Integration of genetic algorithm and cultural algorithms for constrained optimization

Authors:
Fang Gao;Gang Cui;Hongwei Liu
Affiliations:
School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China;School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China;School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
Venue:
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Year:
2006

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Abstract

In this paper, we propose to integrate real coded genetic algorithm (GA) and cultural algorithms (CA) to develop a more efficient algorithm: cultural genetic algorithm (CGA). In this approach, GA's selection and crossover operations are used in CA's population space. GA's mutation is replaced by CA based mutation operation which can attract individuals to move to the semifeasible and feasible region of the optimization problem to avoid the 'eyeless' searching in GA. Thus it is possible to enhance search ability and to reduce computational cost. This approach is applied to solve constrained optimization problems. An example is presented to demonstrate the effectiveness of the proposed approach.