Open Access
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
A hybrid NSGA-II and VNS for solving a bi-objective no-wait flexible flowshop scheduling problem
TL;DR: Computational experiments and comparisons show that the proposed NSGA-II + VNS algorithm generates better or competitive results than the existing NS GA-II and SPEA-II for the no-wait flexible flow shop scheduling problem with sequence-dependent setup times to simultaneous minimizing the makespan and mean tardiness criterion.
BookDOI
Pattern Mining with Evolutionary Algorithms
Sebastin Ventura,Jos Mara Luna +1 more
TL;DR: A completely new problem in the pattern mining field, mining of exceptional relationships between patterns, is discussed, in this problem the goal is to identify patterns which distribution is exceptionally different from the distribution in the complete set of data records.
Journal ArticleDOI
Multiple Reference Points-Based Decomposition for Multiobjective Feature Selection in Classification: Static and Dynamic Mechanisms
TL;DR: A new decomposition approach with two mechanisms (static and dynamic) based on multiple reference points under the multiobjective evolutionary algorithm based on decomposition (MOEA/D) framework to address the above-mentioned difficulties of feature selection.
Journal Article
ε-PAL: an active learning approach to the multi-objective optimization problem
TL;DR: e-PAL reduces the amount of computations and the number of samples from the design space required to meet the user's desired level of accuracy and improves significantly over a state-of-the-art multi-objective optimization method.
Posted Content
Evolutionary Many-Objective Optimization Based on Adversarial Decomposition
TL;DR: This paper develops an adversarial decomposition method for many-objective optimization, which leverages the complementary characteristics of different subproblem formulations within a single paradigm.