A hybrid genetic algorithm for a type of nonlinear programming problem

Jiafu Tang; Dingwei Wang; A. Ip; R. Y K Fung

doi:10.1016/S0898-1221(98)00146-1

A hybrid genetic algorithm for a type of nonlinear programming problem

Jiafu Tang
, Dingwei Wang
, A. Ip
, R. Y K Fung

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

43 Citations (Scopus)

Abstract

Based on the introduction of some new concepts of semifeasible direction, Feasible Degree (FD₁) of semifeasible direction, feasible degree (FD₂) of illegal points 'belonging to' feasible domain, etc., this paper proposed a new fuzzy method for formulating and evaluating illegal points and three new kinds of evaluation functions and developed a special Hybrid Genetic Algorithm (HGA) with penalty function and gradient direction search for nonlinear programming problems. It uses mutation along the weighted gradient direction as its main operator and uses arithmetic combinatorial crossover only in the later generation process. Simulation of some examples show that this method is effective. © 1998 Elsevier Science Ltd. All rights reserved.

Original language	English
Pages (from-to)	11-21
Journal	Computers and Mathematics with Applications
Volume	36
Issue number	5
DOIs	https://doi.org/10.1016/S0898-1221(98)00146-1
Publication status	Published - Sept 1998

Research Keywords

Feasible degree
Hybrid genetic algorithm
Nonlinear programming
Semifeasible direction
Weighted gradient direction

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title = "A hybrid genetic algorithm for a type of nonlinear programming problem",

abstract = "Based on the introduction of some new concepts of semifeasible direction, Feasible Degree (FD1) of semifeasible direction, feasible degree (FD2) of illegal points 'belonging to' feasible domain, etc., this paper proposed a new fuzzy method for formulating and evaluating illegal points and three new kinds of evaluation functions and developed a special Hybrid Genetic Algorithm (HGA) with penalty function and gradient direction search for nonlinear programming problems. It uses mutation along the weighted gradient direction as its main operator and uses arithmetic combinatorial crossover only in the later generation process. Simulation of some examples show that this method is effective. {\textcopyright} 1998 Elsevier Science Ltd. All rights reserved.",

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author = "Jiafu Tang and Dingwei Wang and A. Ip and Fung, \{R. Y K\}",

year = "1998",

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language = "English",

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TY - JOUR

T1 - A hybrid genetic algorithm for a type of nonlinear programming problem

AU - Tang, Jiafu

AU - Wang, Dingwei

AU - Ip, A.

AU - Fung, R. Y K

PY - 1998/9

Y1 - 1998/9

N2 - Based on the introduction of some new concepts of semifeasible direction, Feasible Degree (FD1) of semifeasible direction, feasible degree (FD2) of illegal points 'belonging to' feasible domain, etc., this paper proposed a new fuzzy method for formulating and evaluating illegal points and three new kinds of evaluation functions and developed a special Hybrid Genetic Algorithm (HGA) with penalty function and gradient direction search for nonlinear programming problems. It uses mutation along the weighted gradient direction as its main operator and uses arithmetic combinatorial crossover only in the later generation process. Simulation of some examples show that this method is effective. © 1998 Elsevier Science Ltd. All rights reserved.

AB - Based on the introduction of some new concepts of semifeasible direction, Feasible Degree (FD1) of semifeasible direction, feasible degree (FD2) of illegal points 'belonging to' feasible domain, etc., this paper proposed a new fuzzy method for formulating and evaluating illegal points and three new kinds of evaluation functions and developed a special Hybrid Genetic Algorithm (HGA) with penalty function and gradient direction search for nonlinear programming problems. It uses mutation along the weighted gradient direction as its main operator and uses arithmetic combinatorial crossover only in the later generation process. Simulation of some examples show that this method is effective. © 1998 Elsevier Science Ltd. All rights reserved.

KW - Feasible degree

KW - Hybrid genetic algorithm

KW - Nonlinear programming

KW - Semifeasible direction

KW - Weighted gradient direction

UR - https://www.scopus.com/pages/publications/0032165074

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-0032165074&origin=recordpage

U2 - 10.1016/S0898-1221(98)00146-1

DO - 10.1016/S0898-1221(98)00146-1

M3 - RGC 21 - Publication in refereed journal

SN - 0898-1221

VL - 36

SP - 11

EP - 21

JO - Computers and Mathematics with Applications

JF - Computers and Mathematics with Applications

IS - 5

ER -

A hybrid genetic algorithm for a type of nonlinear programming problem

Abstract

Research Keywords

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