Genetic algorithm in search and optimization: the technique and applications

A genetic algorithm (GA) is a search and optimization method developed by mimicking the evolutionary principles and chromosomal processing in natural genetics. A GA begins its search with a random set of solutions usually coded in binary string structures. Every solution is assigned a Htness which is directly related to the objective function of the search and optimization problem. Thereafter, the population of solutions is modiiied to a new population by applying three operators similar to natural genetic operatorsfreproduction, crossover, and mutation. A GA works iteratively by successively applying these three operators in each generation till a termination criterion is satisiied. Over the past one decade, GAs have been successfully applied to a wide variety of problems, because of their simplicity, global perspective, and inherent parallel processing. In this paper, we outline the working principle of a GA by describing these three operators and by outlining an intuitive sketch of why the GA is a useful search algorithm. Thereafter, we apply a GA to solve a complex engineering design problem. Finally, we discuss how GAs can enhance the performance of other soft computing techniques-fuzzy logic and neural network techniques