Publicação
Non-geometric pulse: An adaptive geometricity approach for Genetic Algorithms
| Resumo: | Evolutionary algorithms (EAs) are a family of algorithms inspired by the Darwinian theory of evolution. Mathematics, particularly geometry and topology, allows for the possibility of developing a general geometrical framework and consequently generating deeper insights that may be shared among the different EAs. Genetic Algorithm (GA), inspired by Darwin’s theory of natural selection, is a popular algorithm among EAs. This is a population-based, fitness-oriented algorithm that performs a convex heuristic search to optimize a plethora of problems. Common limitations of GA as well as other EAs have geometrical origins like premature convergence, where the final population’s convex-hull might not include the best solution, called Global Optima. Population diversity maintenance is a key idea that tries to tackle this problem but is often performed through geometrical methods that constantly diminish the search space’s area. In this work, a self-adaptive geometricity approach will be presented. In particular, the non-geometric crossover is strategically employed in a symbiotic relation with geometric crossover, maintaining diversity in a logical way from a geometric/topological grammar standpoint. A comparison with well-known diversity maintenance methods is provided, using common benchmarks that serve as general testing ground for the considered techniques. |
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| Autores principais: | Ferreira, José Pedro Mendes Ribeiro do Vale |
| Assunto: | Convex Search Evolutionary Algorithms Genetic Algorithms Geometric semantic operators Diversity maintenance |
| Ano: | 2022 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| Resumo: | Evolutionary algorithms (EAs) are a family of algorithms inspired by the Darwinian theory of evolution. Mathematics, particularly geometry and topology, allows for the possibility of developing a general geometrical framework and consequently generating deeper insights that may be shared among the different EAs. Genetic Algorithm (GA), inspired by Darwin’s theory of natural selection, is a popular algorithm among EAs. This is a population-based, fitness-oriented algorithm that performs a convex heuristic search to optimize a plethora of problems. Common limitations of GA as well as other EAs have geometrical origins like premature convergence, where the final population’s convex-hull might not include the best solution, called Global Optima. Population diversity maintenance is a key idea that tries to tackle this problem but is often performed through geometrical methods that constantly diminish the search space’s area. In this work, a self-adaptive geometricity approach will be presented. In particular, the non-geometric crossover is strategically employed in a symbiotic relation with geometric crossover, maintaining diversity in a logical way from a geometric/topological grammar standpoint. A comparison with well-known diversity maintenance methods is provided, using common benchmarks that serve as general testing ground for the considered techniques. |
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