Introducing the Swingometer Crossover and Mutation Operators for FloatingPoint Encoded Genetic Algorithms

Shane Lee; Hefin Rowlands

Introducing the Swingometer Crossover and Mutation Operators for FloatingPoint Encoded Genetic Algorithms

Shane Lee, Hefin Rowlands

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

Abstract

Genetic algorithms that utilise floating pointencoded chromosomes instead of the traditional binary or Gary code have become wide spread in recent years. This paper introduces new floatingpoint crossover and mutation operators for such genetic algorithms. The operators are derived from examining the implicit constraints that traditional binary crossover and mutation operators impose on the values of the parameters being affected. It is shown by an example that a genetic algorithm using these operators does converge and they should be considered for general use in floating-point genetic algorithms.

Original language	English
Title of host publication	Proceedings 19th European Conference on Modelling and Simulation
Editors	Yuri Merkuryev, Richard Zobel, Eugene Kerckhoffs
Publisher	ECMS
Number of pages	5
ISBN (Print)	1-84233-112-4
Publication status	Published - 2005
Event	19th European Conference on Modelling and Simulation - Riga, Latvia Duration: 1 Jun 2005 → 4 Jun 2005

Conference

Conference	19th European Conference on Modelling and Simulation
Abbreviated title	ECMS 2005
Country/Territory	Latvia
City	Riga
Period	1/06/05 → 4/06/05

Cite this

@inproceedings{e5c2f3b29a7b4e17bdd90bb944834ef9,

title = "Introducing the Swingometer Crossover and Mutation Operators for FloatingPoint Encoded Genetic Algorithms",

abstract = "Genetic algorithms that utilise floating pointencoded chromosomes instead of the traditional binary or Gary code have become wide spread in recent years. This paper introduces new floatingpoint crossover and mutation operators for such genetic algorithms. The operators are derived from examining the implicit constraints that traditional binary crossover and mutation operators impose on the values of the parameters being affected. It is shown by an example that a genetic algorithm using these operators does converge and they should be considered for general use in floating-point genetic algorithms.",

author = "Shane Lee and Hefin Rowlands",

year = "2005",

language = "English",

isbn = "1-84233-112-4",

editor = "Yuri Merkuryev and Richard Zobel and Eugene Kerckhoffs",

booktitle = "Proceedings 19th European Conference on Modelling and Simulation",

publisher = "ECMS",

note = "19th European Conference on Modelling and Simulation, ECMS 2005 ; Conference date: 01-06-2005 Through 04-06-2005",

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Introducing the Swingometer Crossover and Mutation Operators for FloatingPoint Encoded Genetic Algorithms. / Lee, Shane; Rowlands, Hefin.
Proceedings 19th European Conference on Modelling and Simulation . ed. / Yuri Merkuryev; Richard Zobel; Eugene Kerckhoffs. ECMS, 2005.

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

TY - GEN

T1 - Introducing the Swingometer Crossover and Mutation Operators for FloatingPoint Encoded Genetic Algorithms

AU - Lee, Shane

AU - Rowlands, Hefin

PY - 2005

Y1 - 2005

N2 - Genetic algorithms that utilise floating pointencoded chromosomes instead of the traditional binary or Gary code have become wide spread in recent years. This paper introduces new floatingpoint crossover and mutation operators for such genetic algorithms. The operators are derived from examining the implicit constraints that traditional binary crossover and mutation operators impose on the values of the parameters being affected. It is shown by an example that a genetic algorithm using these operators does converge and they should be considered for general use in floating-point genetic algorithms.

AB - Genetic algorithms that utilise floating pointencoded chromosomes instead of the traditional binary or Gary code have become wide spread in recent years. This paper introduces new floatingpoint crossover and mutation operators for such genetic algorithms. The operators are derived from examining the implicit constraints that traditional binary crossover and mutation operators impose on the values of the parameters being affected. It is shown by an example that a genetic algorithm using these operators does converge and they should be considered for general use in floating-point genetic algorithms.

M3 - Conference contribution

SN - 1-84233-112-4

BT - Proceedings 19th European Conference on Modelling and Simulation

A2 - Merkuryev, Yuri

A2 - Zobel, Richard

A2 - Kerckhoffs, Eugene

PB - ECMS

T2 - 19th European Conference on Modelling and Simulation

Y2 - 1 June 2005 through 4 June 2005

ER -

Introducing the Swingometer Crossover and Mutation Operators for FloatingPoint Encoded Genetic Algorithms

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