Variable Search Space Converging Genetic Algorithm for Solving System of Non-linear Equations

This paper introduce a new variant of the Genetic Algorithm whichis developed to handle multivariable, multi-objective and very high search space optimization problems like the solving system of non-linear equations. It is an integer coded Genetic Algorithm with conventional cross over and mutation...

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Autores principales:	SS Venkatesh, Mishra Deepak
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2020
Materias:	genetic algorithm roulette wheel selection cross over mutation multi objective optimization multi variable optimization computer science artificial intelligence Science Q Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/cf041b07e80f4acfac6b117f493b7a9f
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id	oai:doaj.org-article:cf041b07e80f4acfac6b117f493b7a9f
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spelling	oai:doaj.org-article:cf041b07e80f4acfac6b117f493b7a9f2021-12-05T14:10:51ZVariable Search Space Converging Genetic Algorithm for Solving System of Non-linear Equations2191-026X10.1515/jisys-2019-0233https://doaj.org/article/cf041b07e80f4acfac6b117f493b7a9f2020-07-01T00:00:00Zhttps://doi.org/10.1515/jisys-2019-0233https://doaj.org/toc/2191-026XThis paper introduce a new variant of the Genetic Algorithm whichis developed to handle multivariable, multi-objective and very high search space optimization problems like the solving system of non-linear equations. It is an integer coded Genetic Algorithm with conventional cross over and mutation but with Inverse algorithm is varying its search space by varying its digit length on every cycle and it does a fine search followed by a coarse search. And its solution to the optimization problem will converge to precise value over the cycles. Every equation of the system is considered as a single minimization objective function. Multiple objectives are converted to a single fitness function by summing their absolute values. Some difficult test functions for optimization and applications are used to evaluate this algorithm. The results prove that this algorithm is capable to produce promising and precise results.SS VenkateshMishra DeepakDe Gruyterarticlegenetic algorithmroulette wheel selectioncross overmutationmulti objective optimizationmulti variable optimizationgenetic algorithmmulti objective optimizationmulti variable optimizationcomputer scienceartificial intelligenceScienceQElectronic computers. Computer scienceQA75.5-76.95ENJournal of Intelligent Systems, Vol 30, Iss 1, Pp 142-164 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	genetic algorithm roulette wheel selection cross over mutation multi objective optimization multi variable optimization genetic algorithm multi objective optimization multi variable optimization computer science artificial intelligence Science Q Electronic computers. Computer science QA75.5-76.95
spellingShingle	genetic algorithm roulette wheel selection cross over mutation multi objective optimization multi variable optimization genetic algorithm multi objective optimization multi variable optimization computer science artificial intelligence Science Q Electronic computers. Computer science QA75.5-76.95 SS Venkatesh Mishra Deepak Variable Search Space Converging Genetic Algorithm for Solving System of Non-linear Equations
description	This paper introduce a new variant of the Genetic Algorithm whichis developed to handle multivariable, multi-objective and very high search space optimization problems like the solving system of non-linear equations. It is an integer coded Genetic Algorithm with conventional cross over and mutation but with Inverse algorithm is varying its search space by varying its digit length on every cycle and it does a fine search followed by a coarse search. And its solution to the optimization problem will converge to precise value over the cycles. Every equation of the system is considered as a single minimization objective function. Multiple objectives are converted to a single fitness function by summing their absolute values. Some difficult test functions for optimization and applications are used to evaluate this algorithm. The results prove that this algorithm is capable to produce promising and precise results.
format	article
author	SS Venkatesh Mishra Deepak
author_facet	SS Venkatesh Mishra Deepak
author_sort	SS Venkatesh
title	Variable Search Space Converging Genetic Algorithm for Solving System of Non-linear Equations
title_short	Variable Search Space Converging Genetic Algorithm for Solving System of Non-linear Equations
title_full	Variable Search Space Converging Genetic Algorithm for Solving System of Non-linear Equations
title_fullStr	Variable Search Space Converging Genetic Algorithm for Solving System of Non-linear Equations
title_full_unstemmed	Variable Search Space Converging Genetic Algorithm for Solving System of Non-linear Equations
title_sort	variable search space converging genetic algorithm for solving system of non-linear equations
publisher	De Gruyter
publishDate	2020
url	https://doaj.org/article/cf041b07e80f4acfac6b117f493b7a9f
work_keys_str_mv	AT ssvenkatesh variablesearchspaceconverginggeneticalgorithmforsolvingsystemofnonlinearequations AT mishradeepak variablesearchspaceconverginggeneticalgorithmforsolvingsystemofnonlinearequations
_version_	1718371690514219008

Variable Search Space Converging Genetic Algorithm for Solving System of Non-linear Equations

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