Homogeneous genetic algorithms
Alexander Stanoyevitch · International Journal of Computer Mathematics · 2008
A new type of genetic algorithm (GA) is developed to mitigate one or both of the following two major difficulties that traditional GAs may suffer: (1) when the number of ‘active genes’ needs to be held constant or kept within some prescribed range, and (2) when the set of genes is much larger than the set of active genes of feasible solutions under consideration. These homogeneous GAs use (unordered) sets to represent ‘active genes’ in chromosomes rather than strings, and a correspondingly natural crossover operator is introduced. ‘Homogeneous’ refers to the fact that, in contrast to traditional GAs where pairs of genes that are ‘close’ have better chances of being preserved under crossover, there is no notion of proximity between pairs of genes. Examples are provided that will demonstrate superior performance of these new GAs for some typical problems in which these difficulties arise.