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A new real-coded Bayesian optimization algorithm based on a team of learning automata for continuous optimization

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Abstract

Estimation of distribution algorithms have evolved as a technique for estimating population distribution in evolutionary algorithms. They estimate the distribution of the candidate solutions and then sample the next generation from the estimated distribution. Bayesian optimization algorithm is an estimation of distribution algorithm, which uses a Bayesian network to estimate the distribution of candidate solutions and then generates the next generation by sampling from the constructed network. The experimental results show that the Bayesian optimization algorithms are capable of identifying correct linkage between the variables of optimization problems. Since the problem of finding the optimal Bayesian network belongs to the class of NP-hard problems, typically Bayesian optimization algorithms use greedy algorithms to build the Bayesian network. This paper proposes a new real-coded Bayesian optimization algorithm for solving continuous optimization problems that uses a team of learning automata to build the Bayesian network. This team of learning automata tries to learn the optimal Bayesian network structure during the execution of the algorithm. The use of learning automaton leads to an algorithm with lower computation time for building the Bayesian network. The experimental results reported here show the preference of the proposed algorithm on both uni-modal and multi-modal optimization problems.

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Acknowledgments

The authors would like to thank reviewers for their time, comments and constructive criticism, which improved the paper.

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Authors and Affiliations

  1. Department of Computer Engineering, Sharif University of Technology, Tehran, Iran

    Behnaz Moradabadi & Hamid Beigy

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  1. Behnaz Moradabadi
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  2. Hamid Beigy
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Correspondence to Behnaz Moradabadi.

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Moradabadi, B., Beigy, H. A new real-coded Bayesian optimization algorithm based on a team of learning automata for continuous optimization. Genet Program Evolvable Mach 15, 169–193 (2014). https://doi.org/10.1007/s10710-013-9206-9

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