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Genetic Algorithm (GA) is one of the remarkable global search methods. In GA using the initial individual population, which is generated by random number, the possibility that the solutions fall into local minimum is high and its solutions are not good sometimes. In this paper, we propose the method, which is called the elite GA (EGA), to improve the solution in GA. Here, some individuals with good adaptation are found by executing GA of a few alternations of generations. The elite population is achieved by repeating the operation several times. Because the variety of the solution can be secured in the elite population, the improvement of the ability to search is expected. Moreover, we propose the island model EGA (Island EGA, IEGA) which is the parallel processing of EGA. In this paper, the simulation of minimizing LSI gate arrangement's area in the standard cell is executed by using each method which is the simulated annealing (SA), GA, EGA and IEGA. It is shown that the EGA and IEGA are more effective from these simulation results.

Standard Cell LSI Gate Arrangement Using the Island Elite Genetic Algorithm