The Baldwin Effect Revisited: Three Steps Characterized by the Quantitative Evolution of Phenotypic Plasticity
An interaction between evolution and learning called the Baldwin effect has been known for a century, but it is still poorly appreciated. This paper reports on a computational approach focusing on the quantitative evolution of phenotypic plasticity in complex environment so as to investigate its benefit and cost. For this purpose, we investigate the evolution of connection weights in a neural network under the assumption of epistatic interactions. Phenotypic plasticity is introduced into our model, in which whether each connection weight is plastic or not is genetically defined and connection weights with plasticity can be adjusted by learning. The simulation results have clearly shown that the evolutionary scenario consists of three steps characterized by transitions of the phenotypic plasticity and phenotypic variation, in contrast with the standard interpretation of the Baldwin effect that consists of two steps. We also conceptualize this evolutionary scenario by using a hill-climbing image of a population on a fitness landscape.
KeywordsPhenotypic Variation Phenotypic Plasticity Epistatic Interaction Connection Weight Evolutionary Scenario
Unable to display preview. Download preview PDF.
- 2.Turney, P., Whitley, D., Anderson, R.W.: Evolution, Learning, and Instinct: 100 Years of the Baldwin Effect. Evolutionary Computation 4(3), 4–8 (1996)Google Scholar
- 5.Harvey, I.: The Puzzle of the Persistent Question Marks: A Case Study of Genetic Drift. In: Proceedings of the Fifth International Conference on Genetic Algorithms, pp. 15–22 (1993)Google Scholar
- 6.Watson, R.A., Pollack, J.B.: How Symbiosis Can Guide Evolution. In: Fifth European Conference on Artificial Life, pp. 29–38 (1999)Google Scholar
- 7.Arita, T., Suzuki, R.: Interactions between Learning and Evolution: The Outstanding Strategy Generated by the Baldwin Effect. In: Proceedings of Artificial Life VII, pp. 196–205 (2000)Google Scholar
- 8.Ackley, D., Littman, M.: Interaction between Learning and Evolution. In: Proceedings of Artificial Life II, pp. 487–509 (1991)Google Scholar
- 9.Parisi, D., Nolfi, S.: The Influence of Learning on Evolution. Adaptive Individuals in Evolving Populations, pp. 419–428 (1996)Google Scholar
- 10.Sasaki, T., Tokoro, M.: Evolving Learnable Neural Networks under Changing Environments with Various Rates of Inheritance of Acquired Characters: Comparison between Darwinian and Lamarckian Evolution. In: Artificial Life, vol. 5(3), pp. 203–223 (1999)Google Scholar
- 15.Provine, W.B.: Sewall Wright and Evolutionary Biology. The University of Chicago Press, Chicago (1986)Google Scholar