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SPEA2: Improving the Strength Pareto Evolutionary Algorithm For Multiobjective Optimization
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The article was published on 2002-01-01 and is currently open access. It has received 1972 citations till now. The article focuses on the topics: Pareto principle & Multi-objective optimization.read more
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Journal ArticleDOI
Evolutionary algorithms for the selection of single nucleotide polymorphisms
TL;DR: This work presents an evolutionary algorithm for multiobjective SNP selection, modified version of the Strength-Pareto Evolutionary Algorithm (SPEA2) in Java, and approximates the set of optimal trade-off solutions for large problems in minutes.
Journal ArticleDOI
Preference-inspired coevolutionary algorithm with active diversity strategy for multi-objective multi-modal optimization
TL;DR: The proposed algorithm, denoted as MMPICEAg, adopts the popular coevolutionary framework of PICEAg and introduces a diversity-aware fitness assignment and a double-diversity archive update strategy to promote diversity in objective and decision spaces simultaneously.
Journal ArticleDOI
Comparative study of multi-objective evolutionary algorithms for hydraulic rehabilitation of urban drainage networks
TL;DR: The results show that the algorithms exhibit different behaviours in solving the hydraulic rehabilitation problem, and the multi-objective version of the HS algorithm provides better optimal solutions and clearly outperforms the other algorithms for this type of nondeterministic polynomial-time hard (NP-hard) problem.
Proceedings ArticleDOI
Simulation-based optimization of StarCraft tactical AI through evolutionary computation
Nasri Bin Othman,James Decraene,Wentong Cai,Nan Hu,Malcolm Yoke Hean Low,Alexandre Gouaillard +5 more
TL;DR: This work presents a modular framework for simulating AI vs. AI conflicts through an XML specification, whereby the behavioural and tactical components for each force can be varied and evolutionary computation can be employed on aspects of the scenario to yield superior solutions.
Book ChapterDOI
A Hypervolume-Based Optimizer for High-Dimensional Objective Spaces
Johannes Bader,Eckart Zitzler +1 more
TL;DR: This paper presents HypE (Hypervolume Estimation Algorithm for Multiobjective Optimization), by which the accuracy of the estimates and the available computing resources can be traded off; thereby, not only many-objective problems become feasible with hypervolume-based search, but also the runtime can be flexibly adapted.