Solving Optimization Problem by Hybrid Genetic Algorithm using Hill Climbing in Replacement Operator

摘要

Genetic algorithm is a population-based search and exploiting objective function. Every basic genetic operators used in a simple GA utilizes “random choice” to an extent or another. Optimization ability can be improved when problem specific knowledge is incorporated and goal oriented operators are used. The population based local refinement mechanism, searches the local area for minima. GA and neighborhood search technique will result in early findings of the optima. In this paper implementation of hill climbing in replacement operator and empirically analyze the convergence rate of hybrid algorithm with simple genetic algorithm. Both algorithms use the complementary property of exploitation to find optimal solution. Memetic algorithm performs good to find optimal result of complex problems. Performance of memetic algorithm is affected by population size and number of generated.