An analysis of the interface between evolutionary algorithm operators and problem features for water resources problems. A case study in water distribution network design. (July 2015) - McClymont, K.; Keedwell, E.; Savic, D.
Abstract: Evolutionary Algorithms (EAs) have been widely employed to solve water resources problems for nearly two decades with much success. However, recent research in hyperheuristics has raised the possibility of developing optimisers that adapt to the characteristics of the problem being solved. In order to select appropriate operators for such optimisers it is necessary to first understand the interaction between operator and problem. This paper explores the concept of EA operator behaviour in real world applications through the empirical study of performance using water distribution networks (WDN) as a case study. Artificial networks are created to embody specific WDN features which are then used to evaluate the impact of network features on operator performance. The method extracts key attributes of the problem which are encapsulated in the natural features of a WDN, such as topologies and assets, on which different EA operators can be tested. The method is demonstrated using small exemplar networks designed specifically so that they isolate individual features. A set of operators are tested on these artificial networks and their behaviour characterised. This process provides a systematic and quantitative approach to establishing detailed information about an algorithm's suitability to optimise certain types of problem. The experiment is then repeated on real-world inspired networks and the results are shown to fit with the expected results. Highlights: Explores the relationship between search operators and problem spaces in water resources problems. Standard mutation robust to most changes within a water distribution network. Problem specific operators less robust to looping in networks than mutation. Combinations of operators with problem specific operators improve performance markedly.