Effect of Hybridization of Hill Climbing with Selection Operator in Genetic Algorithm

Premature Convergence and genetic drift are the inherent characteristics of genetic algorithms that make them incapable of finding global optimal solution. A memetic algorithm is an extension of genetic algorithm that incorporates the local search techniques within genetic operations so as to prevent the premature convergence and improve performance in case of NP-hard problems. This paper proposes a new memetic algorithm where hill climbing local search is applied to each parent selected by selection operators. The experiments have been conducted on two TSP benchmark problems. Implementation is carried out using MATLAB and results shows that the proposed memetic algorithm maintains the balance between exploitation and exploration & outperforms genetic algorithm in terms of producing more optimal solution.