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GECCO
2007
Springer
124views Optimization» more  GECCO 2007»
14 years 2 months ago
Fitness-proportional negative slope coefficient as a hardness measure for genetic algorithms
The Negative Slope Coefficient (nsc) is an empirical measure of problem hardness based on the analysis of offspring-fitness vs. parent-fitness scatterplots. The nsc has been teste...
Riccardo Poli, Leonardo Vanneschi
IPPS
2003
IEEE
14 years 3 months ago
Parallel Heterogeneous Genetic Algorithms for Continuous Optimization
In this paper we address the physical parallelization of a very efficient genetic algorithm (GA) known as gradual distributed real-coded GA (GD-RCGA). This search model naturally...
Enrique Alba, Francisco Luna, Antonio J. Nebro
CEC
2010
IEEE
13 years 11 months ago
A modified genetic algorithm for matching building sets with the histograms of forces
Abstract--This paper presents an approach to the task of locating a group of buildings based solely on their relative spatial relationships. This situation can occur in the problem...
Andrew R. Buck, James M. Keller, Marjorie Skubic
GECCO
2004
Springer
140views Optimization» more  GECCO 2004»
14 years 3 months ago
Keeping the Diversity with Small Populations Using Logic-Based Genetic Programming
We present a new method of Logic-Based Genetic Programming (LBGP). Using the intrinsic mechanism of backtracking in Prolog, we utilize large individual programs with redundant clau...
Ken Taniguchi, Takao Terano
CP
2007
Springer
14 years 4 months ago
Boosting Probabilistic Choice Operators
Probabilistic Choice Operators (PCOs) are convenient tools to model uncertainty in CP. They are useful to implement randomized algorithms and stochastic processes in the concurrent...
Matthieu Petit, Arnaud Gotlieb