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Reinforcement Learning Estimation of Distribution Algorithm
Topon Kumar Paul and Hitoshi Iba
Graduate School of Frontier Sciences
The University of Tokyo
Hongo 7-3-1, Bunkyo-ku
Tokyo 113-8656, Japan
{topon,iba}@miv.t.u-tokyo.ac.jp
Abstract. 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.
LNCS 2724, p. 1259 ff.
lncs@springer.de
© Springer-Verlag Berlin Heidelberg 2003