A Block-Coordinate Descent EMO Algorithm: Theoretical and Empirical Analysis
<|reference_start|>A Block-Coordinate Descent EMO Algorithm: Theoretical and Empirical Analysis: 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.<|reference_end|>
@article{doerr2024a,
    title={A Block-Coordinate Descent EMO Algorithm: Theoretical and Empirical
  Analysis},
    author={Benjamin Doerr, Joshua Knowles, Aneta Neumann, Frank Neumann},
    journal={GECCO '24: Proceedings of the Genetic and Evolutionary Computation
  Conference, 493 - 501, 2024. ACM},
    year={2024},
    doi={10.1145/3638529.3654169},
    archivePrefix={arXiv},
    eprint={2404.03838},
    primaryClass={cs.NE cs.AI}
}