A combinatorial optimization technique using genetic algorithm :a case study in machine layout problem

Solving Combinatorial Optimization Problem is significant a s it abounds in our daily lives. However, it is impractical to solve combinatorial optimization problems by exploring all the possible solutions due to combinatorial explosion. Genetic Algorithms (GAs) are a powerful stochastic search in solving optimization problems. However, conventional GAs with binary representation approach cannot be used in solving these kinds of problems. In this study, different crossover and mutation techniques a re adapted in GAs so that it suits to combinatorial optimization. In empirical tests, the combinatorial optimization techniques using GAs are able to approximating optimization, which had been justified theoretically in a simple Machine Layout Problem (MLP). Several complex cases of MLP also had been demonstrated and the results of different input parameters are compared