A taxonomy for the crossover operator for real-coded genetic algorithms: An experimental study

摘要: The main real-coded genetic algorithm (RCGA) research effort has been spent on developing efficient crossover operators. This study presents a taxonomy for this operator that groups its instances in different categories according to the way they generate genes of offspring from parents. empirical representative crossovers all reveals concrete features allow have positive influence RCGA performance. They may be useful design more effective models. © 2003 Wiley Periodicals, Inc. Genetic algorithms (GAs) are adaptive methods based natural evolution used search and optimization problems. process population space solutions with three operations: selection, crossover, mutation. 1‐3 Under their initial formulation, coded using binary alphabet; however, other coding types taken into account representation issue such as real coding. approach seems particularly when tackling problems parameters variables continuous domains. A chromosome is vector floating point numbers which size kept same length vector, solution problem. GAs real-number called