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

Department of Electrical Engineering

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › 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 Proceedings (Industrial Electronics Conference)
Pages	1268-1272
Volume	2
DOIs	https://doi.org/10.1109/IECON.2004.1431758
Publication status	Published - 2004
Event	30th Annual Conference of IEEE Industrial Electronics Society (IECON 2004) - Busan, Korea, Republic of Duration: 2 Nov 2004 → 6 Nov 2004

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Name
Volume	2

Conference

Conference	30th Annual Conference of IEEE Industrial Electronics Society (IECON 2004)
Place	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",

language = "English",

volume = "2",

pages = "1268--1272",

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note = "30th Annual Conference of IEEE Industrial Electronics Society (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.

PY - 2004

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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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U2 - 10.1109/IECON.2004.1431758

DO - 10.1109/IECON.2004.1431758

M3 - RGC 32 - Refereed conference paper (with host publication)

VL - 2

SP - 1268

EP - 1272

BT - IECON Proceedings (Industrial Electronics Conference)

T2 - 30th Annual Conference of IEEE Industrial Electronics Society (IECON 2004)

Y2 - 2 November 2004 through 6 November 2004

ER -

Jumping-genes in evolutionary computing

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