Compact Genetic Algorithms with larger tournament size for soft-decision decoding

The standard compact Genetic Algorithm uses competition between two individuals for which we calculate the objective function and the winner will be the one who gives the best value of this function. This work presents a new approach of compact Genetic Algorithm that uses more than just two vectors of competition; it introduces tournament selection with larger size using mutation. We apply this algorithm to solve dual domain soft-decision decoding problem. A performance study of the new decoder will be done and the results will be compared to another compact genetic decoder that uses higher selection pressure with randomly generated individuals.