A Multi-population Schema Designed for Biased Random-Key Genetic Algorithms on Continuous Optimisation Problems

Boiani, Mateus; Parpinelli, Rafael Stubs; Dorn, Márcio

doi:10.1007/978-3-031-21686-2_31

Cited by 1 publication

(1 citation statement)

References 23 publications

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“…[12]). Moreover, different approaches, such as a fitness-based Migration Policy designed to promote the maintenance of diversity through a mechanism that combines groups of individuals to alternate between exploration and exploitation proposed in [6], can be used instead.…”

Section: Migration Topologiesmentioning

confidence: 99%

A New Approach to Detecting and Preventing Populations Stagnation Through Dynamic Changes in Multi-Population-Based Algorithms

Łapa,

Rutkowska,

Byrski

et al. 2023

Journal of Artificial Intelligence and Soft Computing Research

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In this paper, a new mechanism for detecting population stagnation based on the analysis of the local improvement of the evaluation function and the infinite impulse response filter is proposed. The purpose of this mechanism is to improve the population stagnation detection capability for various optimization scenarios, and thus to improve multi-population-based algorithms (MPBAs) performance. In addition, various other approaches have been proposed to eliminate stagnation, including approaches aimed at both improving performance and reducing the complexity of the algorithms. The developed methods were tested, among the others, for various migration topologies and various MPBAs, including the MNIA algorithm, which allows the use of many different base algorithms and thus eliminates the need to select the population-based algorithm for a given simulation problem. The simulations were performed for typical benchmark functions and control problems. The obtained results confirm the validity of the developed method.

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Section: Migration Topologiesmentioning

confidence: 99%