Optimization of autonomous agents by means of learning and evolution
Interaction between learning and evolution in populations of autonomous agents is investigated. Any agent of the population has both the genotype (the genome) and the phenotype. The genotype and the phenotype are chains of binary symbols. The initial phenotype (at the moment of the agent birth) is equal to the agent genotype. There is a certain optimum; namely, there is the optimal chain that is searched for by means of learning and evolution. Genotypes are optimized by evolution; phenotypes are optimized by learning. The final phenotype (at the moment of the end of the agent life) determines the agent fitness. Three mechanisms of interaction between learning and evolution are investigated. (1) The mechanism of the genetic assimilation of the acquired features during a number of generations of Darwinian evolution is analyzed. It is shown that the genetic assimilation takes place as follows: the phenotype distribution moves towards the optimum at learning and further selection; subsequently the genomes of selected organisms also move towards the optimum. (2) The mechanism of the hiding effect is studied; this effect means that strong learning can ensure finding the optimal phenotype independently on the agent genotype in some situations; consequently, strong learning can inhibit the genotype optimization. (3) The mechanism of influence of the learning load on investigated processes is also analyzed. It is shown that the learning load leads to a significant acceleration of the genetic assimilation.
genetic assimilation hiding effect learning load speed and efficiency of evolutionary search baldwin effect