Towards behavioral consistency in neuroevolution

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7426 LNAI, Issue , 2012, Pages 177-186

  Ollion, Charles ^a   Doncieux, Stéphane ^a  

^a UNIVERSITÉ PIERRE ET MARIE CURIE   (France)

Author keywords

[No Author keywords available]

Indexed keywords

BEHAVIORAL CONSISTENCY; COMPUTATIONAL NEUROSCIENCE; EVOLVING CONTROLLERS; FITNESS FUNCTIONS; NEURO EVOLUTIONS; OVERFITTING; SEARCH PROCESS; SYSTEM BEHAVIORS;

ARTIFICIAL INTELLIGENCE;

HEALTH;

EID: 84866010684     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-33093-3_18     Document Type: Conference Paper

References (10)

1. Nelson, A.L., Barlow, G.J., Doitsidis, L.: Fitness functions in evolutionary robotics: A survey and analysis. Robotics and Autonomous Systems 57(4) (April 2009)
2. Blynel, J., Floreano, D.: Exploring the T-Maze: Evolving Learning-Like Robot Behaviors Using CTRNNs. In: Raidl, G.R., Cagnoni, S., Cardalda, J.J.R., Corne, D.W., Gottlieb, J., Guillot, A., Hart, E., Johnson, C.G., Marchiori, E., Meyer, J.-A., Middendorf, M. (eds.) EvoWorkshops 2003. LNCS, vol. 2611, pp. 593-604. Springer, Heidelberg (2003)
3. Floreano, D., Urzelai, J.: Evolutionary robots with on-line self-organization and behavioral fitness. Neural Networks 13(4), 431-443 (2000)
4. Pinville, T., Koos, S., Mouret, J.B., Doncieux, S.: How to Promote Generalisation in Evolutionary Robotics: the ProGAb Approach Formalising the Generalisation Ability. In: GECCO 2011: Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, pp. 259-266 (2011)
5. Quinton, J.C.: Exploring and Optimizing Dynamic Neural Fields Parameters Using Genetic Algorithms. Statistics, 0-6 (2010)
6. Amari, S.I.: Dynamics of pattern formation in lateral-inhibition type neural fields. Biological Cybernetics 87, 77-87 (1977)
7. Rougier, N.P., Vitay, J.: Emergence of attention within a neural population. The Official Journal of the International Neural Network Society 19(5) (June 2006)
8. Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization: NSGA-II. In: Deb, K., Rudolph, G., Lutton, E., Merelo, J.J., Schoenauer, M., Schwefel, H.-P., Yao, X. (eds.) PPSN 2000. LNCS, vol. 1917, pp. 849-858. Springer, Heidelberg (2000)
9. Mouret, J.B., Doncieux, S., Girard, B.: Importing the computational neuroscience toolbox into neuro-evolution-application to basal ganglia. In: Proceedings of GECCO 2010, pp. 587-594 (2010)
10. Liénard, J., Guillot, A., Girard, B.: Multi-objective Evolutionary Algorithms to Investigate Neurocomputational Issues: The Case Study of Basal Ganglia Models. In: Doncieux, S., Girard, B., Guillot, A., Hallam, J., Meyer, J.-A., Mouret, J.-B. (eds.) SAB 2010. LNCS, vol. 6226, pp. 597-606. Springer, Heidelberg (2010)