How do lifetime learning and evolution interact?

The impact of learning on the rate of evolution has been subject to significant debate and analysis for over a century. Much progress has been made in explaining how learning, as a non-inherited, highly-adaptive form of plasticity, affects evolutionary trajectories via genetic accommodation and assimilation. To date, most computational and mathematical models devoted to understanding the effect of learning on evolution only focus on how environmental perturbations impact the expression of a limited number of genes (the 'norm of reaction'). However, West-Eberhard suggests learning mediates selection pressures over many phenotypic traits thereby driving genetic correlations; these correlations, in turn, increase the effectiveness of the learning, which with circular reinforcement, increases the effectiveness of genetic evolution. This research aims to show that the interaction between the rapid fitness screening effects of learning and slowly evolving genetic correlations not only accelerates the action of evolution but also constrain the search of fitness landscapes in a way that enables the discovery of fitness optima that would not normally be found without learning. Using numerical simulations, this research seeks to demonstrate that the rapid screening effects of learning and the evolution of genetic correlations enables the discovery of high-fitness phenotypes that are effectively unevolvable otherwise. If successful, the results would support the emerging view of the phenotype as a vital actor in the process of evolution.