On the evolutionary behavior of genetic programming with constants optimization

Bogdan Burlacu; Michael Affenzeller; Michael Kommenda

doi:10.1007/978-3-642-53856-8_36

On the evolutionary behavior of genetic programming with constants optimization

Bogdan Burlacu, Michael Affenzeller, Michael Kommenda

Research output: Chapter in Book/Report/Conference proceedings › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

Evolutionary systems are characterized by two seemingly contradictory properties: robustness and evolvability. Robustness is generally defined as an organism's ability to withstand genetic perturbation while maintaining its phenotype. Evolvability, as an organism's ability to produce useful variation. In genetic programming, the relationship between the two, mediated by selection and variation-producing operators (recombination and mutation), makes it difficult to understand the behavior and evolutionary dynamics of the search process. In this paper, we show that a local gradient-based constants optimization step can improve the overall population evolvability by inducing a beneficial structure-preserving bias on selection, which in the long term helps the process maintain diversity and produce better solutions.

Original language	English
Title of host publication	Computer Aided Systems Theory, EUROCAST 2013 - 14th International Conference, Revised Selected Papers
Publisher	Springer
Pages	284-291
Number of pages	8
Edition	PART 1
ISBN (Print)	9783642538551
DOIs	https://doi.org/10.1007/978-3-642-53856-8_36
Publication status	Published - 2013
Event	14th International Conference on Computer Aided Systems Theory, Eurocast 2013 - Las Palmas de Gran Canaria, Spain Duration: 10 Feb 2013 → 15 Feb 2013

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 1
Volume	8111 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	14th International Conference on Computer Aided Systems Theory, Eurocast 2013
Country/Territory	Spain
City	Las Palmas de Gran Canaria
Period	10.02.2013 → 15.02.2013

Keywords

Algorithm Analysis
Constant Optimization
Evolutionary Behavior
Genetic Programming
Symbolic Regression

Access to Document

10.1007/978-3-642-53856-8_36

Cite this

Burlacu, B., Affenzeller, M., & Kommenda, M. (2013). On the evolutionary behavior of genetic programming with constants optimization. In Computer Aided Systems Theory, EUROCAST 2013 - 14th International Conference, Revised Selected Papers (PART 1 ed., pp. 284-291). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8111 LNCS, No. PART 1). Springer. https://doi.org/10.1007/978-3-642-53856-8_36

Burlacu, Bogdan ; Affenzeller, Michael ; Kommenda, Michael. / On the evolutionary behavior of genetic programming with constants optimization. Computer Aided Systems Theory, EUROCAST 2013 - 14th International Conference, Revised Selected Papers. PART 1. ed. Springer, 2013. pp. 284-291 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).

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Burlacu, B , Affenzeller, M & Kommenda, M 2013, On the evolutionary behavior of genetic programming with constants optimization. in Computer Aided Systems Theory, EUROCAST 2013 - 14th International Conference, Revised Selected Papers. PART 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 8111 LNCS, Springer, pp. 284-291, 14th International Conference on Computer Aided Systems Theory, Eurocast 2013, Las Palmas de Gran Canaria, Spain, 10.02.2013. https://doi.org/10.1007/978-3-642-53856-8_36

On the evolutionary behavior of genetic programming with constants optimization. / Burlacu, Bogdan ; Affenzeller, Michael; Kommenda, Michael.
Computer Aided Systems Theory, EUROCAST 2013 - 14th International Conference, Revised Selected Papers. PART 1. ed. Springer, 2013. p. 284-291 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8111 LNCS, No. PART 1).

Research output: Chapter in Book/Report/Conference proceedings › Conference contribution › peer-review

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AB - Evolutionary systems are characterized by two seemingly contradictory properties: robustness and evolvability. Robustness is generally defined as an organism's ability to withstand genetic perturbation while maintaining its phenotype. Evolvability, as an organism's ability to produce useful variation. In genetic programming, the relationship between the two, mediated by selection and variation-producing operators (recombination and mutation), makes it difficult to understand the behavior and evolutionary dynamics of the search process. In this paper, we show that a local gradient-based constants optimization step can improve the overall population evolvability by inducing a beneficial structure-preserving bias on selection, which in the long term helps the process maintain diversity and produce better solutions.

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KW - Symbolic Regression

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Burlacu B , Affenzeller M, Kommenda M. On the evolutionary behavior of genetic programming with constants optimization. In Computer Aided Systems Theory, EUROCAST 2013 - 14th International Conference, Revised Selected Papers. PART 1 ed. Springer. 2013. p. 284-291. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). doi: 10.1007/978-3-642-53856-8_36

On the evolutionary behavior of genetic programming with constants optimization

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