 A REVIEW OF CROSSOVER METHODS AND PROBLEM REPRESENTATION OF GENETIC ALGORITHM IN RECENT ENGINEERING APPLICATIONS

Abstract

Genetic algorithm (GA) is a popular technique of optimization that is bio-inspired and based on Charles Darwin's proposed principles of natural genetics and natural selection theories. The algorithm operates on three simple genetic operators called selection, crossover and mutation. GA has many variations such as real coded and binary coded depending on the problem representation and so affects the forms of genetic operators. When optimizing process variables, the efficiency of crossover method is crucial. High efficiency of crossover operators enables minimizing the error occurred in engineering application optimization within a short time and cost. Unsuitable crossover method may cause inefficiency to explore the space of possible solutions thoroughly and effectively. This paper reviews crossover methods and problem representation, e.g. in the form of binary coded and real coded representation, used by researchers in order to solve engineering operations. It is expected that with the review of various types of crossovers, better insight in exploring new search spaces may be gained, and thus further varying the offsprings. At the end of the paper, some suggestions on how to achieve more efficient run of GA search in the scope of crossover technique in engineering applications are provided.