A Block-Coordinate Descent EMO Algorithm: Theoretical and Empirical Analysis

• URL: http://arxiv.org/abs/2404.03838v2
• Date: Thu, 11 Apr 2024 00:13:05 GMT
• Title: A Block-Coordinate Descent EMO Algorithm: Theoretical and Empirical Analysis
• Authors: Benjamin Doerr, Joshua Knowles, Aneta Neumann, Frank Neumann,
• Abstract summary: We consider whether conditions exist under which block-coordinate descent is efficient in evolutionary multi-objective optimization. We propose a block-coordinate version of GSEMO and compare its running time to the standard GSEMO algorithm.
• Score: 17.89683724761454
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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