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GECCO
2005
Springer
136views Optimization» more  GECCO 2005»
15 years 10 months ago
Computing the epistasis variance of large-scale traveling salesman problems
The interaction among variables of an optimization problem is known as epistasis, and its degree is an important measure for the nonlinearity of the problem. We address the proble...
Dong-il Seo, Byung Ro Moon
JMLR
2006
92views more  JMLR 2006»
15 years 5 months ago
Linear Programs for Hypotheses Selection in Probabilistic Inference Models
We consider an optimization problem in probabilistic inference: Given n hypotheses Hj, m possible observations Ok, their conditional probabilities pk j, and a particular Ok, selec...
Anders Bergkvist, Peter Damaschke, Marcel Lüt...
GECCO
2000
Springer
225views Optimization» more  GECCO 2000»
15 years 8 months ago
Solving Large Binary Quadratic Programming Problems by Effective Genetic Local Search Algorithm
A genetic local search (GLS) algorithm, which is a combination technique of genetic algorithm and local search, for the unconstrained binary quadratic programming problem (BQP) is...
Kengo Katayama, Masafumi Tani, Hiroyuki Narihisa
TEC
2002
163views more  TEC 2002»
15 years 4 months ago
Ants can solve constraint satisfaction problems
In this paper, we describe a new incomplete approach for solving constraint satisfaction problems (CSPs) based on the ant colony optimization (ACO) metaheuristic. The idea is to us...
Christine Solnon
ECCV
2006
Springer
16 years 7 months ago
Statistical Priors for Efficient Combinatorial Optimization Via Graph Cuts
Abstract. Bayesian inference provides a powerful framework to optimally integrate statistically learned prior knowledge into numerous computer vision algorithms. While the Bayesian...
Daniel Cremers, Leo Grady