Comparação de operadores de cruzamentos utilizados em algoritmos genéticos aplicados aos problemas da cobertura de conjuntos e da mochila multidimensional
Genetic algorithms are artificial intelligence techniques used for optimisation problems based on the theory of Darwinian natural selection. The effectiveness of this algorithm is more noticeable in the case of NP-hard problems such as the Set Covering Problem (SCP) and the Multidimensional Knaps...
| Autor principal: | DUARTE, Renan Lobo |
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| Grau: | Trabalho de Conclusão de Curso - Graduação |
| Publicado em: |
2019
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| Assuntos: | |
| Acesso em linha: |
https://bdm.ufpa.br/jspui/handle/prefix/2365 |
| Resumo: |
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Genetic algorithms are artificial intelligence techniques used for optimisation problems
based on the theory of Darwinian natural selection. The effectiveness of this algorithm is
more noticeable in the case of NP-hard problems such as the Set Covering Problem (SCP)
and the Multidimensional Knapsack Problem (MKP). Crossover operators are the steps of
the genetic algorithms that have fundamental importance in searching solutions in the
search space. Evaluating, therefore, crossover methods is most important because of their
ability to form individuals, which can improve or worsen them. At the end of this work, It
can be said which crossover method fits better in and for which type of problem. |