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
Hybrid Pareto archived dynamically dimensioned search for multi-objective combinatorial optimization: application to water distribution network design
Masoud Asadzadeh,Bryan A. Tolson +1 more
TL;DR: Pareto archived dynamically dimensioned search (PA-DDS) has been modified to solve combinatorial multi-objective optimization problems and shows high potential for approximating the Pareto optimal front, especially with limited computational budget.
Proceedings ArticleDOI
Multi-objective Exploration of Compiler Optimizations for Real-Time Systems
TL;DR: This paper proposes the first adaptive WCET-aware compiler framework for an automatic search of compiler optimization sequences which yield highly optimized code and considers the worst-case execution time (WCET) which is a crucial parameter for real-time systems.
Posted Content
A Pareto-metaheuristic for a bi-objective winner determination problem in a combinatorial reverse auction
Tobias Buer,Herbert Kopfer +1 more
TL;DR: A metaheuristic approach is presented which integrates the greedy randomized adaptive search procedure, large neighborhood search, and self-adaptive parameter setting in order to find a competitive set of non-dominated solutions which outperforms existing heuristics.
Journal Article
Multi-Objective Evolutionary Algorithms and Pattern Search Methods for Circuit Design Problems
Tonio Biondi,Angelo Ciccazzo,Vincenzo Cutello,Santo D'Antona,Giuseppe Nicosia,Salvatore Spinella +5 more
TL;DR: The paper concerns the design of evolutionary algorithms and pattern search methods on two circuit design problems: the multi-objective optimization of an Operational Transconductance Amplifier and of a fifth-order leapfrog filter.
Journal ArticleDOI
A multi-objective test data generation approach for mutation testing of feature models
TL;DR: A multi-objective approach that includes a representation to the problem, search operators, and two objectives related to the number of test cases and dead mutants is extended to include a third objective: the pairwise coverage.