Evolutionary learning through replicator dynamics in continuous space games

Julide Yazar

doi:10.1117/12.604232

28 March 2005 Evolutionary learning through replicator dynamics in continuous space games

Julide Yazar

Proceedings Volume 5803, Intelligent Computing: Theory and Applications III; (2005) https://doi.org/10.1117/12.604232
Event: Defense and Security, 2005, Orlando, Florida, United States

Abstract

Many situations involving strategic interaction between agents involve a continuos set of choices. Therefore it is natural to model these problems using continuous space games. Consequently the population of agents playing the game will be represented with a density function defined over the continuous set of strategy choices. Simulating evolution of this population is a challenging problem. We present a method for simulating replicator dynamics in continuos space games using sequential Monte Carlo methods. The particle approach to density estimation provides a computationally efficient way of modeling the evolution of probability density functions. Finally a resampling step and smoothing methods are used to prevent particle degeneration problem associated with particle estimates. Finally we compare and contrast the resulting algorithm for simulation with genetic programming techniques.

Citation Download Citation

Julide Yazar "Evolutionary learning through replicator dynamics in continuous space games", Proc. SPIE 5803, Intelligent Computing: Theory and Applications III, (28 March 2005); https://doi.org/10.1117/12.604232

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