The Pareto Envelope-Based Selection Algorithm for Multi-objective Optimisation

By David Wolfe Corne, Joshua D. Knowles, Martin John Oates. Parallel Problem Solving from Nature, 2000

983 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/951117b91bb0221dc2f513f82660be4b1739551e

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Citations in the corpus

Failure of Pareto-based MOEAs: does non-dominated really mean near to optimal? (2001, methodology): “Pareto Archived Evolution Strategy (PAES) [Knowles 1999], Strength Pareto Evolutionary Algorithm (SPEA)[Zitzler 1999], Pareto Envelope-based Selection Algorithm (PESA)[Corne 2000], and (Fast Elitist) Non-dominated Sorting Genetic Algorithm (NSGA-II)[Deb 2000], are such sophisticated MOEAs.”
On the Evolutionary Optimization of Many Conflicting Objectives (2007, methodology): “Note that the PAES method can be made to respect property 4 by changing the requirement on density to , as was done in PESA.”
A Survey on Multiobjective Evolutionary Algorithms for the Solution of the Portfolio Optimization Problem and Other Finance and Economics Applications (2013, methodology): “10) Pareto Envelope-Based Selection Algorithm (PESA) [23]: PESA uses, like PAES [22], a small internal population”
Techniques for highly multiobjective optimisation: some nondominated points are better than others (2007, methodology): “Examples are PAES and PESA [ 7 ,19]; in these cases, data-structures and associated algorithms are employed to partition the k-objective fitness space into ‘hyperboxes’, so that a record can be maintained of how many individuals (in the current estimate of the Pareto front, in the population, or bot”
Controlled Elitist Non-dominated Sorting Genetic Algorithms for Better Convergence (2001, methodology): “Spe i ally, we use a geometri distribution for this purpose: ni = rni 1; (1) where ni is the maximum number of allowed individuals in the i-th front and r (< 1) is the redu tion rate.”
A Survey of Multi-Objective Optimization in Wireless Sensor Networks: Metrics, Algorithms, and Open Problems (2016, methodology): “…optimization algorithm (HBOA) [224], the Pareto archive evolution strategy (PAES) [225], the Pareto envelope-based selection algorithm (PESA) [226], the Pareto envelope-based selection algorithm-II (PESA-II) [227], multi-objective differential evolution (MODE) [196], multi-objective…”
Reducing the run-time complexity of multiobjective EAs: The NSGA-II and other algorithms (2003, methodology): “Additionally, directions for reducing the running times of PDE [1], PESA [ 8 ], PAES [17], DMOEA [20], SPEA2 [27], and the algorithm of [25] will be given.”
Learning Accurate and Interpretable Classifiers Using Optimal Multi-Criteria Rules (2013, background): “Points that are not dominated by any other point in this space compose the Pareto Frontier [Corne et al. 2000].”