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JACIII Vol.15 No.9 pp. 1300-1309
doi: 10.20965/jaciii.2011.p1300
(2011)

Paper:

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A Study on Computational Efficiency and Plasticity in Baldwinian Learning

Shu Liu and Hitoshi Iba

Department of Electrical Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan

Received:
May 30, 2011
Accepted:
September 19, 2011
Published:
November 20, 2011
Creative Commons License
Keywords:
Baldwinian evolution, efficiency, learning potential, plasticity
Abstract
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.
Cite this article as:
S. Liu and H. Iba, “A Study on Computational Efficiency and Plasticity in Baldwinian Learning,” J. Adv. Comput. Intell. Intell. Inform., Vol.15 No.9, pp. 1300-1309, 2011.
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