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GECCO
2007
Springer
190views Optimization» more  GECCO 2007»
14 years 1 months ago
Interactive evolutionary multi-objective optimization and decision-making using reference direction method
In this paper, we borrow the concept of reference direction approach from the multi-criterion decision-making literature and combine it with an EMO procedure to develop an algorit...
Kalyanmoy Deb, Abhishek Kumar
GECCO
2010
Springer
207views Optimization» more  GECCO 2010»
14 years 13 days ago
Generalized crowding for genetic algorithms
Crowding is a technique used in genetic algorithms to preserve diversity in the population and to prevent premature convergence to local optima. It consists of pairing each offsp...
Severino F. Galán, Ole J. Mengshoel
GECCO
2008
Springer
238views Optimization» more  GECCO 2008»
13 years 8 months ago
Using multiple offspring sampling to guide genetic algorithms to solve permutation problems
The correct choice of an evolutionary algorithm, a genetic representation for the problem being solved (as well as their associated variation operators) and the appropriate values...
Antonio LaTorre, José Manuel Peña, V...
GECCO
2007
Springer
176views Optimization» more  GECCO 2007»
14 years 1 months ago
Two-level of nondominated solutions approach to multiobjective particle swarm optimization
In multiobjective particle swarm optimization (MOPSO) methods, selecting the local best and the global best for each particle of the population has a great impact on the convergen...
M. A. Abido
CORR
2010
Springer
127views Education» more  CORR 2010»
13 years 7 months ago
Mean field for Markov Decision Processes: from Discrete to Continuous Optimization
We study the convergence of Markov Decision Processes made of a large number of objects to optimization problems on ordinary differential equations (ODE). We show that the optimal...
Nicolas Gast, Bruno Gaujal, Jean-Yves Le Boudec