A New Crossover Technique in Genetic Algorithms

Genetic algorithms have been used for many years to solve optimization problems. They have been employed for many engineering application such as computer aided process planning, scheduling, plant layout, cell formation, prediction, supply chain management and many others. Any genetic algorithm at least has four steps in a complete cycle. The selection step plays the most important role in any genetic algorithm. It consists of two sub steps, crossover and mutation. This paper describes the development of a new crossover technique called Advanced Edge Recombination (AER) to increase the efficiency of genetic algorithms for combinatorial problems including traveling salesman problem, cell formation and cellular layout problem. The results obtained by this new technique have been compared with other existing techniques to prove its efficiencies over them.

A New Crossover Technique in Genetic Algorithms Keywords:

Genetic Algorithms , Crossover , TSP , Plant Layout , Cellular Manufacturing