Randomization tests
C4.5: programs for machine learning
C4.5: programs for machine learning
LEARNABLE EVOLUTION MODEL: Evolutionary Processes Guided by Machine Learning
Machine Learning - Special issue on multistrategy learning
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Controlling Crossover through Inductive Learning
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
The Niched Pareto Genetic Algorithm 2 Applied to the Design of Groundwater Remediation Systems
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Speeding Up Evolution through Learning: LEM
Proceedings of the IIS'2000 Symposium on Intelligent Information Systems
Multiobjective Genetic Algorithms for Pump Scheduling in Water Supply
Selected Papers from AISB Workshop on Evolutionary Computing
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Optimal design of water distribution system by multiobjective evolutionary methods
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Evolutionary multiobjective optimization in watershed water quality management
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Meta-Modeling in Multiobjective Optimization
Multiobjective Optimization
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Using datamining techniques to help metaheuristics: a short survey
HM'06 Proceedings of the Third international conference on Hybrid Metaheuristics
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The design of large scale water distribution systems is a very difficult optimisation problem which invariably requires the use of time-expensive simulations within the fitness function. The need to accelerate optimisation for such problems has not so far been seriously tackled. However, this is a very important issue, since as MOEAs become more and more recognised as the ‘industry standard' technique for water system design, the demands placed on such systems (larger and larger water networks) will quickly meet with problems of scaleup. Meanwhile, LEM (Learnable Evolution Model') has appeared in the Machine Learning literature, and provides a general approach to integrating machine learning into evolutionary search. Published results using LEM show very great promise in terms of finding near-optimal solutions with significantly reduced numbers of evaluations. Here we introduce LEMMO (Learnable Evolution Model for Multi-Objective optimization), which is a multi-objective adaptation of LEM, and we apply it to certain problems commonly used as benchmarks in the water systems community. Compared with NSGA-II, we find that LEMMO both significantly improves performance, and significantly reduces the number of evaluations needed to reach a given target. We conclude that the general approach used in LEMMO is a promising direction for meeting the scale-up challenges in multiobjective water system design.