<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Population Based Algorithms (PBAs) are excellent search tools that allow searching space of parameters defined by problems under consideration. They are especially useful when it is difficult to define a differentiable evaluation criterion. This applies, for example, to problems that are a combination of continuous and discrete (combinatorial) problems. In such problems, it is often necessary to select a certain structure of the solution (e.g. a neural network or other systems with a structure usually selected by the trial and error method) and to determine the parameters of such structure. As PBAs have great application possibilities, the aim is to develop more and more effective search formulas used in them. An interesting approach is to use multiple populations and process them with separate PBAs (in a different way). In this paper, we propose a new multi-population-based algorithm with: (a) subpopulation evaluation and (b) replacement of the associated PBAs subpopulation formulas used for their processing. In the simulations, we used a set of typical CEC2013 benchmark functions. The obtained results confirm the validity of the proposed concept.</p>
</abstract>

Population Based Algorithms (PBAs) are excellent search tools that allow searching space of parameters defined by problems under consideration. They are especially useful when it is difficult to define a differentiable evaluation criterion. This applies, for example, to problems that are a combination of continuous and discrete (combinatorial) problems. In such problems, it is often necessary to select a certain structure of the solution (e.g. a neural network or other systems with a structure usually selected by the trial and error method) and to determine the parameters of such structure. As PBAs have great application possibilities, the aim is to develop more and more effective search formulas used in them. An interesting approach is to use multiple populations and process them with separate PBAs (in a different way). In this paper, we propose a new multi-population-based algorithm with: (a) subpopulation evaluation and (b) replacement of the associated PBAs subpopulation formulas used for their processing. In the simulations, we used a set of typical CEC2013 benchmark functions. The obtained results confirm the validity of the proposed concept.

Multi-Population-Based Algorithm with an Exchange of Training Plans Based on Population Evaluation

Department of Intelligent Computer Systems

Journal of Artificial Intelligence and Soft Computing Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Population Based Algorithms (PBAs) are excellent search tools that allow searching space of parameters defined by problems under consideration. They are...

Multi-Population-Based Algorithm with an Exchange of Training Plans Based on Population Evaluation

Publicado en línea: 29 oct 2022

Páginas: 239 - 253

Recibido: 31 may 2022

Aceptado: 14 oct 2022

DOI: https://doi.org/10.2478/jaiscr-2022-0016

Palabras clave
population-based algorithm, multi-population algorithm, hybrid algorithm, island algorithm, subpopulation evaluation, training plan

© 2022 Krystian Łapa et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Multi-Population-Based Algorithm with an Exchange of Training Plans Based on Population Evaluation

Publicado en línea: 29 oct 2022

Páginas: 239 - 253

Recibido: 31 may 2022

Aceptado: 14 oct 2022

DOI: https://doi.org/10.2478/jaiscr-2022-0016

Palabras clavepopulation-based algorithm, multi-population algorithm, hybrid algorithm, island algorithm, subpopulation evaluation, training plan

© 2022 Krystian Łapa et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Palabras clave
population-based algorithm, multi-population algorithm, hybrid algorithm, island algorithm, subpopulation evaluation, training plan