Affiliation:
1. Xidian University
2. Northwestern Polytechnical University
Abstract
In dynamic environments, it is difficult to track a changing optimal solution over time. Over the years, many approaches have been proposed to solve the problem with genetic algorithms. In this paper a new space-based immigrant scheme for genetic algorithms is proposed to solve dynamic optimization problems. In this scheme, the search space is divided into two subspaces using the elite of the previous generation and the range of variables. Then the immigrants are generated from both the subspaces and inserted into current population. The main idea of the approach is to increase the diversity more evenly and dispersed. Finally an experimental study on dynamic sphere function was carried out to compare the performance of several genetic algorithms. The experimental results show that the proposed algorithm is effective for the function with moving optimum and can adapt the dynamic environments rapidly.
Publisher
Trans Tech Publications, Ltd.
Reference9 articles.
1. J. Branke, Evolutionary optimization in dynamic environments. Norwell, MA: Kluwer, (2001).
2. Y. Jin, J. Branke, Evolutionary optimization in uncertain environments-a survey, IEEE transactions on evolutionary computations, 2005, 9(3): 1-15.
3. H. M. Cartwright, A. L. Tuson Genetic algorithms and flowshop scheduling: Towards the development of a real-time process control system. Proc of the AISB Workshop on Evolutionary Computing, San Francisco: Morgan Kaufmann Publishers, 1994: 277-290.
4. H. G. Cobb, An investigation into the use of hypermutation as an adaptive operator in genetic algorithms having continuous, time-dependent nonstationary environment. Washington: Naval Research Laboratory, (1990).
5. J. J. Grefenstette, Genetic algorithms for changing environments, Parallel Problem Solving from Nature, Brussels, 1992: 137-144.
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