PERFORMANCE ANALYSIS OF LINKAGE LEARNING TECHNIQUES IN GENETIC ALGORITHMS

One variance of Genetic Algorithms is a Linkage Learning Genetic Algorithm (LLGA) enhances the efficiencies of Simple Genetic Algorithm (SGA) while solving NP hard Problems. Discovery of Linkage Learning Technique is an important task in GA. Almost all existing Linkage Learning Techniques follow either random approach or probabilistic approaches. This makes repeated passes over the population to determine the relationship between individuals. SGA with random linkage technique is simple but may take long time to converge to the optimal solutions. This paper uses a linkage learning operator called Gene Silencing which is an inspired mechanism from biological systems. The Gene Silencing mechanism is used to improve the linkages by preserving the building blocks in an individual from the disruption of recombination processes such as Crossover and Mutation. It converges quickly to the optimal solution without compromising the diversification on search spaces. To prove this phenomenon, the Travelling Sales Person problem (TSP) has been chosen to retain the order of cities in a tour. Experiments carried out on different TSP benchmark instances taken from TSPLIB which is a standard library for TSP problems. These benchmark instances have also been applied on various linkage learning techniques and analyses the performance of these techniques with Gene Silencing (GS) mechanism. The performance analysis has been made on experimental results with respect to optimal solution and convergenc