Memetic Algorithm: Hybridization of Hill Climbing with Replacement Operator

Genetic Algorithms are the population based search and optimization technique that mimic the process of natural evolution. 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 individual mutation operation. The experiments have been conducted using three different benchmark instances of tsp and implementation is carried out using MATLAB. The problems result shows that the proposed memetic algorithm performs better than the genetic algorithm in terms of producing more optimal results and maintains balance between exploitation and exploration within the search space.