by BGW Craenen, AE Eiben and van Hemert, J
Abstract:
Constraint handling is not straightforward in evolutionary algorithms (EA) since the usual search operators, mutation and recombination, are `blind' to constraints. Nevertheless, the issue is highly relevant, for many challenging problems involve constraints. Over the last decade numerous EAs for solving constraint satisfaction problems (CSP) have been introduced and studied on various problems. The diversity of approaches and the variety of problems used to study the resulting algorithms prevents a fair and accurate comparison of these algorithms. This paper aligns related work by presenting a concise overview and an extensive performance comparison of all these EAs on a systematically generated test suite of random binary CSPs. The random problem instance generator is based on a theoretical model that fixes deficiencies of models and respective generators that have been formerly used in the Evolutionary Computing (EC) field.
Reference:
Comparing Evolutionary Algorithms on Binary Constraint Satisfaction Problems (BGW Craenen, AE Eiben and van Hemert, J), In IEEE Trans Evol Comput, volume 7, 2003.
Bibtex Entry:
@article{CEH2003,
_day = {01},
abstract = {Constraint handling is not straightforward in evolutionary algorithms (EA) since the usual search operators, mutation and recombination, are `blind' to constraints. Nevertheless, the issue is highly relevant, for many challenging problems involve constraints. Over the last decade numerous EAs for solving constraint satisfaction problems (CSP) have been introduced and studied on various problems. The diversity of approaches and the variety of problems used to study the resulting algorithms prevents a fair and accurate comparison of these algorithms. This paper aligns related work by presenting a concise overview and an extensive performance comparison of all these EAs on a systematically generated test suite of random binary CSPs. The random problem instance generator is based on a theoretical model that fixes deficiencies of models and respective generators that have been formerly used in the Evolutionary Computing (EC) field.},
annote = {ISI 2.426 in 2007, which placed it at the 24th among 382 computer science journals, the 10th among 79 artificial intelligence journals and the 5th among 79 computer science theory & methods journals},
author = {BGW Craenen and AE Eiben and van Hemert, J},
date-added = {2008-08-18 12:42:43 +0100},
date-modified = {2008-08-18 12:42:43 +0100},
journal = {IEEE Trans Evol Comput},
keywords = {constraint satisfaction},
notedisabled = {\textsc{Cited by 48 (Google Scholar 2009/01/19); ISI impact factor = 3.257 (2005, 15th among 352 computer science journals, 4th among 79 artificial intelligence journals and 4th among 71 computer science theory and methods journals)}},
number = {5},
pages = {424--44},
pdf = {http://www.vanhemert.co.uk/publications/trans-ec2004.Comparing_Evolutionary_Algorithms_on_Binary_Constraint_Satisfaction_Problems.pdf},
title = {Comparing Evolutionary Algorithms on Binary Constraint Satisfaction Problems},
url = {http://ieeexplore.ieee.org/xpl/abs_free.jsp?isNumber=27734&prod=JNL&arnumber=1237162&arSt=+424&ared=+444&arAuthor=+Craenen%2C+BGW%3B++Eiben%2C+AE%3B++van+Hemert%2C+J&arNumber=1237162&a_id0=1237161&a_id1=1237162&a_id2=1237163&a_id3=1237164&a_id4=1237165&a_id5=1237166&count=6},
volume = {7},
year = {2003},
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