Learning and lineage selection in genetic algorithms

Lineage selection is a process by which traits that are not directly assessed by the fitness function can evolve. Reported here is an investigation of the effects of individual learning on the evolution of one such trait, self-adaptive mutation rates. It is found that the efficacy of the learning mechanism employed (its potential to increase individual fitness) has a significant effect on the number of generations required for self-adaptive mutation rates to evolve. When highly efficient learning mechanisms are used the evolution of self-adaptive mutation rates requires a greater number of generations than in the absence of learning. Conversely, when less efficient learning mechanisms are used fewer generations are required, as compared to the non-learning case.