History Information Based Optimization of Additively Decomposed Function with Constraints

In this paper, we propose a modified estimation of distribution algorithm HCFA (History information based Constraint Factorization Algorithm) to solve the optimization problem of additivelydecomposed function with constraints. It is based on factorized distribution instead of penalty function and an...

Full description

Saved in:
Bibliographic Details
Published inComputational and Information Science pp. 359 - 364
Main Authors Ren, Qingsheng, Zeng, Jin, Qi, Feihu
Format Book Chapter Conference Proceeding
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg 2004
Springer
SeriesLecture Notes in Computer Science
Subjects
Algorithmics. Computability. Computer arithmetics
Applied sciences
Computer science; control theory; systems
Constraint Optimization
Constraint Problem
Distribution Algorithm
Exact sciences and technology
History Information
Small Population Size
Theoretical computing
Information use
Penalty function
Distributed algorithm
Evolutionary computation
Distribution function
Feasibility
Linear model
Constrained optimization
Online AccessGet full text

Cover

More Information
Summary:In this paper, we propose a modified estimation of distribution algorithm HCFA (History information based Constraint Factorization Algorithm) to solve the optimization problem of additivelydecomposed function with constraints. It is based on factorized distribution instead of penalty function and any transformation to a linear model or others. The history information is used and good results can be achieved with small population size. The feasibility of the new algorithm is also given.
ISBN:9783540241270
3540241272
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-540-30497-5_56