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Hybridising rule induction and multi-objective evolutionary search for optimising water distribution systems

In this article, we present our latest work with a hybrid multiobjective evolutionary algorithm called LEMMO (learnable evolution model for multiobjective optimization) which integrates machine learning into evolutionary search based on Michalski's "LEM" approach. The objective is to both improve the performance of the MOEA and to reduce the number of evaluations needed when used for optimising the design of water distribution networks (where evaluations are highly computationally costly). We compare LEMMO with NSGA-II and conclude that our approach is very promising for improved speed and quality in the water systems optimisation domain.