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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Stefan cel Mare University of Suceava
2014-11-01
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Online Access: | http://dx.doi.org/10.4316/AECE.2014.04009 |
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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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