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Fine-grained population diversity estimation for genetic programming based structure identification

Authors:

Stephan M. Winkler,

Michael Affenzeller,

Stefan WagnerAuthors Info & Claims

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

Pages 1435 - 1436

https://doi.org/10.1145/1389095.1389376

Published: 12 July 2008 Publication History

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Abstract

We here describe a novel formalism for estimating the structural similarity of formulas that are evolved by a genetic programming (GP) based identification process. This method takes into account several aspects of structure tree comparison that are particularly important in the context of evolutionary system identification; this similarity measure is used for measuring the genetic diversity among GP populations.

Reference

[1]

E. Burke, S. Gustafson, and G. Kendall.A survey and analysis of diversity measures in genetic programming. In GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference, pages 716--723, New York, 2002. Morgan Kaufmann Publishers.

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Ferariu LPatelli A(2012)Genetic Programming for System IdentificationFormal and Practical Aspects of Autonomic Computing and Networking10.4018/978-1-60960-845-3.ch006(135-168)Online publication date: 2012
https://doi.org/10.4018/978-1-60960-845-3.ch006
Winkler SAffenzeller MWagner SLanzi P(2011)Analysis of the effects of enhanced selection concepts for genetic programming based structure identification using fine-grained population diversity estimationProceedings of the 13th annual conference companion on Genetic and evolutionary computation10.1145/2001858.2001967(195-196)Online publication date: 12-Jul-2011
https://dl.acm.org/doi/10.1145/2001858.2001967

Index Terms

Fine-grained population diversity estimation for genetic programming based structure identification
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Heuristic function construction
2. Software and its engineering
  1. Software creation and management
    1. Software verification and validation
      1. Process validation

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GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

July 2008

1814 pages

ISBN:9781605581309

DOI:10.1145/1389095

Conference Chair:
Conor Ryan
University of Limerick, Ireland
,
Editor:
Maarten Keijzer
Chordiant Software International B.V., The Netherlands

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

New York, NY, United States

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Published: 12 July 2008

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GECCO08: Genetic and Evolutionary Computation Conference

July 12 - 16, 2008

GA, Atlanta, USA

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Ferariu LPatelli A(2012)Genetic Programming for System IdentificationFormal and Practical Aspects of Autonomic Computing and Networking10.4018/978-1-60960-845-3.ch006(135-168)Online publication date: 2012
https://doi.org/10.4018/978-1-60960-845-3.ch006
Winkler SAffenzeller MWagner SLanzi P(2011)Analysis of the effects of enhanced selection concepts for genetic programming based structure identification using fine-grained population diversity estimationProceedings of the 13th annual conference companion on Genetic and evolutionary computation10.1145/2001858.2001967(195-196)Online publication date: 12-Jul-2011
https://dl.acm.org/doi/10.1145/2001858.2001967

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Analysis of the effects of enhanced selection concepts for genetic programming based structure identification using fine-grained population diversity estimation

An Efficient Structural Diversity Technique for Genetic Programming

A dual-population genetic algorithm for adaptive diversity control

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