Memetic Algorithm: Hybridization at Initialization in Genetic Algorithm with Hill Climbing

Genetic algorithm is a problem solving method that uses genetics as its model of problem solving. It is a search technique to find approximate solutions to optimization and search problems. Genetic algorithm handles a population of possible solutions. Each solution is represented through a chromosome and they mimic the process of natural evolution. A memetic algorithm is an extension of genetic algorithm in which genetic algorithm is hybridized with local search technique to improve the performance of genetic algorithm. This paper proposes a memetic algorithm in which hybridization is applied at initial stage using hill climbing local search. Performance of the memetic algorithm is compared with simple genetic algorithm. The experiments have been conducted using knapsack 0/1 problem and implementation is carried out using matlab. The results show that the proposed hybrid genetic algorithm is more optimized than simple genetic algorithm.