Instance Space Analysis of Combinatorial Multi-objective Optimization Problems
E Yap, MA Munoz, K Smith-Miles, A Liefooghe
2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings | IEEE | Published : 2020
In recent years, there has been a continuous stream of development in evolutionary multi-objective optimization (EMO) algorithms. The large quantity of existing algorithms introduces difficulty in selecting suitable algorithms for a given problem instance. In this paper, we perform instance space analysis on discrete multi-objective optimization problems (MOPs) for the first time under three different conditions. We create visualizations of the relationship between problem instances and algorithm performance for instance features previously identified using decision trees, as well an independent feature selection. The suitability of these features in discriminating between algorithm performa..View full abstract
Related Projects (1)
Awarded by Australian Research Council
Funding was provided by the Australian Research Council through the Australian Laureate Fellowship FL140100012.