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cs.NE2026
A New Approach to Characterising Optimisation Problems Using Programmatic Representation and Complexity Measures
Marcus Gallagher, Katherine M. Malan
Characterising optimisation problem instances is a fundamental part of understanding the behaviour and performance of different algorithms as well as providing information for algo…
cs.NE2025
A Standardized Benchmark Set of Clustering Problem Instances for Comparing Black-Box Optimizers
Diederick Vermetten, Catalin-Viorel Dinu, Marcus Gallagher
One key challenge in optimization is the selection of a suitable set of benchmark problems. A common goal is to find functions which are representative of a class of real-world opt…
cs.NE2024
Analyzing the Runtime of the Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) on the Concatenated Trap Function
Yukai Qiao, Marcus Gallagher
The Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) is a state of the art evolutionary algorithm that leverages linkage learning to efficiently exploit problem structure. B…