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Lessons learned in evolutionary computation: 11 steps to success

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GECCO '09: Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers

Pages 2657 - 2660

https://doi.org/10.1145/1570256.1570377

Published: 08 July 2009 Publication History

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Abstract

Everybody makes mistakes -- we all make one eventually if we just work hard enough! This is good news and bad news. We learn from mistakes but mistakes are also painful and could turn out to be costly in terms of money, reputation and credibility. One is prone to make mistakes particularly with new and complex techniques with unknown or not exactly known properties. This paper talks about some of my more unfortunate experiences with evolutionary computation. The paper covers design and application mistakes as well as misperceptions in academia and industry. You can make a lot of technical mistakes in evolutionary computation. However, technical errors can be detected and rectified. Algorithms are implemented, presented and analysed by humans who also discuss and measure the impact of algorithms from their very individual perspectives. A lot of 'bugs' are actually not of a technical nature, but are human flaws. This text tries also to touch on these 'soft' aspects of evolutionary computing.

References

[1]

D. Adams. The Hitchhiker's Guide To The Galaxy. Pan Macmillan, 1979.

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[2]

T. Bartz-Beielstein. Experimental Research in Evolutionary Computation: The New Experimentalism. Springer, 2006.

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[3]

J. Mehnen and R. Roy. Technology Transfer: Academia to Industry. In: Studies in Computational Intelligence, chapter 12, pages 263--281. Springer, London, New York, 1988.

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K. Weinert, R. Keller, J. Mehnen, and W. Banzhaf. Surface reconstruction from 3D point data with a genetic programming/evolution strategy hybrid. In: Advances in Genetic Programming 3, chapter 3, pages 41--66. MIT Press, Cambridge, MA, 1999.

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[5]

K. Weinert, J. Mehnen, and M. Schneider. Evolutionary Optimization of Approximating Triangulations for Surface Reconstruction from Unstructured 3D Data In: Information Processing with Evolutionary Algorithms, From Industrial Applications to Academic Speculations, chapter 3, pages 30--42. Springer, London, New York, 2005.

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Index Terms

Lessons learned in evolutionary computation: 11 steps to success
1. Applied computing
2. Information systems
  1. Information systems applications

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GECCO '09: Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers

July 2009

1760 pages

ISBN:9781605585055

DOI:10.1145/1570256

General Chair:
Franz Rothlauf
University of Mainz, Germany

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Published: 08 July 2009

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GECCO09: Genetic and Evolutionary Computation Conference

July 8 - 12, 2009

Québec, Montreal, Canada

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Leverage Your Lessons

Lessons Learned: Architects Are Facilitators, Too!

Analysis of evolutionary multi-tasking as an island model

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