Jumping-genes in evolutionary computing

K. F. MAN; T. M. CHAN; K. S. TANG; S. KWONG

doi:10.1109/IECON.2004.1431758

Jumping-genes in evolutionary computing

K. F. MAN, T. M. CHAN, K. S. TANG, S. KWONG

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

36 Citations (Scopus)

Abstract

A new Jumping Genes paradigm is proposed for evolutionary computing. It is speedy and yet capable of generating some extreme solutions within the Pareto front This algorithm emulates the genetic phenomenon of horizontal transmission in which the genes can be jumped from one position to another position either within its own or the other chromosome. This paper describes the concept, methodology and implementation of the algorithm that can be aptly used for evolutionary computing. The performance in simulation based on some standard test functions has demonstrated its effectiveness in multiobjective optimization and its results also have been compared with other algorithms currently in use. © 2004 IEEE.

Original language	English
Title of host publication	IECON 2004 : 30th Annual Conference of IEEE Industrial Electronics Society
Publisher	IEEE
Pages	1268-1272
Number of pages	5
Volume	2
ISBN (Print)	0780387309
DOIs	https://doi.org/10.1109/IECON.2004.1431758
Publication status	Published - 2004
Externally published	Yes
Event	30th Annual Conference of IEEE Industrial Electronics Society, 2004 - Busan, Korea, Republic of Duration: 2 Nov 2004 → 6 Nov 2004

Conference

Conference	30th Annual Conference of IEEE Industrial Electronics Society, 2004
Abbreviated title	IECON 2004
Country/Territory	Korea, Republic of
City	Busan
Period	2/11/04 → 6/11/04

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10.1109/IECON.2004.1431758

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title = "Jumping-genes in evolutionary computing",

abstract = "A new Jumping Genes paradigm is proposed for evolutionary computing. It is speedy and yet capable of generating some extreme solutions within the Pareto front This algorithm emulates the genetic phenomenon of horizontal transmission in which the genes can be jumped from one position to another position either within its own or the other chromosome. This paper describes the concept, methodology and implementation of the algorithm that can be aptly used for evolutionary computing. The performance in simulation based on some standard test functions has demonstrated its effectiveness in multiobjective optimization and its results also have been compared with other algorithms currently in use. {\textcopyright} 2004 IEEE.",

author = "MAN, {K. F.} and CHAN, {T. M.} and TANG, {K. S.} and S. KWONG",

year = "2004",

doi = "10.1109/IECON.2004.1431758",

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volume = "2",

pages = "1268--1272",

booktitle = "IECON 2004 : 30th Annual Conference of IEEE Industrial Electronics Society",

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note = "30th Annual Conference of IEEE Industrial Electronics Society, 2004, IECON 2004 ; Conference date: 02-11-2004 Through 06-11-2004",

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AU - MAN, K. F.

AU - CHAN, T. M.

AU - TANG, K. S.

AU - KWONG, S.

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N2 - A new Jumping Genes paradigm is proposed for evolutionary computing. It is speedy and yet capable of generating some extreme solutions within the Pareto front This algorithm emulates the genetic phenomenon of horizontal transmission in which the genes can be jumped from one position to another position either within its own or the other chromosome. This paper describes the concept, methodology and implementation of the algorithm that can be aptly used for evolutionary computing. The performance in simulation based on some standard test functions has demonstrated its effectiveness in multiobjective optimization and its results also have been compared with other algorithms currently in use. © 2004 IEEE.

AB - A new Jumping Genes paradigm is proposed for evolutionary computing. It is speedy and yet capable of generating some extreme solutions within the Pareto front This algorithm emulates the genetic phenomenon of horizontal transmission in which the genes can be jumped from one position to another position either within its own or the other chromosome. This paper describes the concept, methodology and implementation of the algorithm that can be aptly used for evolutionary computing. The performance in simulation based on some standard test functions has demonstrated its effectiveness in multiobjective optimization and its results also have been compared with other algorithms currently in use. © 2004 IEEE.

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DO - 10.1109/IECON.2004.1431758

M3 - Conference paper (refereed)

SN - 0780387309

VL - 2

SP - 1268

EP - 1272

BT - IECON 2004 : 30th Annual Conference of IEEE Industrial Electronics Society

PB - IEEE

T2 - 30th Annual Conference of IEEE Industrial Electronics Society, 2004

Y2 - 2 November 2004 through 6 November 2004

ER -

Jumping-genes in evolutionary computing

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