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2003 | OriginalPaper | Chapter

Reinforcement Learning Estimation of Distribution Algorithm

Authors : Topon Kumar Paul, Hitoshi Iba

Published in: Genetic and Evolutionary Computation — GECCO 2003

Publisher: Springer Berlin Heidelberg

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This paper proposes an algorithm for combinatorial optimizations that uses reinforcement learning and estimation of joint probability distribution of promising solutions to generate a new population of solutions. We call it Reinforcement Learning Estimation of Distribution Algorithm (RELEDA). For the estimation of the joint probability distribution we consider each variable as univariate. Then we update the probability of each variable by applying reinforcement learning method. Though we consider variables independent of one another, the proposed method can solve problems of highly correlated variables. To compare the efficiency of our proposed algorithm with other Estimation of Distribution Algorithms (EDAs) we provide the experimental results of the two problems: four peaks problem and bipolar function.

Metadata
Title
Reinforcement Learning Estimation of Distribution Algorithm
Authors
Topon Kumar Paul
Hitoshi Iba
Copyright Year
2003
Publisher
Springer Berlin Heidelberg
DOI
https://doi.org/10.1007/3-540-45110-2_2