Conference Paper

A Genetic Algorithm with Variable Range of Local Search for Tracking Changing Environments.

DOI: 10.1007/3-540-61723-X_1002 Conference: Parallel Problem Solving from Nature - PPSN IV, International Conference on Evolutionary Computation. The 4th International Conference on Parallel Problem Solving from Nature, Berlin, Germany, September 22-26, 1996, Proceedings
Source: DBLP

ABSTRACT In this paper we examine a modification to the genetic algorithm - a new adaptive operator was developed for two industrial applications using genetic algorithm based on-line control systems. The aim is to enable the control systems to track optima of a time-varying dynamic system whilst not being detrimental to its ability to provide sound results for the stationary environments. When compared with the hypermutation operator, the new operator matched the level of diversity introduced into the population with the "degree" of the environmental changes better because it increases population diversity only gradually. Although the new technique was developed for the control application domain where real variables are mostly used, a possible generalization of the method is also suggested. It is believed that the technique has the potential to be a further contribution in making genetic algorithm based techniques more readily usable in industrial control applications. The genetic algorithm is a proven search/optimisation technique (1) based on an adaptive mechanism of biological systems. The motivating context of Holland's initial work on genetic algorithms (GAs) was the design and implementation of robust adaptive systems in contrast to mere function optimisers (2). Understanding GAs in this broader adaptive system context is a necessary prerequisite for understanding their potential application to any problem domain and for understanding their relevant strengths and limitations as argued in the previously quoted paper. One important limiting factor for the use of the GA in real time applications common to many real world applications, whose models are not stationary, is the need for the repeated initialization of the GA from a random starting point in the search space to enable tracking optima in such changing/dynamic environments. The use of a repetitive learning cycle has obvious implications in terms of the quality of the solutions available which presents limitations on the use of genetic techniques in dynamic environments such as on-line industrial control. In this paper we present preliminary results of our research into techniques for genetic algorithm based robust systems which will continually evolve an optimal solution in

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