Late-breaking papers

• 
  Adrian K. Agogino: Evaluating evolution and monte carlo for controlling air traffic flow. 1957-1962
• 
  Masoud Asadzadeh, Bryan A. Tolson: A new multi-objective algorithm, pareto archived DDS. 1963-1966
• 
  Yasin Bakis, Osman Ugur Sezerman, M. Tekin Babaç, Cem Meydan: Optimization of morphological data in numerical taxonomy analysis using genetic algorithms feature selection method. 1967-1970
• 
  Edwin Roger Banks, Paul Agarwal, Marshall McBride, Claudette Owens: A comparison of selection, recombination, and mutation parameter importance over a set of fifteen optimization tasks. 1971-1976
• 
  Edwin Roger Banks, Paul Agarwal, Marshall McBride, Claudette Owens: Lessons learned in application of evolutionary computation to a set of optimization tasks. 1977-1982
• 
  Edwin Roger Banks, Paul Agarwal, Marshall McBride, Claudette Owens: Toward a universal operator encoding for genetic programming. 1983-1986
• 
  Aaron K. Baughman, Mweene Monze, Christian Eggenberger, Peter Malkin, Neil Katz, Chris Dawson, Barry Graham: Exploring an evolutionary medical analytic wallet. 1987-1992
• 
  Hendrik Blom, Christiane Küch, Katja Losemann, Chris Schwiegelshohn: PEPPA: a project for evolutionary predator prey algorithms. 1993-1998
• 
  Diego J. Bodas-Sagi, Pablo Fernández, José Ignacio Hidalgo, Francisco J. Soltero, José Luis Risco-Martín: Multiobjective optimization of technical market indicators. 1999-2004
• 
  Yossi Borenstein, Abdullah Alsheddy, Edward P. K. Tsang, Nazaraf Shah: The degree of dynamism for workforce scheduling problem with stochastic task duration. 2005-2010
• 
  David Catteeuw, Bernard Manderick: Learning in the time-dependent minority game. 2011-2016
• 
  Teodor Gabriel Crainic, Gloria Cerasela Crisan, Michel Gendreau, Nadia Lahrichi, Walter Rei: A concurrent evolutionary approach for rich combinatorial optimization. 2017-2022
• 
  Wei Cui, Anthony Brabazon, Michael O'Neill: Efficient trade execution using a genetic algorithm in an order book based artificial stock market. 2023-2028
• 
  Josefa Díaz, José Ignacio Hidalgo, Francisco Fernández, Oscar Garnica, Sonia López: Improving SMT performance: an application of genetic algorithms to configure resizable caches. 2029-2034
• 
  Jonatan Gómez, Roberto Poveda, Elizabeth Leon: Grisland: a parallel genetic algorithm for finding near optimal solutions to the traveling salesman problem. 2035-2040
• 
  António Gorgulho, Rui Ferreira Neves, Nuno Horta: Using GAs to balance technical indicators on stock picking for financial portfolio composition. 2041-2046
• 
  Hajira Jabeen, Zunera Jalil, Abdul Rauf Baig: Opposition based initialization in particle swarm optimization (O-PSO). 2047-2052
• 
  Bryant A. Julstrom: A data-based coding of candidate strings in the closest string problem. 2053-2058
• 
  Chih-Wei Kang, Jian-Hung Chen: Multi-objective evolutionary optimization of 3D differentiated sensor network deployment. 2059-2064
• 
  Thomas R. Kiehl: Evolving biochemical reaction networks with stochastic attributes. 2065-2070
• 
  Kyung-Joong Kim, Hod Lipson: Towards a "theory of mind" in simulated robots. 2071-2076
• 
  Karthik Kuber, Chilukuri K. Mohan: Biasing evolving generations in learning classifier systems using information theoretic measures. 2077-2080
• 
  William B. Langdon: Evo_indent interactive evolution of GNU indent options. 2081-2084
• 
  Jung-Yi Lin: Cancer classification using microarray and layered architecture genetic programming. 2085-2090
• 
  Matthias J. Linhardt, Martin V. Butz: NEAT in increasingly non-linear control situations. 2091-2096
• 
  Lingzhi Luo, Nilanjan Chakraborty, Katia P. Sycara: Prisoner's dilemma on graphs with heterogeneous agents. 2097-2102
• 
  Hossein Moeinzadeh, Babak Nasersharif, Abdolazim Rezaee, Hossein Pazhoumand-dar: Improving classification accuracy using evolutionary fuzzy transformation. 2103-2108
• 
  Hossein Moeinzadeh, Mehdi Mohammadi, Ahmad Akbari, Babak Nasersharif: Robust speech recognition using evolutionary class-dependent LDA. 2109-2114
• 
  Mehdi Mohammadi, Hossein Pazhoumand-dar, Mohsen Soryani, Hossein Moeinzadeh: HS-ROBDD: an efficient variable order binary decision diagram. 2115-2118
• 
  Michael E. Palmer, Daniel B. Miller: An evolved neural controller for bipedal walking with dynamic balance. 2119-2124
• 
  Brian Pinto, Andy Song: Motion detection in complex environments by genetic programming. 2125-2130
• 
  Outi Räihä, Erkki Mäkinen, Timo Poranen: Using simulated annealing for producing software architectures. 2131-2136
• 
  Anthony M. Roy, Erik K. Antonsson, Andrew A. Shapiro: An investigation into the structure of genomes within an evolution that uses embryogenesis. 2137-2142
• 
  Mohammad Samie, Gabriel Dragffy, Anthony G. Pipe: Novel bio-inspired self-repair algorithm for evolvable fault tolerant hardware systems. 2143-2148
• 
  Michael D. Schmidt, Hod Lipson: Solving iterated functions using genetic programming. 2149-2154
• 
  Kisung Seo, Soohwan Hyun, Erik D. Goodman: Tree-structure-aware GP operators for automatic gait generation of quadruped robot. 2155-2160
• 
  Waseem Shahzad, Abdul Basit Siddiqui, Farrukh Aslam Khan: Cryptanalysis of four-rounded DES using binary particleswarm optimization. 2161-2166
• 
  Tim Weninger, William H. Hsu, Jing Xia, Waleed Aljandal: An evolutionary approach to constructive induction for link discovery. 2167-2172
• 
  Deniz Yörükoglu, Yasin Bakis, Osman Ugur Sezerman: An entropy based heuristic model for predicting functional sub-type divisions of protein families. 2173-2178
• 
  Juan Cristóbal Zagal, Hod Lipson: Self-reflection in evolutionary robotics: resilient adaptation with a minimum of physical exploration. 2179-2188

Automated heuristic design: crossing the chasm for search methods

• 
  Matthew R. Hyde, Edmund K. Burke, Graham Kendall: Evolving human-competitive reusable 2D strip packing heuristics. 2189-2192
• 
  Djamila Ouelhadj, Sanja Petrovic, Ender Ozcan: A multi-level search framework for asynchronous cooperation of multiple hyper-heuristics. 2193-2196
• 
  Johann Dréo: Using performance fronts for parameter setting of stochastic metaheuristics. 2197-2200
• 
  Ender Özcan, A. Sima Etaner-Uyar, Edmund K. Burke: A greedy hyper-heuristic in dynamic environments. 2201-2204
• 
  Edmund K. Burke, Timothy Curtois, Graham Kendall, Matthew R. Hyde, Gabriela Ochoa, José Antonio Vázquez Rodríguez: Towards the decathlon challenge of search heuristics. 2205-2208
• 
  Sean A. Bittle, Mark S. Fox: Learning and using hyper-heuristics for variable and value ordering in constraint satisfaction problems. 2209-2212
• 
  Álvaro Fialho, Luís Da Costa, Marc Schoenauer, Michèle Sebag: Extreme: dynamic multi-armed bandits for adaptive operator selection. 2213-2216
• 
  Dirk Thierens: On benchmark properties for adaptive operator selection. 2217-2218

Symbolic regression and modeling workshop (SRM)

• 
  Michael F. Korns: Mutation and crossover with abstract expression grammars. 2219-2224
• 
  Eva Alfaro-Cid, Anna Esparcia-Alcázar, Pilar Moya, Juan Julián Merelo Guervós, Beatriu Femenia-Ferrer, Ken Sharman, Jaime Primo: Multiobjective genetic programming approach for a smooth modeling of the release kinetics of a pheromone dispenser. 2225-2230

Black box optimization benchmarking (BBOB)

• 
  José García-Nieto, Enrique Alba, Javier Apolloni: Noiseless functions black-box optimization: evaluation of a hybrid particle swarm with differential operators. 2231-2238
• 
  Benjamin Doerr, Mahmoud Fouz, Martin Schmidt, Magnus Wahlström: BBOB: Nelder-Mead with resize and halfruns. 2239-2246
• 
  Peter A. N. Bosman, Jörn Grahl, Dirk Thierens: AMaLGaM IDEAs in noiseless black-box optimization benchmarking. 2247-2254
• 
  Daniel Molina, Manuel Lozano, Francisco Herrera: A memetic algorithm using local search chaining forblack-box optimization benchmarking 2009 for noise free functions. 2255-2262
• 
  Mohammed El-Abd, Mohamed S. Kamel: Black-box optimization benchmarking for noiseless function testbed using an EDA and PSO hybrid. 2263-2268
• 
  Mohammed El-Abd, Mohamed S. Kamel: Black-box optimization benchmarking for noiseless function testbed using particle swarm optimization. 2269-2274
• 
  Mohammed El-Abd, Mohamed S. Kamel: Black-box optimization benchmarking for noiseless function testbed using PSO_bounds. 2275-2280
• 
  Marcus Gallagher: Black-box optimization benchmarking: results for the BayEDAcG algorithm on the noiseless function testbed. 2281-2286
• 
  Carlos García-Martínez, Manuel Lozano: A continuous variable neighbourhood search based on specialised EAs: application to the noiseless BBO-benchmark 2009. 2287-2294
• 
  Peter Korosec, Jurij Silc: A stigmergy-based algorithm for black-box optimization: noiseless function testbed. 2295-2302
• 
  Jirí Kubalík: Black-box optimization benchmarking of prototype optimization with evolved improvement steps for noiseless function testbed. 2303-2308
• 
  Petr Posik: BBOB-benchmarking a simple estimation of distribution algorithm with cauchy distribution. 2309-2314
• 
  Petr Posik: BBOB-benchmarking the DIRECT global optimization algorithm. 2315-2320
• 
  Petr Posik: BBOB-benchmarking the generalized generation gap model with parent centric crossover. 2321-2328
• 
  Petr Posik: BBOB-benchmarking two variants of the line-search algorithm. 2329-2336
• 
  Petr Posik: BBOB-benchmarking the Rosenbrock's local search algorithm. 2337-2342
• 
  José García-Nieto, Enrique Alba, Javier Apolloni: Particle swarm hybridized with differential evolution: black box optimization benchmarking for noisy functions. 2343-2350
• 
  Peter A. N. Bosman, Jörn Grahl, Dirk Thierens: AMaLGaM IDEAs in noisy black-box optimization benchmarking. 2351-2358
• 
  Daniel Molina, Manuel Lozano, Francisco Herrera: A memetic algorithm using local search chaining for black-box optimization benchmarking 2009 for noisy functions. 2359-2366
• 
  Carlos García-Martínez, Manuel Lozano: A continuous variable neighbourhood search based on specialised eas: application to the noisy BBO-benchmark 2009 testbed. 2367-2374
• 
  Peter Korosec, Jurij Silc: A stigmergy-based algorithm for black-box optimization: noisy function testbed. 2375-2382
• 
  Marcus R. Gallagher: Black-box optimization benchmarking: results for the BayEDAcG algorithm on the noisy function testbed. 2383-2388
• 
  Nikolaus Hansen: Benchmarking a BI-population CMA-ES on the BBOB-2009 function testbed. 2389-2396
• 
  Nikolaus Hansen: Benchmarking a BI-population CMA-ES on the BBOB-2009 noisy testbed. 2397-2402
• 
  Nikolaus Hansen: Benchmarking the nelder-mead downhill simplex algorithm with many local restarts. 2403-2408
• 
  Raymond Ros: Benchmarking the BFGS algorithm on the BBOB-2009 function testbed. 2409-2414
• 
  Raymond Ros: Benchmarking the BFGS algorithm on the BBOB-2009 noisy testbed. 2415-2420
• 
  Raymond Ros: Benchmarking the NEWUOA on the BBOB-2009 function testbed. 2421-2428
• 
  Raymond Ros: Benchmarking the NEWUOA on the BBOB-2009 noisy testbed. 2429-2434
• 
  Raymond Ros: Benchmarking sep-CMA-ES on the BBOB-2009 function testbed. 2435-2440
• 
  Raymond Ros: Benchmarking sep-CMA-ES on the BBOB-2009 noisy testbed. 2441-2446
• 
  Anne Auger: Benchmarking the (1+1) evolution strategy with one-fifth success rule on the BBOB-2009 function testbed. 2447-2452
• 
  Anne Auger: Benchmarking the (1+1)-ES with one-fifth success rule on the BBOB-2009 noisy testbed. 2453-2458
• 
  Anne Auger, Nikolaus Hansen: Benchmarking the (1+1)-CMA-ES on the BBOB-2009 function testbed. 2459-2466
• 
  Anne Auger, Nikolaus Hansen: Benchmarking the (1+1)-CMA-ES on the BBOB-2009 noisy testbed. 2467-2472
• 
  Miguel Nicolau: Application of a simple binary genetic algorithm to a noiseless testbed benchmark. 2473-2478
• 
  Anne Auger, Raymond Ros: Benchmarking the pure random search on the BBOB-2009 testbed. 2479-2484
• 
  Anne Auger, Raymond Ros: Benchmarking the pure random search on the BBOB-2009 noisy testbed. 2485-2490

Computational intelligence on consumer games and graphics hardware (CIGPU) 2009

• 
  Carlos Pérez-Miguel, José Miguel-Alonso, Alexander Mendiburu: Evaluating the cell broadband engine as a platform to run estimation of distribution algorithms. 2491-2498
• 
  John Rieffel, Frank Saunders, Shilpa Nadimpalli, Harvey Zhou, Soha Hassoun, Jason Rife, Barry Trimmer: Evolving soft robotic locomotion in PhysX. 2499-2504
• 
  Joseph M. Cavanagh, Thomas E. Potok, Xiaohui Cui: Parallel latent semantic analysis using a graphics processing unit. 2505-2510
• 
  William B. Langdon: A fast high quality pseudo random number generator for nVidia CUDA. 2511-2514
• 
  Man Leung Wong: Parallel multi-objective evolutionary algorithms on graphics processing units. 2515-2522
• 
  Shigeyoshi Tsutsui, Noriyuki Fujimoto: Solving quadratic assignment problems by genetic algorithms with GPU computation: a case study. 2523-2530
• 
  Garnett Carl Wilson, Wolfgang Banzhaf: Deployment of CPU and GPU-based genetic programming on heterogeneous devices. 2531-2538

Defense applications of computational intelligence

• 
  Riyad Alshammari, Peter Lichodzijewski, Malcolm I. Heywood, A. Nur Zincir-Heywood: Classifying SSH encrypted traffic with minimum packet header features using genetic programming. 2539-2546
• 
  Brendan J. Babb, Frank W. Moore, Michael R. Peterson: Improved multiresolution analysis transforms for satellite image compression and reconstruction using evolution strategies. 2547-2552
• 
  Bryant A. Julstrom: String- and permutation-coded genetic algorithms for the static weapon-target assignment problem. 2553-2558
• 
  Gary B. Lamont, Eric M. Holloway: Military network security using self organized multi-agent entangled hierarchies. 2559-2566

Evolutionary computation and multi-agent systems and simulation (ECoMASS)

• 
  James A. Wilson, Liying Yan: Self-organizing economic activity with costly information. 2567-2574
• 
  Camelia Chira, Anca Gog, D. Dumitrescu: Asynchronous collaborative search using adaptive co-evolving subpopulations. 2575-2582
• 
  Min Shi: Comparison of sorting algorithms for multi-fitness measurement of cooperative coevolution. 2583-2588
• 
  Eric M. Holloway, Gary B. Lamont: Self organized multi-agent entangled hierarchies for network security. 2589-2596
• 
  Peter J. Bentley: The game of funding: modelling peer review for research grants. 2597-2602

International workshop on learning classifier systems

• 
  Stewart W. Wilson: Coevolution of pattern generators and recognizers. 2605-2608
• 
  M. Zubair Shafiq, S. Momina Tabish, Muddassar Farooq: On the appropriateness of evolutionary rule learning algorithms for malware detection. 2609-2616
• 
  Ajay Kumar Tanwani, Muddassar Farooq: Performance evaluation of evolutionary algorithms in classification of biomedical datasets. 2617-2624
• 
  Richard Preen: An XCS approach to forecasting financial time series. 2625-2632
• 
  Alexander Scheidler, Martin Middendorf: Evolved cooperation and emergent communication structures in learning classifier based organic computing systems. 2633-2640

Learning from failures in evolutionary computation (LFFEC)

• 
  Wolfgang Konen, Thomas Bartz-Beielstein: Reinforcement learning for games: failures and successes. 2641-2648
• 
  Johannes W. Kruisselbrink, Michael T. M. Emmerich, Thomas Bäck: On the limitations of adaptive resampling in using the student's t-test evolution strategies. 2649-2656
• 
  Jörn Mehnen: Lessons learned in evolutionary computation: 11 steps to success. 2657-2660
• 
  J. David Schaffer, Heike Sichtig, Craig B. Laramee: A series of failed and partially successful fitness functions for evolving spiking neural networks. 2661-2664

Medical applications of genetic and evolutionary computation (MedGEC)

• 
  Robert M. Patton, Thomas E. Potok, Barbara G. Beckerman, Jim N. Treadwell: A genetic algorithm for learning significant phrase patterns in radiology reports. 2665-2670
• 
  Ping Wu, Erik D. Goodman, Tang Jiang, Min Pei: A hybrid GA-based fuzzy classifying approach to urinary analysis modeling. 2671-2678
• 
  Margarita Sordo, Gabriela Ochoa, Shawn N. Murphy: A PSO/ACO approach to knowledge discovery in a pharmacovigilance context. 2679-2684
• 
  Meng Yao, Qi-feng Pan, Zhi-fu Tao: Application of quantum genetic algorithm on breast tumor imaging with microwave. 2685-2688

Graduate student workshop

• 
  Abel Garcia-Najera: Preserving population diversity for the multi-objective vehicle routing problem with time windows. 2689-2692
• 
  Antonio López Jaimes, Carlos A. Coello Coello: Some techniques to deal with many-objective problems. 2693-2696
• 
  Arthur Gonçalves Carvalho, Aluizio F. R. Araújo: Improving NSGA-II with an adaptive mutation operator. 2697-2700
• 
  Benjamin James Bush: Solving the eltrut problem with hybrid evolutionary algorithms. 2701-2704
• 
  Erik Hemberg: An exploration of learning and grammars in grammatical evolution. 2705-2708
• 
  Kyriakos Kentzoglanakis, Matthew Poole: Gene network inference using a swarm intelligence framework. 2709-2712
• 
  Leonor Albuquerque Melo: Multi-colony ant colony optimization for the node placement problem. 2713-2716
• 
  Mark Coletti: Learnable evolution model performance impaired by binary tournament survival selection. 2717-2720
• 
  Ivette C. Martínez, Klaus Jaffe: Combined effect of the direction of information transmission and the spatiality over sustaining cooperation. 2721-2724
• 
  Nikhil Padhye, Subodh Kalia: Rapid prototyping using evolutionary approaches: part 1. 2725-2728

Undergraduate student workshop

• 
  Mustafa Safdari: Evolving universal hash functions using genetic algorithms. 2729-2732
• 
  Josh Glascock, Brian Hunter: Minimizing total completion time in two-machine flow shops with exact delay using genetic algorithm & ant colony algorithm. 2733-2736
• 
  Nikhil Padhye, Subodh Kalia: Rapid prototyping using evolutionary approaches: part 2. 2737-2740
• 
  Surabhi Gupta: Relative fitness scaling for improving efficiency of proportionate selection in genetic algorithms. 2741-2744
• 
  Steve R. Bergen: Evolving stylized images using a user-interactive genetic algorithm. 2745-2752

Tutorials

• 
  Erik D. Goodman: Introduction to genetic algorithms. 2753-2774
• 
  Riccardo Poli, Nicholas Freitag McPhee: Introduction to genetic programming. 2775-2810
• 
  Kenneth A. De Jong: A unified approach to Evolutionary Computation. 2811-2824
• 
  Christian Blum: Ant colony optimization. 2825-2852
• 
  Pier Luca Lanzi: Learning classifier systems. 2853-2878
• 
  Martin Pelikan: Probabilistic model-building genetic algorithms. 2879-2906
• 
  Conor Ryan: Grammatical evolution. 2907-2948
• 
  Mark Wineberg, Steffen Christensen: Statistical analysis for evolutionary computation: introduction. 2949-2976
• 
  Risto Miikkulainen, Kenneth O. Stanley: Evolving neural networks. 2977-3014
• 
  Riccardo Poli, William B. Langdon: Genetic programming theory I & II. 3015-3056
• 
  Darrell Whitley: No free lunch: 1995-2008. 3057-3088
• 
  Carlos Artemio Coello Coello: Constraint-handling techniques used with evolutionary algorithms. 3089-3110
• 
  Mark Wineberg, Steffen Christensen: Statistical analysis for evolutionary computation: advanced techniques. 3111-3130
• 
  Franz Rothlauf: Representations for evolutionary algorithms. 3131-3156
• 
  Thomas Jansen, Frank Neumann: Computational complexity and evolutionary computation. 3157-3184
• 
  Thomas Bartz-Beielstein, Mike Preuss: the future of experimental research. 3185-3226
• 
  L. Darrell Whitley, Andrew M. Sutton: Elementary landscape analysis. 3227-3236
• 
  Wolfgang Banzhaf, Simon Harding: Accelerating evolutionary computation with graphics processing units. 3237-3286
• 
  Lee Spector: Evolving quantum computer algorithms. 3287-3316
• 
  Yossi Borenstein: An information perspective on evolutionary computation. 3317-3334
• 
  Kenneth O. Stanley: Generative and developmental systems. 3335-3354
• 
  Mengjie Zhang, Stefano Cagnoni, Gustavo Olague: Evolutionary computer vision. 3355-3380
• 
  Jaume Bacardit, Xavier Llorà: Large scale data mining using genetics-based machine learning. 3381-3412
• 
  Rajeev Kumar: Evolutionary multiobjective combinatorial optimization (EMCO): emco. 3413-3436
• 
  Nawwaf N. Kharma, Luc Varin: Synthetic biology I. 3437-3438
• 
  Natalio Krasnogor: Synthetic biology: modelling and optimisation. 3439-3488
• 
  Julian Francis Miller, Simon L. Harding: Cartesian genetic programming. 3489-3512
• 
  Muddassar Farooq: Bio-inspired telecommunications. 3513-3550
• 
  Carsten Witt: Theory of randomised search heuristics in combinatorial optimisation: an algorithmic point of view. 3551-3592
• 
  Sébastien Vérel: Fitness landscapes and graphs: multimodularity, ruggedness and neutrality. 3593-3656
• 
  Leonardo Vanneschi: Fitness landscapes and problem hardness in genetic programming. 3657-3684

last updated on Tue May 21 02:00:03 2013 by the dblp team
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