Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Multiprocessor systems-on-chip synthesis using multi-objective evolutionary computation
Proceedings of the 12th annual conference on Genetic and evolutionary computation
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Multiobjective Evolutionary Algorithms for Portfolio Management: A comprehensive literature review
Expert Systems with Applications: An International Journal
Multi-objective optimization using teaching-learning-based optimization algorithm
Engineering Applications of Artificial Intelligence
Computers and Operations Research
QAR-CIP-NSGA-II: A new multi-objective evolutionary algorithm to mine quantitative association rules
Information Sciences: an International Journal
Energy and locality aware load balancing in cloud computing
Integrated Computer-Aided Engineering
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We present an evolutionary approach to a difficult, multiobjective problem in groundwater quality management: how to pump-and-treat (PAT) contaminated groundwater to remove the most contaminant at the least cost. Although evolutionary multiobjective (EMO) techniques have been applied successfully to monitoring of groundwater quality and to containment of contaminated groundwater, our work is a first attempt to apply EMO to the long-term (ten year) remediation of contaminated water. We apply an improved version of the Niched Pareto GA (NPGA 2) to determine the pumping rates for up to fifteen fixed-location wells. The NPGA2 uses Pareto-rank-based tournament selection and criteria-space niching to find nondominated frontiers. With 15 well locations, the niched Pareto genetic algorithm is demonstrated to outperform both a single objective genetic algorithm (SGA) and enumerated random search (ERS) by generating a better tradeoff curve.