Using response surface design to determine the optimal parameters of genetic algorithm and a case study

Genetic algorithms (GAs) are efficient stochastic search techniques for approximating optimal solutions within complex search spaces and used widely to solve NP-hard problems. Genetic algorithm includes a number of parameters whose different levels strictly affect the performance of the algorithm. T...

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Bibliographic details

Volume:	51
Main Author:	Kucukkoc, Ibrahim Karaoglan, Aslan Deniz Yaman, Ramazan
Format:	Journal Article
Language:	English
Place of publication:	ABINGDON Routledge 01.09.2013 TAYLOR & FRANCIS LTD
published in:	International journal of production research Vol. 51; no. 17; pp. 5039 - 5054
ORCID:	0000-0001-6042-6896
Data of publication:	9/1/2013
ISSN:	0020-7543 1366-588X
EISSN:	1366-588X
Discipline:	Engineering
Subjects:	Genetic algorithm (GA) Assembly line balancing Parameter optimisation Response surface methodology (RSM) Design of experiment Operations Research & Management Science Engineering Engineering, Manufacturing Engineering, Industrial Technology Science & Technology PARALLEL WORKSTATIONS Genetic algorithm (GA) Response surface methodology (RSM) Design of experiment LINE OPTIMIZATION TIME Assembly line balancing SIMULATION Parameter optimisation
Online Access:	Fulltext
Database:	Web of Science - Science Citation Index Expanded - 2013 Web of Knowledge Science Citation Index Expanded Web of Science CrossRef Database information Databases - DBIS