Population based incremental learning with guided mutation versus genetic algorithms: Iterated prisoners dilemma

Timothy Gosling; Nanlin Jin; Edward Tsang

Population based incremental learning with guided mutation versus genetic algorithms: Iterated prisoners dilemma

Timothy Gosling^*, Nanlin Jin, Edward Tsang

^*Corresponding author for this work

University of Essex

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

8 Citations (Scopus)

Abstract

Axelrod's original experiments for evolving IPD player strategies involved the use of a basic GA. In this paper we examine how well a simple GA performs against the more recent Population Based Incremental Learning system under similar conditions. We find that GA performs slightly better than standard PBIL under most conditions. This differnce in performance can be mitigated and reversed through the use of a 'guided' mutation operator.

Original language	English
Title of host publication	2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings
Pages	958-965
Number of pages	8
Publication status	Published - 2005
Externally published	Yes
Event	2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005 - Edinburgh, Scotland, United Kingdom Duration: 2 Sept 2005 → 5 Sept 2005

Publication series

Name	2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings
Volume	1

Conference

Conference	2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005
Country/Territory	United Kingdom
City	Edinburgh, Scotland
Period	2/09/05 → 5/09/05

Cite this

Gosling, T., Jin, N., & Tsang, E. (2005). Population based incremental learning with guided mutation versus genetic algorithms: Iterated prisoners dilemma. In 2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings (pp. 958-965). (2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings; Vol. 1).

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title = "Population based incremental learning with guided mutation versus genetic algorithms: Iterated prisoners dilemma",

abstract = "Axelrod's original experiments for evolving IPD player strategies involved the use of a basic GA. In this paper we examine how well a simple GA performs against the more recent Population Based Incremental Learning system under similar conditions. We find that GA performs slightly better than standard PBIL under most conditions. This differnce in performance can be mitigated and reversed through the use of a 'guided' mutation operator.",

author = "Timothy Gosling and Nanlin Jin and Edward Tsang",

note = "Funding Information: This study was supported by the grant from Samuel Waxman Cancer Research Foundation Tumor Dormancy Program, NIH/National Cancer Institute (CA109182, CA163131), NIEHS (ES017146) and NYSTEM to JAA-G.; 2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005 ; Conference date: 02-09-2005 Through 05-09-2005",

year = "2005",

language = "English",

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Gosling, T, Jin, N & Tsang, E 2005, Population based incremental learning with guided mutation versus genetic algorithms: Iterated prisoners dilemma. in 2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings. 2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings, vol. 1, pp. 958-965, 2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005, Edinburgh, Scotland, United Kingdom, 2/09/05.

Population based incremental learning with guided mutation versus genetic algorithms: Iterated prisoners dilemma. / Gosling, Timothy; Jin, Nanlin; Tsang, Edward.
2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings. 2005. p. 958-965 (2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings; Vol. 1).

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

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Population based incremental learning with guided mutation versus genetic algorithms: Iterated prisoners dilemma

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Publication series

Conference

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