi]
• Self-organizing economic activity with costly informationJames A. Wilson, Liying Yan. 2567-2574 [doi]
• Asynchronous collaborative search using adaptive co-evolving subpopulationsCamelia Chira, Anca Gog, D. Dumitrescu. 2575-2582 [doi]
• Comparison of sorting algorithms for multi-fitness measurement of cooperative coevolutionMin Shi. 2583-2588 [doi]
• Self organized multi-agent entangled hierarchies for network securityEric M. Holloway, Gary B. Lamont. 2589-2596 [doi]
• The game of funding: modelling peer review for research grantsPeter J. Bentley. 2597-2602 [doi]
• Coevolution of pattern generators and recognizersStewart W. Wilson. 2605-2608 [doi]
• On the appropriateness of evolutionary rule learning algorithms for malware detectionM. Zubair Shafiq, S. Momina Tabish, Muddassar Farooq. 2609-2616 [doi]
• Performance evaluation of evolutionary algorithms in classification of biomedical datasetsAjay Kumar Tanwani, Muddassar Farooq. 2617-2624 [doi]
• An XCS approach to forecasting financial time seriesRichard Preen. 2625-2632 [doi]
• Evolved cooperation and emergent communication structures in learning classifier based organic computing systemsAlexander Scheidler, Martin Middendorf. 2633-2640 [doi]
• Reinforcement learning for games: failures and successesWolfgang Konen, Thomas Bartz-Beielstein. 2641-2648 [doi]
• On the limitations of adaptive resampling in using the student s t-test evolution strategiesJohannes W. Kruisselbrink, Michael T. M. Emmerich, Thomas Bäck. 2649-2656 [doi]
• Lessons learned in evolutionary computation: 11 steps to successJörn Mehnen. 2657-2660 [doi]
• A series of failed and partially successful fitness functions for evolving spiking neural networksJ. David Schaffer, Heike Sichtig, Craig B. Laramee. 2661-2664 [doi]
• A genetic algorithm for learning significant phrase patterns in radiology reportsRobert M. Patton, Thomas E. Potok, Barbara G. Beckerman, Jim N. Treadwell. 2665-2670 [doi]
• A hybrid GA-based fuzzy classifying approach to urinary analysis modelingPing Wu, Erik D. Goodman, Tang Jiang, Min Pei. 2671-2678 [doi]
• A PSO/ACO approach to knowledge discovery in a pharmacovigilance contextMargarita Sordo, Gabriela Ochoa, Shawn N. Murphy. 2679-2684 [doi]
• Application of quantum genetic algorithm on breast tumor imaging with microwaveMeng Yao, Qi-feng Pan, Zhi-fu Tao. 2685-2688 [doi]
• Preserving population diversity for the multi-objective vehicle routing problem with time windowsAbel Garcia-Najera. 2689-2692 [doi]
• Some techniques to deal with many-objective problemsAntonio López Jaimes, Carlos A. Coello Coello. 2693-2696 [doi]
• Improving NSGA-II with an adaptive mutation operatorArthur Gonçalves Carvalho, Aluizio F. R. Araújo. 2697-2700 [doi]
• Solving the eltrut problem with hybrid evolutionary algorithmsBenjamin James Bush. 2701-2704 [doi]
• An exploration of learning and grammars in grammatical evolutionErik Hemberg. 2705-2708 [doi]
• Gene network inference using a swarm intelligence frameworkKyriakos Kentzoglanakis, Matthew Poole. 2709-2712 [doi]
• Multi-colony ant colony optimization for the node placement problemLeonor Albuquerque Melo. 2713-2716 [doi]
• Learnable evolution model performance impaired by binary tournament survival selectionMark Coletti. 2717-2720 [doi]
• Combined effect of the direction of information transmission and the spatiality over sustaining cooperationIvette C. Martínez, Klaus Jaffe. 2721-2724 [doi]
• Rapid prototyping using evolutionary approaches: part 1Nikhil Padhye, Subodh Kalia. 2725-2728 [doi]
• Evolving universal hash functions using genetic algorithmsMustafa Safdari. 2729-2732 [doi]
• Minimizing total completion time in two-machine flow shops with exact delay using genetic algorithm & ant colony algorithmJosh Glascock, Brian Hunter. 2733-2736 [doi]
• Rapid prototyping using evolutionary approaches: part 2Nikhil Padhye, Subodh Kalia. 2737-2740 [doi]
• Relative fitness scaling for improving efficiency of proportionate selection in genetic algorithmsSurabhi Gupta. 2741-2744 [doi]
• Introduction to genetic algorithmsErik D. Goodman. 2753-2774 [doi]
• Introduction to genetic programmingRiccardo Poli, Nicholas Freitag McPhee. 2775-2810 [doi]
• A unified approach to Evolutionary ComputationKenneth De Jong. 2811-2824 [doi]
• Ant colony optimizationChristian Blum. 2825-2852 [doi]
• Learning classifier systemsPier Luca Lanzi. 2853-2878 [doi]
• Grammatical evolutionConor Ryan. 2907-2948 [doi]
• Statistical analysis for evolutionary computation: introductionMark Wineberg, Steffen Christensen. 2949-2976 [doi]
• Evolving neural networksRisto Miikkulainen, Kenneth O. Stanley. 2977-3014 [doi]
• Genetic programming theory I & IIRiccardo Poli, William B. Langdon. 3015-3056 [doi]
• No free lunch: 1995-2008Darrell Whitley. 3057-3088 [doi]
• Constraint-handling techniques used with evolutionary algorithmsCarlos Artemio Coello Coello. 3089-3110 [doi]
• Statistical analysis for evolutionary computation: advanced techniquesMark Wineberg, Steffen Christensen. 3111-3130 [doi]
• Representations for evolutionary algorithmsFranz Rothlauf. 3131-3156 [doi]
• Computational complexity and evolutionary computationThomas Jansen, Frank Neumann. 3157-3184 [doi]
• the future of experimental researchThomas Bartz-Beielstein, Mike Preuss. 3185-3226 [doi]
• Elementary landscape analysisL. Darrell Whitley, Andrew M. Sutton. 3227-3236 [doi]
• Accelerating evolutionary computation with graphics processing unitsWolfgang Banzhaf, Simon Harding. 3237-3286 [doi]
• Evolving quantum computer algorithmsLee Spector. 3287-3316 [doi]
• An information perspective on evolutionary computationYossi Borenstein. 3317-3334 [doi]
• Generative and developmental systemsKenneth O. Stanley. 3335-3354 [doi]
• Evolutionary computer visionMengjie Zhang, Stefano Cagnoni, Gustavo Olague. 3355-3380 [doi]
• Large scale data mining using genetics-based machine learningJaume Bacardit, Xavier Llorà. 3381-3412 [doi]
• Evolutionary multiobjective combinatorial optimization (EMCO): emcoRajeev Kumar. 3413-3436 [doi]
• Synthetic biology INawwaf N. Kharma, Luc Varin. 3437-3438 [doi]
• Synthetic biology: modelling and optimisationNatalio Krasnogor. 3439-3488 [doi]
• Cartesian genetic programmingJulian Francis Miller, Simon L. Harding. 3489-3512 [doi]
• Bio-inspired telecommunicationsMuddassar Farooq. 3513-3550 [doi]
• Theory of randomised search heuristics in combinatorial optimisation: an algorithmic point of viewCarsten Witt. 3551-3592 [doi]
• Fitness landscapes and graphs: multimodularity, ruggedness and neutralitySébastien Vérel. 3593-3656 [doi]
• Fitness landscapes and problem hardness in genetic programmingLeonardo Vanneschi. 3657-3684 [doi]