Toward the Online Visualisation of Algorithm Performance for Parameter Selection
Walker, DJ; Craven, MJ
Date: 8 March 2018
Journal
Sim K., Kaufmann P. (eds) Applications of Evolutionary Computation. EvoApplications 2018. Lecture Notes in Computer Science
Publisher
Springer Verlag
Publisher DOI
Abstract
A visualisation method is presented that is intended to assist evolutionary algorithm users with the parametrisation of their algorithms. The visualisation method presents the convergence and diversity properties such that different parametrisations can be easily compared, and poor performing parameter sets can be easily identified and ...
A visualisation method is presented that is intended to assist evolutionary algorithm users with the parametrisation of their algorithms. The visualisation method presents the convergence and diversity properties such that different parametrisations can be easily compared, and poor performing parameter sets can be easily identified and discarded. The efficacy of the visualisation is presented using a set of benchmark optimisation problems from the literature, as well as a benchmark water distribution network design problem. Results show that it is possible to observe the different performance caused by different parametrisations. Future work discusses the potential of this visualisation within an online tool that will enable a user to discard poor parametrisations as they execute to free up resources for better ones.
Engineering
Faculty of Environment, Science and Economy
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