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

Solving Combinatorial Optimization Problem is significant a \ns it abounds in our daily lives. \nHowever, it is \nimpractical to solve combinatorial optimization problems by exploring all the \npossible solutions \ndue to combinatorial explosion. Genetic \nAlgorithms (GAs) are a powerful stochastic search in solving optimization problems. \nHowever, conventional GAs with binary representation approach cannot be used in \nsolving these kinds of problems. In this study, different crossover and mutation techniques a \nre adapted in GAs so that it suits \nto combinatorial optimization. In \nempirical tests, the combinatorial optimization techniques using GAs are able to \napproximating optimization, which had been \njustified theoretically in a \nsimple Machine Layout Problem (MLP). \nSeveral complex cases \nof MLP also had been \ndemonstrated and the results \nof different input parameters are \ncompared.