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GECCO
2005
Springer
154views Optimization» more  GECCO 2005»
14 years 2 months ago
Genetic algorithms using low-discrepancy sequences
The random number generator is one of the important components of evolutionary algorithms (EAs). Therefore, when we try to solve function optimization problems using EAs, we must ...
Shuhei Kimura, Koki Matsumura
GECCO
2008
Springer
186views Optimization» more  GECCO 2008»
13 years 9 months ago
A pareto following variation operator for fast-converging multiobjective evolutionary algorithms
One of the major difficulties when applying Multiobjective Evolutionary Algorithms (MOEA) to real world problems is the large number of objective function evaluations. Approximate...
A. K. M. Khaled Ahsan Talukder, Michael Kirley, Ra...
GECCO
2008
Springer
124views Optimization» more  GECCO 2008»
13 years 9 months ago
Introducing MONEDA: scalable multiobjective optimization with a neural estimation of distribution algorithm
In this paper we explore the model–building issue of multiobjective optimization estimation of distribution algorithms. We argue that model–building has some characteristics t...
Luis Martí, Jesús García, Ant...
EOR
2007
101views more  EOR 2007»
13 years 8 months ago
Searching for multiobjective preventive maintenance schedules: Combining preferences with evolutionary algorithms
Heavy industry maintenance facilities at aircraft service centers or railroad yards must contend with scheduling preventive maintenance tasks to ensure critical equipment remains ...
Gang Quan, Garrison W. Greenwood, Donglin Liu, Xia...
GECCO
2009
Springer
126views Optimization» more  GECCO 2009»
14 years 1 months ago
Improving NSGA-II with an adaptive mutation operator
The performance of a Multiobjective Evolutionary Algorithm (MOEA) is crucially dependent on the parameter setting of the operators. The most desired control of such parameters pre...
Arthur Gonçalves Carvalho, Aluizio F. R. Ar...