Model of Interaction between Learning and Evolution
This work addresses theoretical biology and evolutionary computation by exploring how learning influences evolutionary optimization, though it appears incremental in its modeling approach.
The study tackled the interaction between learning and evolution by modeling genetic assimilation and the hiding effect, showing that learning load can significantly accelerate evolution.
The model of interaction between learning and evolutionary optimization is designed and investigated. The evolving population of modeled organisms is considered. The mechanism of the genetic assimilation of the acquired features during a number of generations of Darwinian evolution is studied. It is shown that the genetic assimilation takes place as follows: phenotypes of modeled organisms move towards the optimum at learning; then the selection takes place; genotypes of selected organisms also move towards the optimum. The hiding effect is also studied; this effect means that strong learning can inhibit the evolutionary search for the optimal genotype. The mechanism of influence of the learning load on the interaction between learning and evolution is analyzed. It is shown that the learning load can lead to a significant acceleration of evolution.