On evolutionary optimization of large problems using small populations

Authors:
Yaochu Jin;Markus Olhofer;Bernhard Sendhoff
Affiliations:
Honda Research Institute Europe, Offenbach/Main, Germany;Honda Research Institute Europe, Offenbach/Main, Germany;Honda Research Institute Europe, Offenbach/Main, Germany
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Year:
2005

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Abstract

Small populations are very desirable for reducing the required computational resources in evolutionary optimization of complex real-world problems. Unfortunately, the search performance of small populations often reduces dramatically in a large search space. To addresses this problem, a method to find an optimal search dimension for small populations is suggested in this paper. The basic idea is that the evolutionary algorithm starts with a small search dimension and then the search dimension is increased during the optimization. The search dimension will continue to increase if an increase in the search dimension improves the search performance. Otherwise, the search dimension will be decreased and then kept constant. Through empirical studies on a test problem with an infinite search dimension, we show that the proposed algorithm is able to find the search dimension that is the most efficient for the given population size.