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The role of speciation in spatial coevolutionary function approximation
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Genetic And Evolutionary Computation Conference archive
Proceedings of the 2007 GECCO conference companion on Genetic and evolutionary computation table of contents
London, United Kingdom
SESSION: Late-breaking papers table of contents
Pages 2437-2441  
Year of Publication: 2007
ISBN:978-1-59593-698-1
Authors
Folkert de Boer  Utrecht University, Utrecht, Netherlands
Paulien Hogeweg  Utrecht University, Utrecht, Netherlands
Sponsors
ACM: Association for Computing Machinery
SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation
Publisher
ACM  New York, NY, USA
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ABSTRACT

The role of space is more and more accepted as a way to dramatically improve the success of coevolutionary function approximation. The process behind this success however is not yet fully understood. It is suggested that spatiality causes a persistence in the population diversity over generations and a better targeting of weak points in the host-population by means of the parasite.

In this paper we will discuss the role of spatial pattern formation and speciation in coevolutonary function approximation and the influence on the success rate of coevolution.

We observe specific patterns of speciation in the problems as well in the problem solving-population (LISP functions). These patterns depend on a combination of the functions and the fitness criteria. The success of the spatial coevolutionary process can be understood from the speciation patterns: only if the problems speciate such that 'easy ones' are first evaluated, the coevolutionary process is successful.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Pagie, L. & Mitchell, M. (2002). A comparison of evolutionary and coevolutionary search. International Journal of Computational Intelligence and Applications, 2(1), 53--69.
 
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Pagie, L. (1999); Coevolutionary dynamics: information integration, speciation, and red queen dynamics. In: Information integration in evolutionary processes, chap. 5. pp. 67--93.
 
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Juillé, H. and Pollack, J. B. (1998). Coevolving the 'ideal' trainer: Application to the discovery of cellular automata rules. In Genetic Programming 1998: Proceedings of the Third Annual Conference, San Francisco, CA. Morgan Kaufmann.
 
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Collaborative Colleagues:
Folkert de Boer: colleagues
Paulien Hogeweg: colleagues