Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy

By Joshua D. Knowles, David Wolfe Corne. Evolutionary Computation, 2000

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Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/b7d602c7abcf0d4fef40c12ebfd9947de9e85062

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Citations in the corpus

Response surface approximation of pareto optimal front in multi-objective optimization (2004, background): “Some of the latest ones include strength Pareto evolutionary algorithm (Zitzler and Thiele) [6], Pareto archived evolutionary strategies (PAES by Knowles and Corne) [7], elitist nondominated sorting genetic algorithm (NSGA-II by Deb et al.)”
MOEA/D with Adaptive Weight Adjustment (2014, methodology): “, 2001), the Pareto-envelope-based selection algorithm (PESA; Knowles and Corne, 2000) and its enhanced version PESA-II (Corne et al.”
An Evolutionary Many-Objective Optimization Algorithm Based on Dominance and Decomposition (2015, methodology): “The Pareto-based EMO approach (see [16], [17], [21]), whose basic idea is to compare solutions according to Pareto dominance relation and density estimation, is the first to bear the brunt.”
A Survey of Multi-Objective Optimization in Wireless Sensor Networks: Metrics, Algorithms, and Open Problems (2016, methodology): “algorithm-II (MOMGA-II) [222], the Bayesian optimization algorithm (BOA) [223], the hierarchical Bayesian optimization algorithm (HBOA) [224], the Pareto archive evolution strategy (PAES) [225], the Pareto envelope-based selection”
The Measure of Pareto Optima (2003, methodology): “Other methods involve archiving or updating solutions that are nondominated [11], hence indirectly maximize this scalar.”
Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling (2003, background): “Knowles and Corne [23] combined their Pareto archived evolution strategy (PAES [9], [ 11 ]) with a crossover operation for designing a memetic PAES (M-PAES).”
Solving Multiobjective Optimization Problems Using an Artificial Immune System (2005, methodology): “In order to enforce a uniform distribution of nondominated solutions that cover the entire Pareto front of a problem, we implemented the adaptive grid proposed by Knowles and Corne [28] (see Figure 2).”
Reducing the run-time complexity of multiobjective EAs: The NSGA-II and other algorithms (2003, methodology): “This is usually accomplished with a huge elite within the population (e.g., NSGA-II, DMOEA), or by an explicit archive separate from the population in which the elite is stored (e.g., PAES [ 17 ] and SPEA [28]).”
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.”
MOPSO: a proposal for multiple objective particle swarm optimization (2002, methodology): “This technique is inspired on the external file used with the Pareto Archive Evolution Strategy (PAES) [ 8 ].”
Multi-Objective Particle Swarm Optimization with time variant inertia and acceleration coefficients (2007, methodology): “MOPSO uses method inspired by [17] for maintaining diversity.”
Particle swarm optimization method in multiobjective problems (2002, methodology): “The benchmark problems tha t were used are d ~ m e d in [8, 21]:”
M-PAES: a memetic algorithm for multiobjective optimization (2000, methodology): “The resultant child is accepted only if it is nondominated with respect to the entire global archive, and it resides in a less crowded region (grid location [18]) than at least one of its parents.”
A robotic multidimensional directed evolution approach applied to fluorescent voltage reporters (2018, neutral): “…combinations of membrane localization and fluorescence brightness along the Pareto frontier 20 (i.e., the set of cells that dominate other cells in at least one parameter), as previously used to computationally evaluate progress in the context of multiobjective evolutionary optimisation 21–23 (Fig.”
PDE: a Pareto-frontier differential evolution approach for multi-objective optimization problems (2001, methodology): “Most recently, Knowles and Corne (Knowles and Corne 1999; Knowles and Corne 2000) proposed a simple Evolution Strategies (ES), (1+1)-ES, known as the Pareto Archived Evolution Strategy (PAES) that keeps a record of limited non-dominated individuals.”
One decade of multi-objective calibration approaches in hydrological modelling: a review (2010, background): “…Evolutionary Algorithm (SPEA; Zitzler & Thiele, 1999) and its successor SPEA II (Zitzler et al., 2001), the Pareto Archive Evolution Strategy (PAES; Knowles & Corne, 2000), the Nondominated Sorting Genetic Algorithm II (NSGA II; Deb et al., 2002), the Pareto Envelope-based Selection Algorithm…”