Introduction to Genetic Algorithms for Engineering Optimization

Abstract

A genetic algorithm (GA) is a search and optimization method which works 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 fitness which is directly related to the objective function of the search and optimization problem. Thereafter, the population of solutions is modified to a new population by applying three operators similar to natural genetic operators-reproduction, crossover, and mutation. A GA works iteratively by successively applying these three operators in each generation till a termination criterion is satisfied. Over the past couple of decades and more, GAs have been successfully applied to a wide variety of engineering problems, because of their simplicity, global perspective, and inherent parallel processing.