Improving genetic algorithms performance via deterministic population shrinkage

• URL: http://arxiv.org/abs/2401.12121v1
• Date: Mon, 22 Jan 2024 17:05:16 GMT
• Title: Improving genetic algorithms performance via deterministic population shrinkage
• Authors: Juan Luis Jim\'enez Laredo and Carlos Fernandes and Juan Juli\'an Merelo and Christian Gagn\'e
• Abstract summary: This paper presents an empirical study on the possible benefits of a Simple Variable Population Sizing scheme on the performance of Genetic Algorithms (GAs) It consists in decreasing the population for a GA run following a predetermined schedule, configured by a speed and a severity parameter. Results show several combinations of speed-severity where SVPS-GA preserves the solution quality while improving performances, by reducing the number of evaluations needed for success.
• Score: 9.334663477968027
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite the intuition that the same population size is not needed throughout the run of an Evolutionary Algorithm (EA), most EAs use a fixed population size. This paper presents an empirical study on the possible benefits of a Simple Variable Population Sizing (SVPS) scheme on the performance of Genetic Algorithms (GAs). It consists in decreasing the population for a GA run following a predetermined schedule, configured by a speed and a severity parameter. The method uses as initial population size an estimation of the minimum size needed to supply enough building blocks, using a fixed-size selectorecombinative GA converging within some confidence interval toward good solutions for a particular problem. Following this methodology, a scalability analysis is conducted on deceptive, quasi-deceptive, and non-deceptive trap functions in order to assess whether SVPS-GA improves performances compared to a fixed-size GA under different problem instances and difficulty levels. Results show several combinations of speed-severity where SVPS-GA preserves the solution quality while improving performances, by reducing the number of evaluations needed for success.

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