ORCID
- Craven, Matthew: 0000-0001-9522-6173
Abstract
The comprehension of the Evolutionary Algorithm (EA) search process is often eluded by challenges of transparency inherent to \textit{black-box} EAs, thus affecting algorithm enhancement and hyper-parameter optimisation. In this work, we develop algorithm insight by introducing the Population Dynamics Plot (PopDP). PopDP is a novel and intuitive visualisation capable of visualising the population of solutions, the parent-offspring lineage, solution perturbation operators, and the search process journey. We apply PopDP to NSGA-II to demonstrate the insight attained and the effectiveness of PopDP for visualising algorithm search on a series of discrete dual- and many-objective knapsack problems of different complexities, and our results demonstrate that the method can be used to produce a visualisation in which the lineage of solutions can be clearly seen. We also consider the efficacy of the proposed explainable visualisation against emerging approaches to benchmarking explainable AI methods and consider the accessibility of the resulting visualisations.
DOI
10.1145/3520304.3533984
Publication Date
2022-07-19
Publication Title
GECCO'22: Proceedings of the Genetic and Evolutionary Computation Conference Companion
First Page
1794
Last Page
1802
Embargo Period
2022-07-22
Organisational Unit
School of Engineering, Computing and Mathematics
Keywords
Evolutionary Computation, Explainability, Many-Objective Optimisation, Multi-Objective Optimisation, Visualisation
Recommended Citation
Walter, M. J., Walker, D., & Craven, M. (2022) 'An Explainable Visualisation of the Evolutionary Search Process', GECCO'22: Proceedings of the Genetic and Evolutionary Computation Conference Companion, , pp. 1794-1802. Available at: https://doi.org/10.1145/3520304.3533984
