Simplified Genetic Algorithm: Simplify and Improve RGA for Parameter Optimizations

The structural complexity and complicated generic operators of Genetic Algorithm (GA) contribute to its slow computational speed. Furthermore, GA and other similar algorithms with a small population size are vulnerable to the problem of premature convergence. Premature convergence causes the algo...

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Main Authors: NGAMTAWEE, R., WARDKEIN, P.
Format: Article
Language:English
Published: Stefan cel Mare University of Suceava 2014-11-01
Series:Advances in Electrical and Computer Engineering
Subjects:
Online Access:http://dx.doi.org/10.4316/AECE.2014.04009
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spelling doaj-928df2ba872d4b6fa7a301dba55d54eb2020-11-24T23:42:42ZengStefan cel Mare University of SuceavaAdvances in Electrical and Computer Engineering1582-74451844-76002014-11-01144556410.4316/AECE.2014.04009Simplified Genetic Algorithm: Simplify and Improve RGA for Parameter OptimizationsNGAMTAWEE, R.WARDKEIN, P. The structural complexity and complicated generic operators of Genetic Algorithm (GA) contribute to its slow computational speed. Furthermore, GA and other similar algorithms with a small population size are vulnerable to the problem of premature convergence. Premature convergence causes the algorithms to stagnate and stop searching, giving rise to wasteful computation. Even though the problem can be addressed with a larger population size, computational time is inevitably increased. This research paper has thus proposed Simplified Genetic Algorithm (SimpGA). This algorithm utilizes a one-pair-built-all structure in which only two parent chromosomes are required to produce the entire population (offspring). Rather than relying on the conventional operators, simplified operators, i.e. timer mutation, diform crossover and topmost selection, are used in the proposed SimpGA. In addition, tests are carried out with SimpGA on four test functions and four applications. The experimental results show that SimpGA is simpler to implement and performs well, especially in a small population environment. http://dx.doi.org/10.4316/AECE.2014.04009algorithmevolutionary computationgenetic algorithmsoptimizationparticle swarm optimization
collection DOAJ
language English
format Article
sources DOAJ
author NGAMTAWEE, R.
WARDKEIN, P.
spellingShingle NGAMTAWEE, R.
WARDKEIN, P.
Simplified Genetic Algorithm: Simplify and Improve RGA for Parameter Optimizations
Advances in Electrical and Computer Engineering
algorithm
evolutionary computation
genetic algorithms
optimization
particle swarm optimization
author_facet NGAMTAWEE, R.
WARDKEIN, P.
author_sort NGAMTAWEE, R.
title Simplified Genetic Algorithm: Simplify and Improve RGA for Parameter Optimizations
title_short Simplified Genetic Algorithm: Simplify and Improve RGA for Parameter Optimizations
title_full Simplified Genetic Algorithm: Simplify and Improve RGA for Parameter Optimizations
title_fullStr Simplified Genetic Algorithm: Simplify and Improve RGA for Parameter Optimizations
title_full_unstemmed Simplified Genetic Algorithm: Simplify and Improve RGA for Parameter Optimizations
title_sort simplified genetic algorithm: simplify and improve rga for parameter optimizations
publisher Stefan cel Mare University of Suceava
series Advances in Electrical and Computer Engineering
issn 1582-7445
1844-7600
publishDate 2014-11-01
description The structural complexity and complicated generic operators of Genetic Algorithm (GA) contribute to its slow computational speed. Furthermore, GA and other similar algorithms with a small population size are vulnerable to the problem of premature convergence. Premature convergence causes the algorithms to stagnate and stop searching, giving rise to wasteful computation. Even though the problem can be addressed with a larger population size, computational time is inevitably increased. This research paper has thus proposed Simplified Genetic Algorithm (SimpGA). This algorithm utilizes a one-pair-built-all structure in which only two parent chromosomes are required to produce the entire population (offspring). Rather than relying on the conventional operators, simplified operators, i.e. timer mutation, diform crossover and topmost selection, are used in the proposed SimpGA. In addition, tests are carried out with SimpGA on four test functions and four applications. The experimental results show that SimpGA is simpler to implement and performs well, especially in a small population environment.
topic algorithm
evolutionary computation
genetic algorithms
optimization
particle swarm optimization
url http://dx.doi.org/10.4316/AECE.2014.04009
work_keys_str_mv AT ngamtaweer simplifiedgeneticalgorithmsimplifyandimprovergaforparameteroptimizations
AT wardkeinp simplifiedgeneticalgorithmsimplifyandimprovergaforparameteroptimizations
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