Constructive-synthesizing modeling of the genetic algorithm chromosomes with encoded sorting algorithms
In the structural adaptation of algorithms using a genetic algorithm, one of the challenges is encoding the struc ture of algorithms into a chromosome. This article explores an approach to structural adaptation and the devel opment of efficient sorting algorithms based on the paradigm of constructiv...
Збережено в:
| Дата: | 2025 |
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| Автори: | , |
| Формат: | Стаття |
| Мова: | Ukrainian |
| Опубліковано: |
PROBLEMS IN PROGRAMMING
2025
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| Онлайн доступ: | https://pp.isofts.kiev.ua/index.php/ojs1/article/view/857 |
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| Назва журналу: | Problems in programming |
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Репозитарії
Problems in programming| Резюме: | In the structural adaptation of algorithms using a genetic algorithm, one of the challenges is encoding the struc ture of algorithms into a chromosome. This article explores an approach to structural adaptation and the devel opment of efficient sorting algorithms based on the paradigm of constructive-synthesizing modeling. A method is proposed for representing chromosomes as encoded structures corresponding to various variants of sorting algorithms. This approach allows the solution search space to be formed not only as a set of numerical parameters but also as complete software. Operations of substitution, partial inference, and composition are described, which enable the synthesis of new sorting algorithms. A genetic algorithm is applied to generate and select functionally equivalent algorithms. Examples are provided of constructing chromosome trees that encode sorting procedures of varying complexity. A program has been implemented for generating and evolutionarily improving chromo somes with encoded sorting algorithms. The application of constructive-synthesizing modeling to the problem of encoding structurally different but functionally equivalent algorithms into chromosomes is demonstrated. Ex perimental results confirmed that usage of constructive-synthesizing modeling increases population diversity and accelerates the discovery of efficient solutions compared to classical genetic algorithms. The proposed method ology can be used for automated algorithm construction, optimization of their structure, and adaptation to spe cific usage conditions.Prombles in programming 2025; 3: 39-52 |
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