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» Indicator-Based Selection in Multiobjective Search
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CEC
2008
IEEE
14 years 2 months ago
Evolutionary Multi-objective Simulated Annealing with adaptive and competitive search direction
— In this paper, we propose a population-based implementation of simulated annealing to tackle multi-objective optimisation problems, in particular those of combinatorial nature....
Hui Li, Dario Landa Silva
EMO
2001
Springer
109views Optimization» more  EMO 2001»
14 years 1 days ago
Specification of Genetic Search Directions in Cellular Multi-objective Genetic Algorithms
When we try to implement a multi-objective genetic algorithm (MOGA) with variable weights for finding a set of Pareto optimal solutions, one difficulty lies in determining appropri...
Tadahiko Murata, Hisao Ishibuchi, Mitsuo Gen
GECCO
2007
Springer
156views Optimization» more  GECCO 2007»
14 years 1 months ago
Techniques for highly multiobjective optimisation: some nondominated points are better than others
The research area of evolutionary multiobjective optimization (EMO) is reaching better understandings of the properties and capabilities of EMO algorithms, and accumulating much e...
David W. Corne, Joshua D. Knowles
HIS
2007
13 years 9 months ago
Pareto-based Multi-Objective Machine Learning
—Machine learning is inherently a multiobjective task. Traditionally, however, either only one of the objectives is adopted as the cost function or multiple objectives are aggreg...
Yaochu Jin
TEC
2008
98views more  TEC 2008»
13 years 7 months ago
A Fast Incremental Hypervolume Algorithm
When hypervolume is used as part of the selection or archiving process in a multiobjective evolutionary algorithm, it is necessary to determine which solutions contribute the least...
Lucas Bradstreet, R. Lyndon While, Luigi Barone