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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2023
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2401.00168 |
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| _version_ | 1866909058195259392 |
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| author | Hou, Yaqing Sun, Mingyang Gupta, Abhishek Jin, Yaochu Piao, Haiyin Ge, Hongwei Zhang, Qiang |
| author_facet | Hou, Yaqing Sun, Mingyang Gupta, Abhishek Jin, Yaochu Piao, Haiyin Ge, Hongwei Zhang, Qiang |
| contents | In this paper, we scale evolutionary algorithms to high-dimensional optimization problems that deceptively possess a low effective dimensionality (certain dimensions do not significantly affect the objective function). To this end, an instantiation of the multiform optimization paradigm is presented, where multiple low-dimensional counterparts of a target high-dimensional task are generated via random embeddings. Since the exact relationship between the auxiliary (low-dimensional) tasks and the target is a priori unknown, a multiform evolutionary algorithm is developed for unifying all formulations into a single multi-task setting. The resultant joint optimization enables the target task to efficiently reuse solutions evolved across various low-dimensional searches via cross-form genetic transfers, hence speeding up overall convergence characteristics. To validate the overall efficacy of our proposed algorithmic framework, comprehensive experimental studies are carried out on well-known continuous benchmark functions as well as a set of practical problems in the hyper-parameter tuning of machine learning models and deep learning models in classification tasks and Predator-Prey games, respectively. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_00168 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Multiform Evolution for High-Dimensional Problems with Low Effective Dimensionality Hou, Yaqing Sun, Mingyang Gupta, Abhishek Jin, Yaochu Piao, Haiyin Ge, Hongwei Zhang, Qiang Neural and Evolutionary Computing In this paper, we scale evolutionary algorithms to high-dimensional optimization problems that deceptively possess a low effective dimensionality (certain dimensions do not significantly affect the objective function). To this end, an instantiation of the multiform optimization paradigm is presented, where multiple low-dimensional counterparts of a target high-dimensional task are generated via random embeddings. Since the exact relationship between the auxiliary (low-dimensional) tasks and the target is a priori unknown, a multiform evolutionary algorithm is developed for unifying all formulations into a single multi-task setting. The resultant joint optimization enables the target task to efficiently reuse solutions evolved across various low-dimensional searches via cross-form genetic transfers, hence speeding up overall convergence characteristics. To validate the overall efficacy of our proposed algorithmic framework, comprehensive experimental studies are carried out on well-known continuous benchmark functions as well as a set of practical problems in the hyper-parameter tuning of machine learning models and deep learning models in classification tasks and Predator-Prey games, respectively. |
| title | Multiform Evolution for High-Dimensional Problems with Low Effective Dimensionality |
| topic | Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2401.00168 |