A Prescription of Methodological Guidelines for Comparing Bio-inspired Optimization Algorithms
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arXiv
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| Format: | Preprint |
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2020
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| author | LaTorre, Antonio Molina, Daniel Osaba, Eneko Del Ser, Javier Herrera, Francisco |
| author_facet | LaTorre, Antonio Molina, Daniel Osaba, Eneko Del Ser, Javier Herrera, Francisco |
| contents | Bio-inspired optimization (including Evolutionary Computation and Swarm Intelligence) is a growing research topic with many competitive bio-inspired algorithms being proposed every year. In such an active area, preparing a successful proposal of a new bio-inspired algorithm is not an easy task. Given the maturity of this research field, proposing a new optimization technique with innovative elements is no longer enough. Apart from the novelty, results reported by the authors should be proven to achieve a significant advance over previous outcomes from the state of the art. Unfortunately, not all new proposals deal with this requirement properly. Some of them fail to select appropriate benchmarks or reference algorithms to compare with. In other cases, the validation process carried out is not defined in a principled way (or is even not done at all). Consequently, the significance of the results presented in such studies cannot be guaranteed. In this work we review several recommendations in the literature and propose methodological guidelines to prepare a successful proposal, taking all these issues into account. We expect these guidelines to be useful not only for authors, but also for reviewers and editors along their assessment of new contributions to the field. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2004_09969 |
| institution | arXiv |
| publishDate | 2020 |
| record_format | arxiv |
| spellingShingle | A Prescription of Methodological Guidelines for Comparing Bio-inspired Optimization Algorithms LaTorre, Antonio Molina, Daniel Osaba, Eneko Del Ser, Javier Herrera, Francisco Neural and Evolutionary Computing Artificial Intelligence Bio-inspired optimization (including Evolutionary Computation and Swarm Intelligence) is a growing research topic with many competitive bio-inspired algorithms being proposed every year. In such an active area, preparing a successful proposal of a new bio-inspired algorithm is not an easy task. Given the maturity of this research field, proposing a new optimization technique with innovative elements is no longer enough. Apart from the novelty, results reported by the authors should be proven to achieve a significant advance over previous outcomes from the state of the art. Unfortunately, not all new proposals deal with this requirement properly. Some of them fail to select appropriate benchmarks or reference algorithms to compare with. In other cases, the validation process carried out is not defined in a principled way (or is even not done at all). Consequently, the significance of the results presented in such studies cannot be guaranteed. In this work we review several recommendations in the literature and propose methodological guidelines to prepare a successful proposal, taking all these issues into account. We expect these guidelines to be useful not only for authors, but also for reviewers and editors along their assessment of new contributions to the field. |
| title | A Prescription of Methodological Guidelines for Comparing Bio-inspired Optimization Algorithms |
| topic | Neural and Evolutionary Computing Artificial Intelligence |
| url | https://arxiv.org/abs/2004.09969 |