Preference-Inspired Coevolutionary Algorithms for Many-Objective Optimization

The simultaneous optimization of many objectives (in excess of 3), in order to obtain a full and satisfactory set of tradeoff solutions to support a posteriori decision making, remains a challenging problem. The concept of coevolving a family of decision-maker preferences together with a population...

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Vydáno v:	IEEE transactions on evolutionary computation Ročník 17; číslo 4; s. 474 - 494
Hlavní autoři:	Rui Wang, Purshouse, Robin C., Fleming, Peter J.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York, NY IEEE 01.08.2013 Institute of Electrical and Electronics Engineers
Témata:	Algorithmics. Computability. Computer arithmetics Applied sciences Approximation algorithms Coevolution Computer science; control theory; systems evolutionary algorithms Evolutionary computation Exact sciences and technology many-objective optimization Monte Carlo methods Optimization Search problems Theoretical computing Vectors Dominating set Pareto optimum Decision support system Decision making Evolutionary algorithm Performance characteristic Multiobjective programming evolutionary algorithms Dominance many-objective optimization Randomized algorithm Coevolution Optimization Search algorithm Genetic algorithm Preference
ISSN:	1089-778X, 1941-0026
On-line přístup:	Získat plný text
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