ORCID
- Craven, Matthew: 0000-0001-9522-6173
Abstract
This work assesses the efficacy of evolutionary algorithms (EAs) using an intuitive Multi-Dimensional Scaling (MDS) visualisation of the evolution of a population. We propose the use of Landmark Multi-Dimensional Scaling (LMDS) to overcome computational challenges inherent to visualising many-objective and complex problems with MDS. For the benchmark problems we tested, LMDS is akin to MDS visually, whilst requiring less than 1% of the time and memory necessary to produce an MDS visualisation of the same objective space solutions, leading to the possibility of online visualisations for multiand many-objective optimisation evaluation. Using multi- and many-objective problems from the DTLZ and WFG benchmark test suites, we analyse how Landmark MDS visualisations can offer far greater insight into algorithm performance than using traditional algorithm performance metrics such as hypervolume alone, and can be used to complement explicit performance metrics. Ultimately, this visualisation allows visual identification of problem features and assists the decision maker in making intuitive recommendations for algorithm parameters/operators for creating and testing better EAs to solve multi- and manyobjective problems.
DOI
10.1109/TEVC.2022.3157143
Publication Date
2022-03-07
Publication Title
IEEE Transactions on Evolutionary Computation
ISSN
1089-778X
Embargo Period
2022-04-22
Organisational Unit
School of Engineering, Computing and Mathematics
Keywords
Landmark multi-dimensional scaling, Many-objective optimisation, Multi-objective optimisation, Visualisation
Recommended Citation
Walter, M. J., Walker, D., & Craven, M. (2022) 'Visualising Population Dynamics to Examine Algorithm Performance', IEEE Transactions on Evolutionary Computation, . Available at: https://doi.org/10.1109/TEVC.2022.3157143
