GECCO 2009: Montreal, Québec, Canada - Companion Material

Late-breaking papers

• Adrian 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 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, M-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 Ozcan, Sima Etaner Uyar, Edmund Burke:
  A greedy hyper-heuristic in dynamic environments. 2201-2204
  
• Edmund K. Burke, Timothy Curtois, Graham Kendall, Matthew R. Hyde, Gabriela Ochoa, Jose A. Vazquez-Rodriguez:
  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 Kubalik:
  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 Perez-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 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