On the Design of a Parallel Genetic Algorithm Based on a Modified Survival Method for Evolvable Hardware

In this paper, we propose a Parallel Genetic Algorithm (PGA) based on a modified survival method and discuss its efficient implementation. For parallel computation, we use a hybrid distributed architecture based on the coarse-grain and fine-grain. Moreover, we propose a modified survival-based GA using tournament selection method. To show the validity of a proposed PGA, we evaluate its performance with optimization problems such as DeJong's functions, mathematical function, and set covering problem. In addition, we implement a PGA processor with ALTERA EP2A40672F FPGA device. The experimental results will be shown that proposed PGA remarkably improves the speed of finding optimal solution than single GAP.

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