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Article

Adaptive Gene Level Mutation

by *,†,‡ and *,†,‡
Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, 1083 Budapest, Hungary
*
Authors to whom correspondence should be addressed.
Current address: Práter u. 50/A, 1083 Budapest, Hungary.
These authors contributed equally to this work.
Received: 27 November 2020 / Revised: 29 December 2020 / Accepted: 7 January 2021 / Published: 9 January 2021
Genetic Algorithms are stochastic optimization methods where solution candidates, complying to a specific problem representation, are evaluated according to a predefined fitness function. These approaches can provide solutions in various tasks even, where analytic solutions can not be or are too complex to be computed. In this paper we will show, how certain set of problems are partially solvable allowing us to grade segments of a solution individually, which results local and individual tuning of mutation parameters for genes. We will demonstrate the efficiency of our method on the N-Queens and travelling salesman problems where we can demonstrate that our approach always results faster convergence and in most cases a lower error than the traditional approach. View Full-Text
Keywords: genetic algorithms; evolution strategies; adaptive mutation; genetic programming; evolutionary programming genetic algorithms; evolution strategies; adaptive mutation; genetic programming; evolutionary programming
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MDPI and ACS Style

Al-Afandi, J.; Horváth, A. Adaptive Gene Level Mutation. Algorithms 2021, 14, 16. https://0-doi-org.brum.beds.ac.uk/10.3390/a14010016

AMA Style

Al-Afandi J, Horváth A. Adaptive Gene Level Mutation. Algorithms. 2021; 14(1):16. https://0-doi-org.brum.beds.ac.uk/10.3390/a14010016

Chicago/Turabian Style

Al-Afandi, Jalal; Horváth, András. 2021. "Adaptive Gene Level Mutation" Algorithms 14, no. 1: 16. https://0-doi-org.brum.beds.ac.uk/10.3390/a14010016

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