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
Pareto-based evolutionary computational approach for wireless sensor placement
TL;DR: FLEX starts with an initial population of simple WSNs and complexifies their topologies over generations, which keeps track of new genes through historical markings, which are used in later generations to assess two networks' compatibility and also to align genes during crossover.
Book ChapterDOI
A hybrid multi-objective evolutionary algorithm for the uncapacitated exam proximity problem
TL;DR: A hybrid Multi-Objective Evolutionary Algorithm is used to tackle the uncapacitated exam proximity problem and the resulting non dominated timetables are compared with those produced by other optimization methods using 15 public domain datasets.
Proceedings ArticleDOI
Multi-criteria optimization for mapping programs to multi-processors
TL;DR: This paper models tradeoffs between communication cost and the balance of processor workloads for the problem of mapping applications to processors in a multicore environment and formulate several query strategies for finding Pareto optimal and approximately Pare to optimal solutions to the mapping problem using a constraint solver as a time-bounded oracle.
Proceedings ArticleDOI
DBSCAN-based multi-objective niching to approximate equivalent pareto-subsets
Oliver Kramer,Holger Danielsiek +1 more
TL;DR: A niching method that approximates Pareto-optimal solutions with diversity mechanisms in objective and decision space, and an indicator for the adaptive control of clustering processes, and rake selection by the concept of adaptive corner points is introduced.
Book ChapterDOI
Combining Two Search Paradigms for Multi-objective Optimization: Two-Phase and Pareto Local Search
TL;DR: This chapter reviews metaheuristics for solving multi-objective combinatorial optimization problems, when no information about the decision maker’s preferences is available, that is, when problems are tackled in the sense of Pareto optimization.