Maintaining Multiple Populations with Different Diversities for Evolutionary Optimization Based on Probability Models

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概要

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• This paper proposes a novel method, Hierarchical Importance Sampling (HIS) that can be used instead of population convergence in evolutionary optimization based on probability models (EOPM) such as estimation of distribution algorithms and cross entropy methods. In HIS, multiple populations are maintained simultaneously such that they have different diversities, and the probability model of one population is built through importance sampling by mixing with the other populations. This mechanism can allow populations to escape from local optima. Experimental comparisons reveal that HIS outperforms general EOPM.

• 2008-03-15

著者

• Takadama Keiki

  Faculty Of Electro-communications The University Of Electro-communications

• Higo Takayuki

  Interdisciplinary Graduate School Of Science And Engineering Tokyo Institute Of Technology

関連論文

• Maintaining Multiple Populations with Different Diversities for Evolutionary Optimization Based on Probability Models
• Maintaining Multiple Populations with Different Diversities for Evolutionary Optimization Based on Probability Models
• Maintaining Multiple Populations with Different Diversities for Evolutionary Optimization Based on Probability Models

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