ABSTRACT
This chapter describes the following intelligent optimization techniques, which reportedly successful in solving a wide variety of search and optimization problems in science, engineering and commerce, are Genetic algorithm (GA), Simulated annealing algorithm, Particle swarm optimization, Tabu search, Ant colony optimization. GAs are very different from traditional search and optimization methods used in different manufacturing problems. GAs were developed by John Holland of the University of Michigan in 1965. The working principles of GAs are very different from that of most of traditional optimization techniques. As seen from the description of GAs working principles, GAs are very different from most traditional optimization methods. In the prior discussion about GA operators or their working principles, nothing is mentioned about the gradient or any other auxiliary problem information. The more striking difference between GAs and most of the traditional optimization methods is that GAs work with a population of points instead of a single point.
