Using ensemble method to improve the performance of genetic algorithm

Authors:
Shude Zhou;Zengqi Sun
Affiliations:
State Key Lab of Intelligent Technology and Systems, Department of Computer Science and Technology, Tsinghua University, Beijing, China;State Key Lab of Intelligent Technology and Systems, Department of Computer Science and Technology, Tsinghua University, Beijing, China
Venue:
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
Year:
2005

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Abstract

Ensemble method has been deeply studied and widely used in the machine learning communities. Its basic idea can be represented as: A ‘weak’ learning algorithm that performs just slightly better than random guessing can be ‘boosted’ into an arbitrarily accurate ‘strong’ learning algorithm. Inspired from the fascinating idea, the paper used ensemble method to improve the performance of genetic algorithm and proposed an efficient hybrid optimization algorithm: GA ensemble. In GA ensemble, a collection of genetic algorithms are designed to solve the same problem and population of each algorithm is sampled from a solutions pool using bagging method. Experiments on combinatorial optimization problem and GA-deceptive problems show that ensemble method improves the performance of genetic algorithm greatly.