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
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.
-  D. Simon, “Biogeography-Based Optimization,” IEEE Trans. on Evolutionary Computation, Vol.12, No.6, pp. 702-713, 2008.
-  N. K. Bambha, S. S. Bhattacharyya, J. Teich, and E. Zitzler, “Systematic integration of parameterized local search in evolutionary algorithm,” IEEE Trans. on Evolutionary Computation, Vol.8, No.2, pp. 137-155, 2004.
-  D. E. Goldberg and S. Voessner, “Optimizing global-local search hybrids,” In Proc. of the Genetic and Evolutionary Computation Conference (GECCO), pp. 220-228, 1999.
-  N. Krasnogor and J. Smith, “A memetic algorithm with selfadaptive local search: TSP as a case study,” In Proc. of the Genetic and Evolutionary Computation Conference (GECCO), pp. 987-994, 2000.
-  K. W. C. Ku, M. W. Mak, and W. C. Siu, “A study of the Lamarckian evolution of recurrent neural networks,” IEEE Trans. on Evolutionary Computation, Vol.4, No.1, pp. 31-42, 2000.
-  Q. Nguyen, Y. Ong, and N. Krasnogor, “A study on the design issues of Memetic Algorithm,” In Proc. of 2007 IEEE Congress on Evolutionary Computation, pp. 2390-2397, 2007.
-  N. Krasnogor and J. Smith, “A tutorial for competent memetic algorithms: Model, taxonomy, and design issues,” IEEE Trans. on Evolutionary Computation, Vol.9, No.5, pp. 474-488, 2005.
-  Q. H. Nguyen, Y.-S. Ong, and M. H. Lim, “A Probabilistic Memetic Framework,” IEEE Trans. on Evolutionary Computation, Vol.13, No.3, pp. 604-623, 2009.
-  N. Noman and H. Iba, “Accelerating differential evolution using an adaptive local search,” IEEE Trans. on Evolutionary Computation, 2008.
-  Y.-S. Ong, M.-H. Lim, N. Zhu, and K.-W. Wong, “Classification of adaptive memetic algorithms: a comparative study,” IEEE Trans. on Systems, Man, and Cybernetics, Part B, Vol.36, No.1, pp. 141-152, 2006.
-  J. M. Baldwin, “A new factor in evolution,” American Naturalist, Vol.30, pp. 441-451, 1896.
-  K. L. Downing, “Development and the Baldwin effect,” Artificial Life, Vol.10, No.1, pp. 39-63, 2004.
-  K. L. Downing, “Computational Explorations of the Baldwin Effect,” In Proc. of the First Norwegian Artificial Intelligence Symposium, pp. 41-52, 2009.
-  K. L. Downing, “The Baldwin Effect in Developing Neural Networks,” In Proc. of the Genetic and Evolutionary Computation Conference, pp. 555-562, 2010.
-  G. Hinton and S. Nowlan, “How learning can guide evolution,” Complex Systems, Vol.1, pp. 495-502, 1987.
-  I. Paenke, T. J. Kawecki, and B. Sendhoff, “The Influence of Learning on Evolution: A Mathematical Framework,” Artificial Life, Vol.15, No.2, pp. 227-245, 2009.
-  R. Suzuki and T. Arita, “The Dynamic Changes in Roles of Learning through the Baldwin effect,” Artificial Life, Vol.13, No.1, pp. 31-43, 2007.
-  P. Turney, “Myths and legends of the Baldwin effect,” In Proc. of theWorkshop on Evolutionary Computing and Machine Learning at the 13th Int. Conf. on Machine Learning (ICML-96), pp. 135-142, 1996.
-  C. G. Carrier, “Unifying learning with evolution through Baldwinian evolution and Lamarckism: A case study,” In in Proc. Symp. Computational Intelligence and Learning (CoIL-2000), pp. 36-41, 2000.
-  K. L. Downing, “Designing neutral networks using genetic algorithms with graph generation system,” Genetic Programming and Evolvable Machines, Vol.2, No.3, pp. 259-288, 2001.
-  H. Ishibuchi, S. Kaige, and K. Narukawa, “Comparison between Lamarckian and Baldwinian repair on multiobjective 0/1 knapsack problems,” In Proc. of Third Int. Conf. on Evolutionary Multi-Criterion Optimization, EMO 2005, ser., Lecture Notes in Computer Science, pp. 370-385, Springer, 2005.
-  S. Kauffman, “The Origin of Order: Self-Organization and Selection in Evolution,” Oxford University Press, 1993.
-  G. Mayley, “Landscapes, Learning Costs and Genetic Assimilation,” Evolutionary Computation, Vol.4, pp. 213-234, 1996.
-  T. Sasaki and M. Tokoro, “Evolving Learnable Neural Networks under Changing Environments with Various Rates of Inheritance of Acquired Characters: Comparison between Darwinian and Lamarckian Evolution,” Artificial Life, Vol.5, No.3, pp. 203-223, 1999.
-  D. L. Whitley, V. S. Gordon, and K. E. Mathias, “Lamarckian Evolution, The Baldwin Effect and Function Optimization,” In Proc. of Int. Conf. on Parallel Problem Solving From Nature III (PPSN III), pp. 6-15, 1994.
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