Performance comparison between a simple genetic algorithm and a Hybrid Genetic Algorithm

The Hybrid Genetic Algorithm is developed that out performs a simple genetic algorithm in almost all problems that are presented. The Hybrid Genetic Algorithm is described in detail: pseudo-code is provided for it. and for many of the operators and algorithms presented. Advance operators such as inversion, preselection, and uniform crossover are used by the Hybrid Genetic Algorithm. Simulated annealing is used to initialize the population, and hill climbing is used to search locally for a solution. Eight problems of different levels of complexity arc used to compare the simple genetic algorithm and the Hybrid Genetic Algorithm --Abstract, page iii