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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Book ChapterDOI
SPAM: Set Preference Algorithm for Multiobjective Optimization
TL;DR: The experimental results demonstrate that SPAM provides full flexibility with respect to user preferences and is effective in optimizing according to the specified preferences, and offers a new perspective on preference-guided multiobjective search.
Journal ArticleDOI
Trustworthy Genetic Programming-Based Synthesis of Analog Circuit Topologies Using Hierarchical Domain-Specific Building Blocks
TL;DR: MOJITO is a system that performs structural synthesis of analog circuits, returning designs that are trustworthy by construction, and generalizes to other problem domains which have accumulated structural domain knowledge, such as robotic structures, car assemblies, and modeling biological systems.
Journal ArticleDOI
MOEA/D with uniform decomposition measurement for many-objective problems
TL;DR: A novel weight vectors initialization method based on the uniform decomposition measurement is introduced to obtain uniform weight vectors in any amount and the modified Tchebycheff decomposition approach is used in MOEA/D-UDM to alleviate the inconsistency between the weight vector of subproblem and the direction of its optimal solution in the Tche byCheff decompositions approach.
Journal ArticleDOI
Including preferences into a multiobjective evolutionary algorithm to deal with many-objective engineering optimization problems
TL;DR: A new preference relation based on a reference point approach that induces a finer order on vectors of objective space than that achieved by the Pareto dominance relation is introduced, appropriate to cope with problems having a high number of objectives.
Journal ArticleDOI
Solving a multi-objective dynamic stochastic districting and routing problem with a co-evolutionary algorithm
TL;DR: An enhanced multi-objective dynamic stochastic algorithm called PICEA-g-mr, i.e., the preference-inspired co-evolutionary algorithm using mating restriction, is developed for the problem and outperforms two state-of-the-art MOEAs on randomly generated instances.