Conference Proceeding

Towards Explainable Metaheuristic: Mining Surrogate Fitness Models for Importance of Variables

Details

Citation

Singh M, Brownlee AEI & Cairns D (2022) Towards Explainable Metaheuristic: Mining Surrogate Fitness Models for Importance of Variables. In: TBC. GECCO '22:, Boston, USA, 09.07.2022-13.07.2022. New York: ACM. https://doi.org/10.1145/3520304.3533966

Abstract
Metaheuristic search algorithms look for solutions that either max-imise or minimise a set of objectives, such as cost or performance. However most real-world optimisation problems consist of nonlin-ear problems with complex constraints and conflicting objectives. The process by which a GA arrives at a solution remains largely unexplained to the end-user. A poorly understood solution will dent the confidence a user has in the arrived at solution. We propose that investigation of the variables that strongly influence solution quality and their relationship would be a step toward providing an explanation of the near-optimal solution presented by a meta-heuristic. Through the use of four benchmark problems we use the population data generated by a Genetic Algorithm (GA) to train a surrogate model, and investigate the learning of the search space by the surro-gate model. We compare what the surrogate has learned after being trained on population data generated after the first generation and contrast this with a surrogate model trained on the population data from all generations. We show that the surrogate model picks out key characteristics of the problem as it is trained on population data from each generation. Through mining the surrogate model we can build a picture of the learning process of a GA, and thus an explanation of the solution presented by the GA. The aim being to build trust and confidence in the end-user about the solution presented by the GA, and encourage adoption of the model. CCS CONCEPTS • Theory of computation → Models of learning; Theory of randomized search heuristics.

Keywords
genetic algorithms; explainability; interpretable; surrogate model; fitness function; optimization

Notes
Output Status: Forthcoming

StatusAccepted
FundersDatalab
URLhttp://hdl.handle.net/1893/34231
PublisherACM
Place of publicationNew York
ISBN978-1-4503-9268-6
ConferenceGECCO '22:
Conference locationBoston, USA
Dates

People (2)

People

Dr Sandy Brownlee
Dr Sandy Brownlee

Senior Lecturer in Computing Science, Computing Science and Mathematics - Division

Dr David Cairns
Dr David Cairns

Lecturer, Computing Science