Abstract
In this paper, we study the relationship between learning and evolution in a simple abstract model, where neural networks capable of learning are evolved through genetic algorithms (GAs). The connective weights of individuals’ neural networks undergo modification, i.e., certain characters will be acquired, through their lifetime learning. By setting various rates for the heritability of acquired characters, which is a motive force of Lamarckian evolution, we observe adaptational processes of the populations over successive generations. Paying particular attention to behaviours under changing environments, we show the following results. The population with the lower rate of heritability not only shows more stable behaviour against environmental changes, but also maintains greater adaptability with respect to such changing environments. Consequently, the population with zero heritability, i.e., the Darwinian population, attains the highest level of adaptation toward dynamic environments.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
David H. Ackley and Michael L. Littman. Interactions between Learning and Evolution. In Artificial Life II, SFI Studies in the Sciences of Complexity, vol.X, pages 487–509. Addison-Wesley, 1992.
Helen G. Cobb and John J. Grefenstette. Genetic Algorithms for Tracking Changing Environments. In Proceedings of 5th International Conference on Genetic Algorithms and their applications (ICGA-93), pages 523–530, 1993.
John J. Grefenstette. Lamarckian Learning in Multi-agent Environments. In Proceedings of 4th International Conference on Genetic Algorithms and their applications (ICGA-91), pages 303–310, 1991.
G. E. Hinton and S. J. Nowlan. How Learning Can Guide Evolution. Complex Systems, 1:495–502, 1987.
Akira Imada and Keijiro Araki. Lamarckian evolution of associative memory. In Proceedings of 1996 IEEE The Third International Conference on Evolutionary Computation (ICEC-96), pages 676–680, 1996.
David J. Montana and Lawrence Davis. Training Feedforward Neural Networks Using Genetic Algorithms. In Proceedings of the 11th International Conference on Artificial Intelligence (IJCAI-89), pages 762–767, 1989.
Naoki Mori, Seiji Imanishi, Hajime Kita, and Yoshikazu Nishikawa. Adaptation to Changing Environments by Means of the Memory Based Thermodynamical Genetic Algorithm. In Proceedings of 7th International Conference on Genetic Algorithms and their applications (ICGA-97), pages 299–306, 1997.
Stefano Nolfi and Domenico Parisi. Learning to adapt to changing environments in evolving neural networks. Technical Report 95-15, Institute of Psychology, National Research Council, Rome, 1995.
Domenico Parisi, Stefano Nolfi, and Federico Cecconi. Learning, Behaviour and Evolution. In Toward a Practice of Autonomous Systems: Proceedings of the First European Conference on Artificial Life, pages 207–216. MIT Press, 1991.
Takahiro Sasaki and Mario Tokoro. Adaptation toward changing environments: Why Darwinian in nature? In 4th European Conference on Artificial Life (ECAL-97), pages 145–153, 1997.
John Maynard Smith. Evolutionary Genetics. Oxford University Press, 1989.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1999 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sasaki, T., Tokoro, M. (1999). Adaptation under Changing Environments with Various Rates of Inheritance of Acquired Characters. In: McKay, B., Yao, X., Newton, C.S., Kim, JH., Furuhashi, T. (eds) Simulated Evolution and Learning. SEAL 1998. Lecture Notes in Computer Science(), vol 1585. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48873-1_6
Download citation
DOI: https://doi.org/10.1007/3-540-48873-1_6
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-65907-5
Online ISBN: 978-3-540-48873-6
eBook Packages: Springer Book Archive