Using behavioral exploration objectives to solve deceptive problems in neuro-evolution

Proceedings of the 11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009

Volumn , Issue , 2009, Pages 627-634

  Mouret, Jean Baptiste ^a   Doncieux, Stéphane ^a  

^a UNIVERSITÉ PIERRE ET MARIE CURIE   (France)

Author keywords

Deceptive problems; Diversity; Multiobjective evolutionary algorithm; Neural networks

Indexed keywords

ALGORITHMIC COMPLEXITY; BEHAVIORAL DIVERSITY; CONTROL EXPERIMENTS; DECEPTIVE PROBLEM; DIVERSITY; EFFICIENT METHOD; ENCODINGS; EVOLVING NEURAL NETWORK; FITNESS FUNCTIONS; GRAPH SIMILARITY; MULTI OBJECTIVE EVOLUTIONARY ALGORITHMS; PREMATURE CONVERGENCE;

BOOLEAN FUNCTIONS; COMPUTATIONAL COMPLEXITY; COMPUTATIONAL EFFICIENCY; FUNCTION EVALUATION; MULTIOBJECTIVE OPTIMIZATION; NEURAL NETWORKS; PARALLEL PROCESSING SYSTEMS; PROBLEM SOLVING;

EVOLUTIONARY ALGORITHMS;

EID: 72749114789     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1569901.1569988     Document Type: Conference Paper

References (29)

1. H. A. Abbass and K. Deb. Searching under multi-evolutionary pressures. Proceedings of the Fourth Conference on Evolutionary Multi-Criterion Optimization, 2003.
2. L. Bui, H. A. Abbass, and J. Branke. Multiobjective optimization for dynamic environments. The 2005 IEEE Congress on Evolutionary Computation (CEC), 3:2349-2356 Vol. 3, 2005.
3. H. Bunke. Recent developments in graph matching. Proc. 15th International Conference on Pattern Recognition, pages 117-124, 2000.
4. E. Burke, S. Gustafson, and G. Kendall. A survey and analysis of diversity measures in genetic programming. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'02), pages 716-723, 2002.
5. C. A. Coello Coello and G. B. Lamont. Applications Of Multi-Objective Evolutionary Algorithms. World Scientific, 2004.
6. E. D. de Jong, R. A. Watson, and J. B. Pollack. Reducing bloat and promoting diversity using multi-objective methods. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'01), pages 11-18, 2001.
7. K. Deb. Multi-objectives optimization using evolutionnary algorithms. Wiley, 2001.
8. K. Deb, S. Agrawal, A. Pratab, and T. Meyarivan. A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization: NSGA-II. In Proceedings of the Parallel Problem Solving from Nature VI Conference, pages 849-858, 2000.
9. K. Deb, M. Mohan, and S. Mishra. Evaluating the ∈-Domination Based Multi-Objective Evolutionary Algorithm for a Quick Computation of Pareto-Optimal Solutions. Evolutionary Computation, 13(4):501-525, 2005.
10. S. Doncieux and J.-A. Meyer. Evolving PID-like neurocontrollers for non-linear control problems. International Journal of Control and Intelligent Systems (IJCIS). Special Issue on nonlinear adaptive PID control, 33(1):55-62, 2005.
11. C. M. Fonseca and P. J. Fleming. Genetic algorithms for multiobjective optimization : formulation, discussion and generalization. In Proceedings of the Fourth International Conference on Evolutionary Programming, pages 416-423, 1993.
12. D. E. Goldberg. Simple genetic algorithms and the minimal, deceptive problem. Genetic Algorithms and Simulated Annealing, 74, 1987.
13. D. E. Goldberg, K. Deb, and J. Horn. Massive multimodality, deception, and genetic algorithms. In Proceedings of the Second Conference on Parallel Problem Solving from Nature, pages 37-48, 1992.
14. D. E. Goldberg and J. Richardson. Genetic algorithms with sharing for multimodal function optimization. In Proceedings of the Second International Conference on Genetic Algorithms, pages 148-154, 1987.
15. F. Gruau. Automatic definition of modular neural networks. Adaptive Behaviour, 3(2):151-183, 1995.
16. J. Handl, S. C. Lovell, and J. Knowles. Multiobjectivization by decomposition of scalar cost functions. In Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X, pages 31-40. Springer, 2008.
17. J. H. Holland. Adaptation in Natural and Artificial Systems. MI: University of Michigan Press, 1975.
18. N. Kashtan and U. Alon. Spontaneous evolution of modularity and network motifs. Proceedings of the National Academy of Sciences, 102(39):13773-13778, 2005.
19. J. D. Knowles, R. A. Watson, and D. W. Corne. Reducing Local Optima in Single-Objective Problems by Multi-objectivization. First International Conference on Evolutionary Multi-Criterion Optimization, 1993:268-282, 2001.
20. J. Lehman and K. Stanley. Exploiting open-endedness to solve problems through the search for novelty. In Artificial Life XI: Proceedings of the Eleventh International Conference on the Simulation and Synthesis of Living Systems, pages 329-336, 2008.
21. D. Lopresti and G. Wilfong. A fast technique for comparing graph representations with applications to performance evaluation. International Journal on Document Analysis and Recognition, 6(4):219-229, 2003.
22. J.-B. Mouret and S. Doncieux. Mennag: a modular, regular and hierarchical encoding for neural-networks based on attribute grammars. Evolutionary Intelligence, 1:187-207, 2008.
23. J.-B. Mouret and S. Doncieux. Overcoming the bootstrap problem in evolutionary robotics using behavioral diversity. In IEEE Congress Evolutionary Computation (CEC)., page to appear, 2009.
24. K. Praditwong and X. Yao. How well do multi-objective evolutionary algorithms scale to large problems. In IEEE Congress on Evolutionary Computation (CEC)., pages 3959-3966, 2007.
25. R. C. Purshouse and P. J. Fleming. On the Evolutionary Optimization of Many Conicting Objectives. IEEE Transactions on Evolutionary Computation, 11(6):770-784, 2007.
26. R. Reveaux, B. Eugen, H. Locteau, S. Adam, P. Heroux, and E. Trupin. A Graph Classification Approach Using a Multi-objective Genetic Algorithm Application to Symbol Recognition. In IAPR GBR Workshop on Graph-based Representations in Pattern Recognition, volume 4538, page 361, 2007.
27. K. O. Stanley and R. Miikkulainen. Evolving neural networks through augmenting topologies. Evolutionary Computation, 10(2)(99-127), 2002.
28. A. Toffolo and E. Benini. Genetic diversity as an objective in multi-objective evolutionary algorithms. Evolutionary Computation, 11(2):151-167, 2003.
29. X. Yao. Evolving artificial neural networks. Proceedings of the IEEE, 87(9):1423-1447, 1999.