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Baldwinian evolution is a hybridization of populationbased search and local refinements. Unlike in Lamarckian evolution, selection is made based on fitness improved in local refining, but refined traits are not known to offspring. For potential use in computational applications, we must investigate the Baldwinian evolutionmechanism, in term of computational cost and fitness improvement. In this paper, a set of experiments is presented, to find what is produced in Baldwinian learning. We found that, on the static landscapes involved, learning cost is paid to maintain a certain level of potential to reach good solutions, rather than to further explore on the landscape. Plasticity codes in genotypes can help in selecting appropriate parts to refine and improve search performance. However, this improvement remains limited because no learned traits are passed on, and does not enable exploration far beyond parents.

A Study on Computational Efficiency and Plasticity in Baldwinian Learning