Utilizing Hybrid Genetic Algorithms

Genetic algorithms (GAs) have been shown to be quite effective at solving a wide range of difficult problems. They are very efficient at exploring the entire search space; however, they are relatively poor at finding the precise local optimal solution in the region in which the algorithm converges. Hybrid GAs are the combination of local improvement procedures, which are good at finding local optima, and genetic algorithms. Hybrid GAs have been shown to be quite effective at solving a wide range of problems. How the GA (the global explorer) and the local improvement procedure (the local exploiter) are combined is extremely important with respect to the final solution quality as well as the computational efficiency of the algorithm. Several different combination strategies will be investigated to determine the most effective method. Furthermore, a new adaptive memory technique will be used to enhance these methods.

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