An Added Level of Sophistication
Randy L. Haupt, Sue Ellen Haupt · 2003
More detail is given about the specifics of coding a genetic algorithm (including the messy genetic algorithm) and how it converges (schema theorem). Multiple objective optimization and the Pareto genetic algorithm are discussed. Combining genetic algorithms with other optimization techniques (hybrid genetic algorithm) is introduced. Application of the genetic algorithm to permutation problems is presented. This chapter reviews the use of the crossover and mutation operators in detail and shows how to choose genetic algorithm parameters in order to minimize the number of calls to the cost function. Implementations of genetic algorithms on parallel computers are introduced. A comparison is made between the continuous and binary genetic algorithms.