Publicação
Reducing the number of objectives for many-objectives optimization: empirical analysis of a machine learning approach
| Resumo: | The practical need of solving real-world optimization problems is faced very often of dealing with many objectives, but from the beginning, a question arises: Are all the objectives really necessary? The answer to this question lies in the complex relations existing between the parameters of the process, i.e., not only between the objectives and the decision variables (DVs), but also between the DVs and DVs and between the objectives and objectives. Simultaneously, intense research is made to improve the performance of multi-objective population-based algorithms to deal with many objectives that, often, imply complex algorithms and time consuming computations with complex results that experts on the field of the problem might not understand and, as a consequence, did not accept and apply in practice. A straightforward alternative is to infer the complex relations between the process parameters with the aim of reducing the number of objectives. The use of Machine Learning (ML) methodologies for that can be very useful since it is someway demonstrated in the literature on the subject of reducing the number of objectives. In this work, ML is used to reduce the number of objectives and the results are assessed empirically using a real-world application. |
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| Autores principais: | Gaspar-Cunha, A. |
| Outros Autores: | Costa, Paulo; Monaco, Francisco; Delbem, Alexandre |
| Assunto: | Multi-objective optimization Machine Learning Evolutionary Algorithms Reduction of the number of objectives |
| Ano: | 2023 |
| País: | Portugal |
| Tipo de documento: | capítulo de livro |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade do Minho |
| Idioma: | inglês |
| Origem: | RepositóriUM - Universidade do Minho |
| Resumo: | The practical need of solving real-world optimization problems is faced very often of dealing with many objectives, but from the beginning, a question arises: Are all the objectives really necessary? The answer to this question lies in the complex relations existing between the parameters of the process, i.e., not only between the objectives and the decision variables (DVs), but also between the DVs and DVs and between the objectives and objectives. Simultaneously, intense research is made to improve the performance of multi-objective population-based algorithms to deal with many objectives that, often, imply complex algorithms and time consuming computations with complex results that experts on the field of the problem might not understand and, as a consequence, did not accept and apply in practice. A straightforward alternative is to infer the complex relations between the process parameters with the aim of reducing the number of objectives. The use of Machine Learning (ML) methodologies for that can be very useful since it is someway demonstrated in the literature on the subject of reducing the number of objectives. In this work, ML is used to reduce the number of objectives and the results are assessed empirically using a real-world application. |
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