Adaptive Gene Level Mutation

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@Article{al-afandi:2021:Algorithms,

• author = "Jalal Al-Afandi and Andras Horvath",
• title = "Adaptive Gene Level Mutation",
• journal = "Algorithms",
• year = "2021",
• volume = "14",
• number = "1",
• keywords = "genetic algorithms, genetic programming",
• ISSN = "1999-4893",
• URL = "https://www.mdpi.com/1999-4893/14/1/16",
• DOI = "doi:10.3390/a14010016",
• abstract = "Genetic Algorithms are stochastic optimisation 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.",
• notes = "also known as \cite{a14010016}",

}

Genetic Programming entries for Jalal Al-Afandi Andras Horvath

Citations