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Simplified Genetic Algorithm: Simplify and Improve RGA for Parameter OptimizationsNGAMTAWEE, R. , WARDKEIN, P. |
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Author keywords
algorithm, evolutionary computation, genetic algorithms, optimization, particle swarm optimization
References keywords
genetic(14), swarm(11), evolutionary(11), computation(11), algorithms(9), optimization(8), convergence(5), premature(4), intelligence(4), algorithm(4)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2014-11-30
Volume 14, Issue 4, Year 2014, On page(s): 55 - 64
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2014.04009
Web of Science Accession Number: 000348772500009
SCOPUS ID: 84921692972
Abstract
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. |
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[2] Hybridization of Genetic Algorithm and Artificial Immune System for Assignment Problem, Farhan, La Ode Muhammad, Zukhri, Zainudin, IOP Conference Series: Materials Science and Engineering, ISSN 1757-8981, Issue 1, Volume 803, 2020.
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[3] Performance assessment of the metaheuristic optimization algorithms: an exhaustive review, Halim, A. Hanif, Ismail, I., Das, Swagatam, Artificial Intelligence Review, ISSN 0269-2821, Issue 3, Volume 54, 2021.
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
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