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AI
2006
Springer
15 years 4 months ago
Backward-chaining evolutionary algorithms
Starting from some simple observations on a popular selection method in Evolutionary Algorithms (EAs)--tournament selection--we highlight a previously-unknown source of inefficien...
Riccardo Poli, William B. Langdon
GECCO
2007
Springer
154views Optimization» more  GECCO 2007»
15 years 10 months ago
Finding critical backbone structures with genetic algorithms
This paper introduces the concept of a critical backbone as a minimal set of variables or part of the solution necessary to be within the basin of attraction of the global optimum...
Adam Prügel-Bennett
GECCO
2005
Springer
130views Optimization» more  GECCO 2005»
15 years 10 months ago
Hybrid real-coded mutation for genetic algorithms applied to graph layouts
In this paper we introduce an application of real-coded genetic algorithms to the problem of consistent graph layout and exploring the role of mutation for this particular problem...
Dana Vrajitoru, Jason DeBoni
CSB
2004
IEEE
112views Bioinformatics» more  CSB 2004»
15 years 8 months ago
Inferring Genetic Networks from Microarray Data
In theory, it should be possible to infer realistic genetic networks from time series microarray data. In practice, however, network discovery has proved problematic. The three ma...
Shawn Martin, George Davidson, Elebeoba E. May, Je...
GECCO
2007
Springer
124views Optimization» more  GECCO 2007»
15 years 8 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