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abstract: |-
This position paper argues that the Baldwin effect is widely
misunderstood by the evolutionary computation community. The
misunderstandings appear to fall into two general categories.
Firstly, it is commonly believed that the Baldwin effect is
concerned with the synergy that results when there is an evolving
population of learning individuals. This is only half of the story.
The full story is more complicated and more interesting. The Baldwin
effect is concerned with the costs and benefits of lifetime
learning by individuals in an evolving population. Several
researchers have focussed exclusively on the benefits, but there
is much to be gained from attention to the costs. This paper explains
the two sides of the story and enumerates ten of the costs and
benefits of lifetime learning by individuals in an evolving population.
Secondly, there is a cluster of misunderstandings about the relationship
between the Baldwin effect and Lamarckian inheritance of acquired
characteristics. The Baldwin effect is not Lamarckian. A Lamarckian
algorithm is not better for most evolutionary computing problems than
a Baldwinian algorithm. Finally, Lamarckian inheritance is not a
better model of memetic (cultural) evolution than the Baldwin effect.
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conference: Workshop on Evolutionary Computation and Machine Learning at the 13th International Conference on Machine Learning
confloc: 'Bari, Italy'
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pagerange: 135-142
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referencetext: |2
Ackley, D., and Littman, M. (1991). Interactions between learning and
evolution. In Proceedings of the Second Conference on Artificial Life, C.
Langton, C. Taylor, D. Farmer, and S. Rasmussen, editors. California:
Addison-Wesley.
Anderson, R.W. (1995a). Learning and evolution: A quantitative genetics
approach. Journal of Theoretical Biology, 175, 89-101.
Anderson, R.W. (1995b). Genetic mechanisms underlying the Baldwin
effect are evident in natural antibodies. In Evolutionary Programming IV:
The Edited Proceedings of the Fourth Annual Conference on Evolutionary
Programming, edited by J.R. McDonnell, R.G. Reynolds, and D.B.
Fogel, pp. 547-563. Cambridge, MA: MIT Press.
Balakrishnan, K., and Honavar, V. (1995). Evolutionary design of neural
architectures: A preliminary taxonomy and guide to literature. Artificial
Intelligence Research Group, Department of Computer Science, Iowa
State University, Technical Report CS TR #95-01.
Baldwin, J.M. (1896). A new factor in evolution. American Naturalist,
30, 441-451.
Barkow, J.H., Cosmides, L., and Tooby, J. (1992). Editors, The Adapted
Mind: Evolutionary Psychology and the Generation of Culture, New
York: Oxford University Press.
Behera, N., and Nanjundiah, V. (1995). An investigation into the role of
phenotypic plasticity in evolution. Journal of Theoretical Biology, 172,
225-234.
Belew, R.K. (1989). When both individuals and populations search:
Adding simple learning to the Genetic Algorithm. In Proceedings of the
Third International Conference on Genetic Algorithms, 34-41, Washington
D.C.
Belew, R.K. (1990). Evolution, learning and culture: computational
metaphors for adaptive search. Complex Systems, 4, 11-49.
Belew, R.K., McInerney, J., and Schraudolph, N.N. (1991). Evolving
networks: Using the Genetic Algorithm with connectionist learning. In
Proceedings of the Second Artificial Life Conference, 511-547, Addison-
Wesley.
Belew, R.K. and Mitchell, M., (1996). Editors, Adaptive Individuals in
Evolving Populations: Models and Algorithms, Massachusetts: Addison-
Wesley.
Burke, E.K., Newall, J.P., and Weare, R.F. (1995). A memetic algorithm
for university exam timetabling. Proceedings of the First International
Conference on the Practice and Theory of Automated Timetabling
(ICPTAT-95), 496-503. Napier University, Edinburgh.
Cecconi, F., Menczer, F., and Belew, R.K. (1995). Maturation and the
evolution of imitative learning in artificial organisms. Technical Report
CSE 506, University of California, San Diego, 1995; to appear in
Adaptive Behavior, 4, January 1996.
Cziko, G. (1995). Without Miracles: Universal Selection Theory and the
Second Darwinian Revolution. Massachusetts: MIT Press.
Dawkins, R. (1976). The Selfish Gene. Oxford: Oxford University Press.
Dawkins, R. (1982). The Extended Phenotype: The Gene as the Unit of
Natural Selection. San Francisco: Freeman.
French, R., and Messinger, A. (1994). Genes, phenes and the Baldwin
effect. In Rodney Brooks and Patricia Maes (editors), Artificial Life IV.
Cambridge MA: MIT Press.
Gottlieb, G. (1992). Evolution: The modern synthesis and its failure to
incorporate individual development into evolutionary theory. Chapter 11
of Individual Development and Evolution: The Genesis of Novel
Behavior. New York: Oxford University Press.
Gould, (1991). Bully for Brontosaurus: Reflections in Natural History.
New York: Norton.
Hart, W.E. (1994). Adaptive Global Optimization with Local Search.
Ph.D. Thesis, Department of Computer Science and Engineering, University
of California, San Diego.
Hart, W.E., and Belew, R.K. (1996). Optimization with genetic algorithm
hybrids that use local search. In R.K. Belew and M. Mitchell, (editors),
Adaptive Individuals in Evolving Populations: Models and Algorithms,
Addison-Wesley.
Hart, W.E., Kammeyer, T.E., and Belew, R.K. (1995). The role of development
in genetic algorithms. In L.D. Whitley and M.D. Vose, (editors),
Foundations of Genetic Algorithms 3, California: Morgan Kaufmann.
Harvey, I. (1993). The puzzle of the persistent question marks: A case
study of genetic drift. In S. Forrest (editor) Proceedings of the Fifth International
Conference on Genetic Algorithms, ICGA-93, California:
Morgan Kaufmann.
Haussler, D. (1988). Quantifying inductive bias: AI learning algorithms
and Valiant’s learning framework. Artificial Intelligence, 36, 177-221.
Hightower, R., Forrest, S., and Perelson, A. (1996) The Baldwin effect in
the immune system: learning by somatic hypermutation. In R.K. Belew
and M. Mitchell, (editors), Adaptive Individuals in Evolving Populations:
Models and Algorithms, Addison-Wesley.
Hinton, G.E., and Nowlan, S.J. (1987). How learning can guide evolution.
Complex Systems, 1, 495-502.
Maynard Smith, J. (1987). When learning guides evolution. Nature, 329,
761-762.
Menczer, F., and Belew, R.K. (1994). Evolving sensors in environments
of controlled complexity. In R. Brooks and P. Maes, (editors), Artificial
Life IV, MIT Press.
Morgan, C.L. (1896). On modification and variation. Science, 4, 733-
740.
Moscato, P. (1989). On evolution, search, optimization, genetic algorithms
and martial arts: Towards memetic algorithms. Technical Report
826, California Institute of Technology, Pasadena, California.
Moscato, P. (1993). An introduction to population approaches for optimization
and hierarchical objective functions: The role of tabu search.
Annals of Operations Research, 41, 85-121.
Moscato, P., and Fontanari, J.F. (1990). Stochastic versus deterministic
update in simulated annealing. Physics Letters A, 146, 204-208.
Moscato, P. and Norman, M.G. (1992). A memetic approach for the travelling
salesman problem. In Proceedings of the International Conference
on Parallel Computing and Transputer Applications, 177-186. Amsterdam:
IOS Press.
Nolfi, S., Elman, J.L., and Parisi, D. (1994). Learning and evolution in
neural networks, Adaptive Behavior, 3, 5-28.
Norman, M.G., and Moscato, P. (1989). A competitive-cooperative
approach to complex combinatorial search. In Selected Work for the Proceedings
of the 20th Joint Conference on Informatics and Operations
Research, 3.15-3.29, Buenos Aires, Argentina.
Osborn, H.F. (1896). Ontogenic and phylogenic variation. Science, 4,
786-789.
Paechter, B., Cumming, A., Norman, M.G., and Luchian, H. (1995).
Extensions to a memetic timetabling system. Proceedings of the First
International Conference on the Practice and Theory of Automated Timetabling
(ICPTAT-95), 455-467. Napier University, Edinburgh.
Pinker, S. (1994). The Language Instinct: How the Mind Creates
Language, New York: William Morrow and Co.
Radcliffe, N.J., and Surry, P.D., (1994). Formal memetic algorithms. Proceedings
of the AISB Workshop on Evolutionary Computing. Berlin:
Springer-Verlag.
Rendell, L. (1986). A general framework for induction and a study of
selective induction. Machine Learning, 1, 177-226.
Scheiner, S. (1993). Genetics and evolution of phenotypic plasticity.
Annual Review of Ecology and Systematics, 24, 35-68.
Simpson, G.C. (1953). The Baldwin effect. Evolution, 7, 110-117.
Sober, E. (1994) The adaptive advantage of learning and a priori prejudice.
In From a Biological Point of View: Essays in Evolutionary Philosophy,
a collection of essays by E. Sober, 50-70, Cambridge University
Press.
Turney, P.D. (1995). Cost-sensitive classification: Empirical evaluation
of a hybrid genetic decision tree induction algorithm. Journal for AI
Research, 2, 369-409.
Unemi, T., Nagayoshi, M., Hirayama, N., Nade, T., Yano, K., and
Masujima, Y. (1994). Evolutionary differentiation of learning abilities - a
case study on optimizing parameter values in Q-learning by a genetic
algorithm, In Rodney Brooks and Patricia Maes (editors), Artificial Life
IV. Cambridge MA: MIT Press.
Utgoff, P. (1986). Shift of bias for inductive concept learning. In Machine
Learning: An Artificial Intelligence Approach, Volume II. Edited by R.S.
Michalski, J.G. Carbonell, and T.M. Mitchell. California: Morgan
Kaufmann.
Waddington, C.H. (1942). Canalization of development and the inheritance
of acquired characters. Nature, 150, 563-565.
Wcislo, W.T. (1989). Behavioral environments and evolutionary change.
Annual Review of Ecology and Systematics, 20, 137-169.
West-Eberhard, M. (1989). Phenotypic plasticity and the origins of diversity.
Annual Review of Ecology and Systematics, 20, 249-278.
Whitley, D., and Gruau, F. (1993). Adding learning to the cellular development
of neural networks: Evolution and the Baldwin effect. Evolutionary
Computation, 1, 213-233.
Whitley, D., Gordon, S., and Mathias, K. (1994). Lamarckian evolution,
the Baldwin effect and function optimization. Parallel Problem Solving
from Nature - PPSN III. In Y. Davidor, H.P. Schwefel, and R. Manner,
editors, pp. 6-15. Berlin: Springer-Verlag.
relation_type: []
relation_uri: []
reportno: ~
rev_number: 12
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status_changed: 2007-09-12 16:47:20
subjects:
- comp-sci-mach-learn
- bio-evo
succeeds: ~
suggestions: ~
sword_depositor: ~
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thesistype: ~
title: Myths and Legends of the Baldwin Effect
type: confpaper
userid: 2175
volume: ~