1. Jae C. Oh & Daniel Mosse: Games and Emergent Behaviors in Distributed Computing Environments
2. Larry Bull: Foundations of Learning Classifier Systems
3. Natalio Krasnogor: Workshop on Memetic Algorithms (WOMA-V)
4. Ashutosh Tiwari & Rajkumar Roy:Challenges in Real World Optimisation Using Evolutionary Computation

1. Jae C. Oh & Daniel Mosse: Games and Emergent Behaviors in Distributed Computing Environments
2. Will Browne: Future Directions for Learning Classifier Systems
3. Yong Xu & Sancho Salcedo-Sanz: Nature Inspired Approaches to Networks and Telecommunications
4. Waseem Asrar Ahmed: Data Preprocessing techniques for neural nets (Cancelled)
   John A. Bullinaria & Xin Yao: Evolving Neural Networks

1. Richard Watson: Introduction to population genetics
2. Darrell Whitley: Evolutionary algorithms for optimisation
3. Tim Kovacs: Classifier systems and reinforcement learning
4. Michael Vose:Genetic algorithm theory

1. James Foster: Biological applications of evolutionary computation
2. Marco Dorigo: Ant colony optimisation and swarm intelligence
3. Steve Phelps: Market-based systems
4. Adrian Thompson: Evolvable hardware

Poster Session 1

1. LS-CMA-ES: a Second-order algorithm for Covariance Matrix Adaptation. Auger, A., Schoenauer, M. and Vanhaecke, N.
2. The Ising Model: Simple Evolutionary Algorithms as Adaptation Schemes. Briest, P., Brockhoff, D., Degener, B., Englert, M., Gunia, C., Heering, O., Jansen, T., Leifhelm, M., Plociennik, K. and Röglin, H., Schweer, A., Sudholt, D., Tannenbaum, S.and Wegener, I .
3. Spread of Vector Borne Diseases in a Population with Spatial Structure. Chu, D. and Rowe, J.E.
4. Migration of Probability Models Instead of Individuals: an Alternative When Applying the Island Model to EDAs. De la Ossa, L., Gámez, J.A. and Puerta, J.M.
5. Control of bloat in Genetic Programming by means of the Island Model. Fernández, F., Galeano, G., Gómez, J.A. and Guisado, J.L.
6. Design Of An Efficient Search Algorithm For P2P Networks Using Concepts From Natural Immune Systems. Ganguly, N., Canright, G. and Deutsch, A.
7. Intransitivity in coevolution. De Jong, E .D.
8. Evolutionary Multi-Agent Systems. 't Hoen, P.J. and De Jong, E.D.
9. Fast Unsupervised Clustering with Artificial Ants. Labroche, N., Guinot, C. and Venturini, G.
10. A Primer on the Evolution of Equivalence Classes of Bayesian-Network Structures. Muruzábal, J. and Cotta, C.
11. Expected Runtimes of a Simple Evolutionary Algorithm for the Multi-objective Minimum Spanning Tree Problem. Neumann, F.
12. Sequential Process Optimisation Using Genetic Algorithms. Oduguwa, V., Tiwari, A. and Roy, R.
13. Behavior of Evolutionary Algorithms in Chaotically Changing Fitness Landscapes. Richter, H.
14. Dominance Based Crossover Operator for Evolutionary Multi-objective Algorithms. Rudenko, O. and Schoenauer, M.
15. A Neuroevolutionary Approach to Emergent Task Decomposition. Thangavelautham, J and D'Eleuterio, G.M.T.
16. Recognizing Speed Limit Sign Numbers by Evolvable Hardware. Torresen, J., Bakke, J.W. and Sekanina, L.

Poster Session 2

1. An Artificial Immune System for Fuzzy-Rule Induction in Data Mining. Alves, R.T., Delgado, M.R., Lopes, H.S. and Freitas, A.A.
2. Forecasting Time Series by means of Evolutionary Algorithms. Arco-Calderón, C. Luque del, Vinuela, P. Isasi , Castro, J. and Hernández C.
3. Learning Probabilistic Tree Grammars for Genetic Programming. Bosman, P.A.N. and De Jong, E.D.
4. Evolutionary Continuous Optimization by Distribution Estimation with Variational Bayesian Independent Component Analyzers Mixture Model. Cho, D.-Y. and Zhang, B.-T.
5. Robustness in the long run: Auto-teaching vs Anticipation in Evolutionary Robotics. Godzik, N., Schoenauer, M. and Sebag, M.
6. Evaluating the CMA Evolution Strategy on Multimodal Test Functions. Hansen, N. and Kern, S.
7. Bridging the Gap Between Theory and Practice. Jansen, T. and Wiegand, R.P.
8. Ensemble Learning with Evolutionary Computation: Application to Feature Ranking. Jong, K., Marchiori, E. and Sebag, M .
9. Credit Assignment among Neurons in Co-evolving Populations. Khare, V.R., Yao, X. and Sendhoff, B.
10. SPEA2+: Improving the Performance of the Strength Pareto Evolutionary Algorithm 2. Kim, M., Hiroyasu, T., Miki, M. and Watanabe, S.
11. Exploring the Evolutionary Details of a Feasible-Infeasible Two-Population GA in the Context of Constrained Optimization. Kimbrough, S.O., Lu, M. and Wood, H.D.
12. Evolving Genetic Regulatory Networks for Hardware Fault Tolerance. Koopman, A. and Roggen, D.
13. An Evolutionary Approach to Modeling Radial Brightness Distributions in Elliptical Galaxies. Li, J., Yao, X., Frayn, C., Khosroshahi, H.G. and Raychaudhury, S.
14. The Infection Algorithm: An Artificial Epidemic Approach for Dense Stereo Matching. Olagüe, G., Fernández, F., Pérez, C.B. and Lutton, E.
15. Multi-Objective Optimization of a Composite Material Spring Design Using an Evolutionary Algorithm. Ratle, F., Lecarpentier B., Labib, R. and Trochu, F.
16. Expected rates of building block discovery, retention and combination under 1-point and uniform crossover. Skinner, C. and Riddle, P.
17. An Analysis of the Effectiveness of Multi-parent Crossover. Ting, C.-K.

Poster Session 3

Chairman: JJ Merelo

1. An Inexpensive Cognitive Approach for Bi-Objective Optimization Using Bliss Points and Interaction. Abbass, H.A.
2. On the Quality Gain of (1, λ)-ES under Fitness Noise. Beyer, H.-G. and Meyer-Nieberg, S.
3. Sequential Sampling in Noisy Environments. Branke, J. and Schmidt, C.
4. Experimental Supplements to the Theoretical Analysis of EAs on Problems from Combinatorial Optimization. Briest, P., Brockhoff, D., Degener, B., Englert, M., Gunia, C., Heering, O., Jansen, T., Leifhelm, M., Plociennik, K. and Röglin, H., Schweer, A., Sudholt, D., Tannenbaum, S. and Wegener, I .
5. A Hybrid GRASP -- Evolutionary Algorithm Approach to Golomb Ruler Search. Cotta, C. and Fernández, A.
6. A Novel Ant Algorithm for Solving the Minimum Broadcast Time Problem. Hasson, Y. and Sipper, M.
7. Evolutionary Multiobjective Knowledge Extraction for High-Dimensional Pattern Classification Problems. Ishibuchi, H. and Namba, S.
8. Hierarchical Genetic Algorithms. De Jong, E.D., Thierens, D. and Watson, R.A.
9. The Application of Bayesian Optimization and Classifier Systems in Nurse Scheduling. Li, J. and Aickelin, U.
10. Natural Policy Gradient Reinforcement Learning for a CPG Control of a Biped Robot. Nakamura, Y., Mori, T. and Ishii, S.
11. A Novel Method of Searching the Microarray Data for the Best Gene Subsets by Using a Genetic Algorithm. Ni, B. and Liu, J.
12. A Mixed Bayesian Optimization Algorithm with variance adaptation. Ocenasek, J., Kern, S., Hansen, N.and Koumoutsakos, P.
13. Coupling of Evolution and Learning to Optimize a Hierarchical Object Recognition Model. Schneider, G., Wersing, H., Sendhoff, B. and Körner, E.
14. Using Genetic Programming for Feature Creation with a Genetic Algorithm Feature Selector. Smith, M.G. and Bull, L.
15. A Simple Two-Module Problem to Exemplify Building-Block Assembly Under Crossover. Watson, R.A.
16. Spatial Embedding and Loss of Gradient in Cooperative Coevolutionary Algorithms. Wiegand, R.P. and Sarma, J.
17. Indicator-Based Selection in Multiobjective Search. Zitzler, E. and Künzli, S.

Poster Session 4

1. A Simple Payoff-based Learning Classifier System. Bull, L.
2. AntHocNet: an Ant-Based Hybrid Routing Algorithm for Mobile Ad Hoc Networks. Di Caro, G., Ducatelle, F. and Gambardella, L.M.
3. A Scatter Search Algorithm for the 3D Image Registration Problem. Cordón, O., Damas, S. and Santamría, J.
4. On the use of a non-redundant encoding for learning Bayesian networks from data with a GA. Van Dijk, S. and Thierens, D.
5. Comparison of Steady-State and Generational Evolution Strategies for Parallel Architectures. Enache, R., Sendhoff, B. Olhofer, M. and Hasenjäger, M.
6. Evolutionary Multiobjective Clustering. Handl, J. and Knowles, J.
7. Designing Multiple-Use Primer Set for Multiplex PCR by Using Compact Gas. Huang, Y.-C., Chuang, H.-Y., Tsai, H.-K., Chang, C.-F. and Kao, C.-Y.
8. Self-Organizing Neural Grove: Efficient Multiple Classifier System Using Pruned Self-Generating Neural Trees. Inoue, H. and Narihisa, H.
9. An Approach to Evolutionary Robotics Using a Genetic Algorithm with a Variable Mutation Rate Strategy. Katada, Y., Ohkura, K.and Ueda, K.
10. Analyzing Sensor States and Internal States in the Tartarus Problem with Tree State Machines. Kim, D.
11. Multi-objective Optimisation by Co-operative Co-evolution. Maneeratana, K., Boonlong, K. and Chaiyaratana, N.
12. On Test Functions for Evolutionary Multi-Objective Optimization. Okabe, T., Jin, Y., Olhofer, M. and Sendhoff, B.
13. A Visual Demonstration of Convergence Properties of Cooperative Coevolution. Panait, L., Wiegand, P.R. and Luke, S.
14. Optimization via Parameter Mapping with Genetic Programming. Pujol, J.C.F. and Poli, R.
15. Robust Parallel Genetic Algorithms with Re-Initialisation. Sekaj, I.
16. A Powerful New Encoding for Tree-Based Combinatorial Optimisation Problems. Soak, S.-M., Corne, D. and Ahn, B.-H.
17. Statistical Racing Techniques for Improved Empirical Evaluation of Evolutionary Algorithms. Yuan, B. and Gallagher, M.

Poster Session 5

1. Lookahead and Latent Learning in a Simple Accuracy-based Classifier System. Bull, L.
2. Knowledge Extraction and Problem Structure Identification in XCS. Butz, M.V., Lanzi, P. L. and Llorŕ, X. and Goldberg, D.E.
3. Web Page Classification with an Ant Colony Algorithm. Holden, N. and Freitas, A.A.
4. Search Space Features Underlying the Performance of Stochastic Local Search Algorithms for MAX-SAT. Hoos, H.H., Smyth, K. and Stützle, T.
5. Multi-objective Parallel Tabu Search. Jaeggi, D., Asselin-Miller, C., Parks, G., Kipouros, T., Bell, T. and Clarkson, J.
6. An Evolutionary Algorithm for the Maximum Weight Trace Formulation of the Multiple Sequence Alignment Problem. Koller, G. and Raidl, G.R.
7. Evolving Dynamics in an Artificial Regulatory Network Model. Kuo, P.D., Leier, A. and Banzhaf, W.
8. A Novel Programmable Molecular Computing Method Based on Signaling Pathways Regulated by Rho-GTPases in Living MDCK Epithelial Mammalian Cells. Liu, J.-Q. and Shimohara, K.
9. Adaptive Weighted Particle Swarm Optimisation for Multi-objective Optimal Design of Alloy Steels. Mahfouf, M. and Chen, M.-Y. and Linkens, D.A.
10. Conference Paper Assignment Using a Combined Greedy/Evolutionary Algorithm. Merelo-Guervós, J.J. and Castillo-Valdivieso, P.
11. Distribution Tree-Building Real-valued Evolutionary Algorithm. Povsík, P.
12. On the Importance of Information Speed in Structured Populations. Preuss, M. and Lasarczyk, C.
13. Cooperative Coevolution of Image Feature Construction and Object Detection. Roberts, M.E. and Claridge, E.
14. Improving Evolutionary Algorithms with Multi-representation Island Models. Skolicki, Z. and De Jong, K.D.
15. Evolving the “Feeling” of Time Through Sensory-Motor Coordination: A Robot Based Model. Tuci, E., Trianni, V. and Dorigo, M.
16. Dynamic Routing Problems with Fruitful Regions: Models and Evolutionary Computation. Van Hemert, J.I. and La Poutré, J.A.
17. Optimising the Performance of a Formula One Car using a Genetic Algorithm. Wloch, K. and Bentley, P.

Poster Session 6

1. Speeding-up Pittsburgh Learning Classifier Systems: Modeling Time and Accuracy. Bacardit, J., Goldberg, D.E., Butz, M.V. and Llorŕ, X. and Garrell, J.M.
2. Fitness distributions and GA hardness. Borenstein, Y. and Poli, R.
3. Detecting and Pruning Introns for Faster Decision Tree Evolution. Eggermont, J., Kok, J.N. and Kosters, W.A.
4. Saving Resources with Plagues in Genetic Algorithms. Fernández de Vega, A F., Cantú-Paz, E., López, J.I. and Manzano, T.
5. Group Transport of an Object to a Target that Only Some Group Members May Sense. Gro b , R. and Dorigo, M.
6. A Self-Adaptive Neural Learning Classifier System with Constructivism for Mobile Robot Control. Hurst, J. and Bull, L.
7. Robust Inferential Sensors based on Ensemble of Predictors generated by Genetic Programming. Jordaan, E., Kordon, A., Chiang, L. and Smits, G.
8. Evolution of Voronoi-based Fuzzy Controllers. Kavka, C. and Schoenauer, M.
9. An Extension of Generalized Differential Evolution for Multi-Objective Optimization with Constraints. Kukkonen, S. and Lampinen, J.
10. Empirical Investigations on Parallelized Linkage Identification. Munetomo, M., Murao, N. and Akama, K.
11. Topology-Oriented Design of Analog Circuits Based on Evolutionary Graph Generation. Natsui, M., Homma, N., Aoki, T. and Higuchi, T.
12. Evaluation of Adaptive Nature Inspired Task Allocation Against Alternate Decentralised Multiagent Strategies. Price, R. and Tino, P.
13. An Evolutionary Algorithm for Column Generation in Integer Programming: an Effective Approach for 2D Bin Packing. Puchinger, J. and Raidl, G.R.
14. An Improved Evaluation Function for the Bandwidth Minimization Problem. Rodriguez-Tello, E., Hao J.-K. and Torres-Jimenez, J.
15. Multi-cellular Development: Is There Scalability and Robustness to Gain?. Roggen, D. and Federici, D.
16. Phase transition properties of clustered travelling salesman problem instances generated with evolutionary computation. Van Hemert, J.I. and Urquhart, N.B.
17. Evolutionary Bi-objective Controlled Elevator Group Regulates Passenger Service Level and Minimises Energy Consumption. Tyni, T. and Ylinen, J.

Poster Session 7

1. Metaheuristics for the Vehicle Routing Problem with Stochastic Demands. Bianchi, L., Birattari, M., Chiarandini, M., Manfrin, M., Mastrolilli, M., Paquete, L., Rossi-Doria, O. and Schiavinotto, T.
2. Finding Knees in Multi-objective Optimization. Branke, J., Deb, K. Dierolf, H. and Osswald, M.
3. Hawks, Doves and Lifetime Reproductive Success. Hingston, P. and Barone, L.
4. Evolutionary Algorithms with on-the-fly Population Size Adjustment. Eiben, A.E., Marchiori, E. and Valkó, V.A.
5. Oneric Processing Utilising the Anticipatory Classifier System. Holley, J.C., Pipe, A.G. and Carse, B.
6. Searching Transcriptional Modules Using Evolutionary Algorithms. Joung, J.-G., Oh, S.J. and Zhang, B.-T.
7. Translating the Dances of Honeybees into Resource Location. Kim, D.
8. A Reduced Markov Model of GAs without the Exact Transition Matrix. Moey, C.C.J. and Rowe, J.
9. The EAX algorithm considering diversity loss. Nagata, Y.
10. A Swarm Intelligence Based VLSI Multiplication-and-Add Scheme. Pani, D. and Raffo, L.
11. Optimising Cancer Chemotherapy Using Particle Swarm Optimisation and Genetic Algorithms. Petrovski, A., Sudha, B. and McCall, J.
12. Estimating the Number of Solutions for SAT Problems. Reeves, C.R. and Aupetit-Bélaidouni, M.
13. Constrained Evolutionary Optimization by Approximate Ranking and Surrogate Models. Runarsson, T.P.
14. Evolution of Small-World Networks of Automata for Computation. Tomassini, M., Giacobini, M. and Darabos, C.
15. Partially Evaluated Genetic Algorithm based on Fuzzy c-Means Algorithm. Yoo, S.-H. and Cho, S.-B.
16. AgentP Model: Learning Classifier System with Associative Perception. Zatuchna, Z.V.
17. A High Performance Multi-objective Evolutionary Algorithm Based on the Principles of Thermodynamics. Zou, X., Liu, M. and He, J.

Information for Poster Presenters

POSTERS

Please remember that posters are neither papers nor transparencies, though they are closer to the latter. Take the necessary time to prepare your poster appropriately, and please:

• DO NOT SIMPLY HANG UP A COPY OF YOUR PAPER.
• Use large fonts and magnify your figures and plots - the poster should be readable from some distance.
• Do not reproduce long sequences of equations or include a reference section - all attendees will have the proceedings in hand.
• Remember that you will be there to explain, so stick to the main issues and results.

BOARDS

The poster boards will be 1.22m (4 feet) wide and 1.83m (6 feet) high. If necessary, there will be room for posters to hang over the edge of this area. Fixing materials will be provided.

TIMING

You should put up your poster during the coffee or lunch break BEFORE your session. There is very little time available between sessions, so it is important that you remove your poster immediately at the end of your session (or it will be removed and thrown away for you). If you are not sure which session you are in - refer to the conference programme.

INTRODUCTIONS

At the beginning of each session, the Session Chair will give a short presentation introducing all the posters in that session.