Evolutionary Algorithms for Solving Multi-Objective Problems

By Carlos Artemio Coello Coello, D. V. Veldhuizen, Gary B. Lamont. 2002

6,825 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

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Citations in the corpus

Benchmark and Survey of Automated Machine Learning Frameworks (2019, methodology): “An alternative to SMBO are evolutionary algorithms (Coello et al., 2007).”
Survey on Automated Machine Learning (2019, methodology): “An alternative to SMBO are evolutionary algorithms (Coello et al., 2007).”
A historical review of evolutionary learning methods for Mamdani-type fuzzy rule-based systems: Designing interpretable genetic fuzzy systems (2011, neutral): “Both the (2 + 2)-PAES and the NSGA-II EMO algorithms are considered to put the learning task into effect by optimizing the same two criteria.”
An EMO Algorithm Using the Hypervolume Measure as Selection Criterion (2005, background): “Coello Coello, Van Veldhuizen and Lamont [2] described it as the Lebesgue measure Λ of the union of hypercubes a i defined by a non-dominated point m i and a reference point x ref : Zitzler and Thiele note that this measure prefers convex regions to nonconvex ones [3].”
Finding Knees in Multi-objective Optimization (2004, background): “For comprehensive books on the topic, the reader is referred to [8, 4].”
Localized Weighted Sum Method for Many-Objective Optimization (2018, background): “candidate solutions in JointS; 19 Update ideal and nadir vectors, and normalize JointF into [0,1]; 20 Calculate the angle θ sw ij between F(x) i and weight w j, obtaining an angle matrix θ sw; 21 For each solution, e.”
Comparing state-of-the-art evolutionary multi-objective algorithms for long-term groundwater monitoring design (2005, methodology): “[31].”
Innovization: innovating design principles through optimization (2006, background): “5) is the thickness of discs in mm, F ∈ [600, 1000] (in steps of 10) is the actuating force in N and Z ∈ [2, 10] (in steps of one) is the number of friction surfaces (or discs).”
Quality Assessment of Pareto Set Approximations (2008, background): “In many application domains, it is useful to approximate the set of Paretooptimal solutions, cf. (Ehrgott and Gandibleux, 2000; Deb, 2001; Coello Coello et al., 2002).”
A comparative study of differential evolution variants for global optimization (2006, methodology): “Categories and Subject Descriptors G.1.6 [Mathematics of Computing]: Numerical Analysis—global optimization, unconstrained optimization; J.2 [Computer Applications]: Physical Sciences and Engineering—Engineering General Terms Algorithms Keywords Differential Evolution, Global Optimization”
Memetic algorithms and memetic computing optimization: A literature review (2012, methodology): “This mechanism has been employed by popular evolutionary algorithms for multi-objective optimization, see [33, 44, 45].”
A survey on multi-objective evolutionary algorithms for many-objective problems (2014, methodology): “In general, MOEAs differ on the ﬁtness assignment method, but most of them are part of a family, called Pareto-based, which use the Pareto dominance concept as the foundation to discriminate solutions to guide their search [17].”
Solving Multiobjective Optimization Problems Using an Artificial Immune System (2005, methodology): “Our proposal is based on the clonal selection principle and is validated using several test functions and metrics, following the standard methodology adopted in the EMO community [ 7 , 11].”