Measuring and Optimizing Behavioral Complexity for Evolutionary Reinforcement Learning-Reference-Cited by-同舟云学术

Measuring and Optimizing Behavioral Complexity for Evolutionary Reinforcement Learning

Published:2009 Issue: Volume: Page:765-774
ISSN:0302-9743
Container-title:Artificial Neural Networks – ICANN 2009
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Author:

Gomez Faustino J.,Togelius Julian,Schmidhuber Juergen

Publisher

Springer Berlin Heidelberg

Link

http://link.springer.com/content/pdf/10.1007/978-3-642-04277-5_77

Reference16 articles.

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3. De Jong, E.D., Watson, R.A., Pollack, J.B.: Reducing bloat and promoting diversity using multi-objective methods. In: Spector, L., Goodman, E.D., Wu, A., Langdon, W.B., Voigt, H.-M., Gen, M., Sen, S., Dorigo, M., Pezeshk, S., Garzon, M.H., Burke, E. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference, pp. 11–18. Morgan Kaufmann, San Francisco (2001)

4. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transaction on Evolutionary Computation 6, 182–197 (2002)

5. Gomez, F.: Sustaining diversity using behavioral information distance. In: Proceedings of the Genetic and Evolutionary Computation Conference (to appear, 2009)

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