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GECCO
2007
Springer
124views Optimization» more  GECCO 2007»
14 years 1 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
EMO
2006
Springer
161views Optimization» more  EMO 2006»
14 years 26 days ago
Design Issues in a Multiobjective Cellular Genetic Algorithm
In this paper we study a number of issues related to the design of a cellular genetic algorithm (cGA) for multiobjective optimization. We take as an starting point an algorithm fol...
Antonio J. Nebro, Juan José Durillo, Franci...
GECCO
2006
Springer
163views Optimization» more  GECCO 2006»
14 years 26 days ago
The quadratic multiple knapsack problem and three heuristic approaches to it
The quadratic multiple knapsack problem extends the quadratic knapsack problem with K knapsacks, each with its own capacity Ck. A greedy heuristic fills the knapsacks one at a tim...
Amanda Hiley, Bryant A. Julstrom
GECCO
2006
Springer
175views Optimization» more  GECCO 2006»
14 years 26 days ago
A comparative study of differential evolution variants for global optimization
In this paper, we present an empirical comparison of some Differential Evolution variants to solve global optimization problems. The aim is to identify which one of them is more s...
Efrén Mezura-Montes, Jesús Vel&aacut...
GECCO
2006
Springer
220views Optimization» more  GECCO 2006»
14 years 26 days ago
Comparing evolutionary algorithms on the problem of network inference
In this paper, we address the problem of finding gene regulatory networks from experimental DNA microarray data. We focus on the evaluation of the performance of different evoluti...
Christian Spieth, Rene Worzischek, Felix Streicher...