Sciweavers

744 search results - page 15 / 149
» Parallelization of Multi-objective Evolutionary Algorithms U...
Sort
View
PDCN
2004
13 years 9 months ago
K-Means VQ algorithm using a low-cost parallel cluster computing
It is well-known that the time and memory necessary to create a codebook from large training databases have hindered the vector quantization based systems for real applications. T...
Paulo Sergio Lopes de Souza, Alceu de Souza Britto...
PPSN
2010
Springer
13 years 6 months ago
General Lower Bounds for the Running Time of Evolutionary Algorithms
Abstract. We present a new method for proving lower bounds in evolutionary computation based on fitness-level arguments and an additional condition on transition probabilities bet...
Dirk Sudholt
ICDAR
2003
IEEE
14 years 28 days ago
A Low-Cost Parallel K-Means VQ Algorithm Using Cluster Computing
In this paper we propose a parallel approach for the Kmeans Vector Quantization (VQ) algorithm used in a twostage Hidden Markov Model (HMM)-based system for recognizing handwritte...
Alceu de Souza Britto Jr., Paulo Sergio Lopes de S...
GECCO
2008
Springer
163views Optimization» more  GECCO 2008»
13 years 8 months ago
Embedded evolutionary multi-objective optimization for worst case robustness
In Multi-Objective Problems (MOPs) involving uncertainty, each solution might be associated with a cluster of performances in the objective space depending on the possible scenari...
Gideon Avigad, Jürgen Branke
GECCO
2007
Springer
200views Optimization» more  GECCO 2007»
14 years 1 months ago
Adaptive variance scaling in continuous multi-objective estimation-of-distribution algorithms
Recent research into single–objective continuous Estimation– of–Distribution Algorithms (EDAs) has shown that when maximum–likelihood estimations are used for parametric d...
Peter A. N. Bosman, Dirk Thierens