Sim-EDA: A Multipopulation Estimation of Distribution Algorithm Based on Problem Similarity

Abstract. In this paper a new estimation of distribution algorithm SimEDA is presented. This algorithm combines a multipopulation approach with distribution modelling. The proposed approach is to tackle several similar instances of the same optimization problem at once. Each subpopulation is assigned to a different instance and a migration mechanism is used for transferring information between the subpopulations. The migration process can be performed using one of the proposed strategies: two based on similarity between problem instances and one which migrates specimens between subpopulations with uniform probability. Similarity of problem instances is expressed numerically and the value of the similarity function is used for determining how likely a specimen is to migrate between two populations. The Sim-EDA algorithm is a general framework which can be used with various EDAs. The presented algorithm has been tested on several instances of the Max-Cut and TSP problems using three diï...

• Type: Journal
• Year: 2016
• Where: EVOW
• Authors: Krzysztof Michalak
• Keywords: Artificial Intelligence, EVOW 2016