A Block-Coordinate Descent EMO Algorithm: Theoretical and Empirical Analysis
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866911956535869440 |
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| author | Doerr, Benjamin Knowles, Joshua Neumann, Aneta Neumann, Frank |
| author_facet | Doerr, Benjamin Knowles, Joshua Neumann, Aneta Neumann, Frank |
| contents | We consider whether conditions exist under which block-coordinate descent is asymptotically efficient in evolutionary multi-objective optimization, addressing an open problem. Block-coordinate descent, where an optimization problem is decomposed into $k$ blocks of decision variables and each of the blocks is optimized (with the others fixed) in a sequence, is a technique used in some large-scale optimization problems such as airline scheduling, however its use in multi-objective optimization is less studied. We propose a block-coordinate version of GSEMO and compare its running time to the standard GSEMO algorithm. Theoretical and empirical results on a bi-objective test function, a variant of LOTZ, serve to demonstrate the existence of cases where block-coordinate descent is faster. The result may yield wider insights into this class of algorithms. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2404_03838 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Block-Coordinate Descent EMO Algorithm: Theoretical and Empirical Analysis Doerr, Benjamin Knowles, Joshua Neumann, Aneta Neumann, Frank Neural and Evolutionary Computing Artificial Intelligence We consider whether conditions exist under which block-coordinate descent is asymptotically efficient in evolutionary multi-objective optimization, addressing an open problem. Block-coordinate descent, where an optimization problem is decomposed into $k$ blocks of decision variables and each of the blocks is optimized (with the others fixed) in a sequence, is a technique used in some large-scale optimization problems such as airline scheduling, however its use in multi-objective optimization is less studied. We propose a block-coordinate version of GSEMO and compare its running time to the standard GSEMO algorithm. Theoretical and empirical results on a bi-objective test function, a variant of LOTZ, serve to demonstrate the existence of cases where block-coordinate descent is faster. The result may yield wider insights into this class of algorithms. |
| title | A Block-Coordinate Descent EMO Algorithm: Theoretical and Empirical Analysis |
| topic | Neural and Evolutionary Computing Artificial Intelligence |
| url | https://arxiv.org/abs/2404.03838 |