Real-coded evolutionary algorithms with parent-centric recombination

Abstract

Abstract-Due to an increasing interest in solving real-world optimization problems using evolutionary algorithms (EAs), researchers have developed a number of real-parameter genetic algorithms (GAs) in the recent past. In such studies, the main research effort is spent on developing an efficient recombination operator. Such recombination operators use probability distributions around the parent solutions to create offspring. Some operators emphasize solutions at the center of mass of parents and some emphasize solutions around the parents. In this paper, we propose a generic parent-centric recombination operator (PCX) and compare its performance with a couple of commonly-used mean-centric recombination operators (UNDX and SPX). With the help of a steady-state, elite-preserving, and computationally fast EA model, the simulation results show the superiority of PCX over mean-centric operators on three test problems.

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

“…1 However, multiparent crossover operators have been proposed, which combine the features of more than two parents for generating the offspring. 34,[37][38][39] Furthermore, crossover operators with multiple descendents have been presented, 34 -36,40 -42 which produce more than two offspring for each group of parents. In this case, the OSM limits the number of offspring that will be population members.…”

Multiple Crossover per Couple with Selection of the Two Best Offspring: An Experimental study with the BLX-α Crossover Operator for Real-Coded Genetic Algorithms

“…In [14], the generation of multiple descendants is proposed, selecting the two chromosomes for the next population from the named family, the parents and their children (MGG model). In [3], a generalization of this model is presented. In this case, one of the chromosomes is the best one of the population and another is selected by a classical sampling mechanism (or various ones in the case of multiple parents) and, taking them as point of departure, multiple descendants are generated; finally, the two best chromosomes of the family are selected (G3 model).…”

Particle swarm optimization with crossover: a review and empirical analysis

“…3.4 Parent-Centric Crossover Deb et al (2002) developed the parent-centric crossover (PCX) operator as a variation of the unimodal distributed (UNDX) operator (Ono and Kobayashi, 1997). The PCX operator implements a multi-parent crossover which blends the candidate solutions represented by the selected parents to produce a single offspring.…”