Multi-chromosomal genetic programming

Rachel Cavill; Steve Smith; Andy Tyrrell

Multi-chromosomal genetic programming

Rachel Cavill, Steve Smith, Andy Tyrrell

Electronic Engineering

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

Abstract

This paper introduces an evolutionary algorithm which uses multiple chromosomes to evolve solutions to a symbolic regression problem. Inspiration for this algorithm is provided by the existence of multiple chromosomes in natural evolution, particularly in plants. A multi-chromosomal system usually requires a dominance system and subsequently dominance in nature and in previous artificial evolutionary systems has also been considered. An implementation of a multi-chromosomal system is presented with initial results which support the use of multi-chromosomal techniques in evolutionary algorithms.

Original language	English
Title of host publication	GECCO 2005: Genetic and Evolutionary Computation Conference, Vols 1 and 2
Editors	HG Beyer
Place of Publication	NEW YORK
Publisher	ACM
Pages	1753-1759
Number of pages	7
ISBN (Print)	1-59593-010-8
Publication status	Published - 2005
Event	Genetic and Evolutionary Computation Conference - Washington Duration: 25 Jun 2005 → 29 Jun 2005

Conference

Conference	Genetic and Evolutionary Computation Conference
City	Washington
Period	25/06/05 → 29/06/05

Keywords

algorithms performance design
genetic programming
representations
team evolution
EVOLUTION

Cite this

@inproceedings{8bcd02ec08ac4d1c9f357275682804f8,

title = "Multi-chromosomal genetic programming",

abstract = "This paper introduces an evolutionary algorithm which uses multiple chromosomes to evolve solutions to a symbolic regression problem. Inspiration for this algorithm is provided by the existence of multiple chromosomes in natural evolution, particularly in plants. A multi-chromosomal system usually requires a dominance system and subsequently dominance in nature and in previous artificial evolutionary systems has also been considered. An implementation of a multi-chromosomal system is presented with initial results which support the use of multi-chromosomal techniques in evolutionary algorithms.",

keywords = "algorithms performance design, genetic programming, representations, team evolution, EVOLUTION",

author = "Rachel Cavill and Steve Smith and Andy Tyrrell",

year = "2005",

language = "English",

isbn = "1-59593-010-8",

pages = "1753--1759",

editor = "HG Beyer",

booktitle = "GECCO 2005: Genetic and Evolutionary Computation Conference, Vols 1 and 2",

publisher = "ACM",

address = "United States",

note = "Genetic and Evolutionary Computation Conference ; Conference date: 25-06-2005 Through 29-06-2005",

}

TY - GEN

T1 - Multi-chromosomal genetic programming

AU - Cavill, Rachel

AU - Smith, Steve

AU - Tyrrell, Andy

PY - 2005

Y1 - 2005

N2 - This paper introduces an evolutionary algorithm which uses multiple chromosomes to evolve solutions to a symbolic regression problem. Inspiration for this algorithm is provided by the existence of multiple chromosomes in natural evolution, particularly in plants. A multi-chromosomal system usually requires a dominance system and subsequently dominance in nature and in previous artificial evolutionary systems has also been considered. An implementation of a multi-chromosomal system is presented with initial results which support the use of multi-chromosomal techniques in evolutionary algorithms.

AB - This paper introduces an evolutionary algorithm which uses multiple chromosomes to evolve solutions to a symbolic regression problem. Inspiration for this algorithm is provided by the existence of multiple chromosomes in natural evolution, particularly in plants. A multi-chromosomal system usually requires a dominance system and subsequently dominance in nature and in previous artificial evolutionary systems has also been considered. An implementation of a multi-chromosomal system is presented with initial results which support the use of multi-chromosomal techniques in evolutionary algorithms.

KW - algorithms performance design

KW - genetic programming

KW - representations

KW - team evolution

KW - EVOLUTION

M3 - Conference contribution

SN - 1-59593-010-8

SP - 1753

EP - 1759

BT - GECCO 2005: Genetic and Evolutionary Computation Conference, Vols 1 and 2

A2 - Beyer, HG

PB - ACM

CY - NEW YORK

T2 - Genetic and Evolutionary Computation Conference

Y2 - 25 June 2005 through 29 June 2005

ER -