Variable size population for dynamic optimization with genetic programming

Leonardo Vanneschi

doi:10.1145/1569901.1570222

Variable size population for dynamic optimization with genetic programming

Leonardo Vanneschi

NOVA Information Management School (NOVA IMS)

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

6 Citations (Scopus)

Abstract

A new model of Genetic Programming with variable size population is presented in this paper and applied to the reconstruction of target functions in dynamic environments (i.e. problems where target functions change with time). The suitability of this model is tested on a set of benchmarks based on some well known symbolic regression problems. Experimental results confirm that our variable size population model finds solutions of the same quality as the ones found by standard Genetic Programming, but with a smaller amount of computational effort.

Original language	Unknown
Title of host publication	Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Editors	Raidl Gea(
Publisher	ACM - Association for Computing Machinery
Pages	1895–1896
DOIs	https://doi.org/10.1145/1569901.1570222
Publication status	Published - 1 Jan 2009
Event	GECCO ’09 - Duration: 1 Jan 2009 → …

Conference

Conference	GECCO ’09
Period	1/01/09 → …

Access to Document

10.1145/1569901.1570222

Cite this

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title = "Variable size population for dynamic optimization with genetic programming",

abstract = "A new model of Genetic Programming with variable size population is presented in this paper and applied to the reconstruction of target functions in dynamic environments (i.e. problems where target functions change with time). The suitability of this model is tested on a set of benchmarks based on some well known symbolic regression problems. Experimental results confirm that our variable size population model finds solutions of the same quality as the ones found by standard Genetic Programming, but with a smaller amount of computational effort.",

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