Abstract
Baldwinian evolution and Lamarckism hold that behaviour is not evolved solely through genetics, but also through learning. In Baldwinian evolution, learned behaviour causes changes only to the fitness landscape, whilst in Lamarckism, learned behaviour also causes changes to the parents’ genotypes. Although the biological plausibility of these positions remains arguable, they provide a potentially useful framework for the construction of artificial systems. As an example, we describe the use of Baldwinian and Lamarckian evolution in the design of the hidden layer of a RBF network.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Baldwin, J.M., 1896, “A New Factor in Evolution”, American Naturalist, 30:441–451.
Blake, C.L.; Merz, C.J., 1998, “UCI Repository of Machine Learning Databases”, University of California, Irvine/CA, Department of Information and Computer Sciences. (http://www.ics.uci.edu/~mleam/MLRepository.html/~mleam/MLRepository.html).
Burdsall, B.; Giraud-Carrier, C, 1997a, “Evolving Fuzzy Prototypes for Efficient Data Clustering”, Second International ICSC Symposium on Fuzzy Logic and Applications, Zürich, Switzerland, 217–223.
Burdsall, B.; Giraud-Carrier, C, 1997b, “GA-RBF: A Self-optimising RBF Network”, Third International Conference on Artificial Neural Networks and Genetic Algorithms, Norwich, UK, 346–349.
Darwin, C, 1859, “On The Origin Of Species By Means of Natural Selection, Or The Preservation of Favoured Races In The Struggle For Life”, London: John Murray.
Fogel, L.J., 1994, “Evolutionary Programming in Perspective: The Top Down View”, Computational Intelligence: Imitating Life, Zurada, J.M.; et al., (Eds.), IEEE Press, Inc., NY.
Goldberg, D.E., 1989, “Genetic Algorithms in Search, Optimization and Machine Learning”, Addison-Wesley.
Gruau, F.; Whitley, D., 1993, “Adding Learning to the Cellular Developmental Process: A Comparative Study”, Evolutionary Computing, 1(3):213–233.
Lamarck, J.B., 1815, “Histoire Naturelle des Animaux Sans Vertèbres”, (in French). Translated and published as “Zoological Philosophy: An Exposition with Regard to the Natural History of Animals”, 1984, University of Chicago Press, Chicago/IL, USA.
Michalewicz, Z., 1992, “Genetic Algorithms + Data Structures = Evolution Programs”, Springer-Verlag.
Mühlenbein, H.; Kinderman, J., 1989, “The Dynamics of Evolution and Learning — Toward Genetic Neural Networks”, Connectionism in Perspective, Pfeifer, R.; et al., (Eds.), Elsevier.
Neruda, R., 1995, “Functional Equivalence and Genetic Learning”, Proceedings of the First International Conference on Artificial Neural Networks and Genetic Algorithms, 53–56.
Pederson, K., 1996, “Evolving Neural Networks with Genetic Algorithms”, Project Report PR-96-33, University of Bristol, Department of Computer Science.
Schaffer, J.D.; Whitley, D; Eshelman, L.J., 1992, “Combinations of Genetic Algorithms and Neural Networks: A Survey of the State of the Art”, Proceedings of the Intemational Workshop on Combinations of Genetic Algorithms and Neural Networks, 1–37.
Simon, H., (1983), “Why Should Machines Leam?”, Machine Leaming: An Artificial Intelligence Approach, Vol. 1, Michalski, R.S.; Carbonell, J.G.; Mitchell, T.M., (Eds.), Tioga, Palo Alto/CA.
Whitley, D.; Gordon, V.S.; Mathias, K., 1994, “Lamarckian Evolution, The Baldwin Effect and Function Optimization”, Parallel Problem Solving from Nature-PPSN III, Davidor, Y.; Schwefel, H.P.; Manner, R., (Eds.), Springer-Veriag, 6–15.
Yao, X., 1993, “A Review of Evolutionary Artificial Neural Networks”, Intemational Joumal of Intelligent Systems, 8(4):539–567.
Yao, X.; Liu, Y., 1998, “Towards Designing Artificial Neural Networks by Evolution”, Applied Mathematics and Computation, 91(1):83–90.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2002 Springer Science+Business Media New York
About this chapter
Cite this chapter
Giraud-Carrier, C. (2002). Unifying Learning with Evolution Through Baldwinian Evolution and Lamarckism. In: Zimmermann, HJ., Tselentis, G., van Someren, M., Dounias, G. (eds) Advances in Computational Intelligence and Learning. International Series in Intelligent Technologies, vol 18. Springer, Dordrecht. https://doi.org/10.1007/978-94-010-0324-7_11
Download citation
DOI: https://doi.org/10.1007/978-94-010-0324-7_11
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-010-3872-0
Online ISBN: 978-94-010-0324-7
eBook Packages: Springer Book Archive