A distributed genetic algorithm for multivariable fuzzy control

The traditional approach to multiple parameter optimization in genetic algorithm (GA) practice is to combine the coding of the parameters into a single compound bit-string; the so-called concatenated binary mapping. This approach has some shortcomings; the GA is a competition-based technique that has a natural tendency to evolve one winner which in complex problems yields a solution that is better on some parameters than the others. An extension to the simple GA, called vector evaluated genetic algorithm (VEGA), has been used in multiobjective optimization where one is not interested in a single solution, but a family of optimal solutions. In VEGA each member of the population is evaluated and assigned a weighted fitness value dependent on how it relates to each objective criteria. The reproduction plan then develops groupings within the populations for each of the objectives to be optimized, ensuring that the improvement of one objective does not adversely affect the others. This, however, requires large population sizes and can be quite inefficient. In cases where the complex task is divisible into simpler optimization problems, a better solution set may be obtained using parallel genetic algorithms to search for the optimal solution to each sub-problem.<
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