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GECCO
2007
Springer
124views Optimization» more  GECCO 2007»
14 years 14 days 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
GECCO
2007
Springer
147views Optimization» more  GECCO 2007»
14 years 14 days ago
A comparison of evaluation methods in coevolution
In this research, we compare four different evaluation methods in coevolution on the Majority Function problem. The size of the problem is selected such that an evaluation against...
Ting-Shuo Yo, Edwin D. de Jong
GECCO
2007
Springer
162views Optimization» more  GECCO 2007»
14 years 14 days ago
Genetic evolution of hierarchical behavior structures
The development of coherent and dynamic behaviors for mobile robots is an exceedingly complex endeavor ruled by task objectives, environmental dynamics and the interactions within...
Brian G. Woolley, Gilbert L. Peterson
GECCO
2007
Springer
136views Optimization» more  GECCO 2007»
14 years 14 days ago
Configuring an evolutionary tool for the inventory and transportation problem
EVITA, standing for Evolutionary Inventory and Transportation Algorithm, aims to be a commercial tool to address the problem of minimising both the transport and inventory costs o...
Anna Esparcia-Alcázar, Lidia Lluch-Revert, ...
GECCO
2007
Springer
180views Optimization» more  GECCO 2007»
14 years 14 days ago
Support vector regression for classifier prediction
In this paper we introduce XCSF with support vector prediction: the problem of learning the prediction function is solved as a support vector regression problem and each classifie...
Daniele Loiacono, Andrea Marelli, Pier Luca Lanzi
GECCO
2007
Springer
150views Optimization» more  GECCO 2007»
14 years 14 days ago
Overcoming hierarchical difficulty by hill-climbing the building block structure
The Building Block Hypothesis suggests that Genetic Algorithms (GAs) are well-suited for hierarchical problems, where efficient solving requires proper problem decomposition and a...
David Iclanzan, Dan Dumitrescu
GECCO
2007
Springer
184views Optimization» more  GECCO 2007»
14 years 14 days ago
Evolving kernels for support vector machine classification
While support vector machines (SVMs) have shown great promise in supervised classification problems, researchers have had to rely on expert domain knowledge when choosing the SVM&...
Keith Sullivan, Sean Luke
GECCO
2007
Springer
159views Optimization» more  GECCO 2007»
14 years 14 days ago
Discrimination of metabolic flux profiles using a hybrid evolutionary algorithm
Studying metabolic fluxes is a crucial aspect of understanding biological phenotypes. However, it is often not possible to measure these fluxes directly. As an alternative, fluxom...
Stefan Bleuler, Eckart Zitzler
GECCO
2007
Springer
212views Optimization» more  GECCO 2007»
14 years 14 days ago
Controlling overfitting with multi-objective support vector machines
Recently, evolutionary computation has been successfully integrated into statistical learning methods. A Support Vector Machine (SVM) using evolution strategies for its optimizati...
Ingo Mierswa
GECCO
2007
Springer
215views Optimization» more  GECCO 2007»
14 years 14 days ago
Finding safety errors with ACO
Model Checking is a well-known and fully automatic technique for checking software properties, usually given as temporal logic formulae on the program variables. Most model checke...
Enrique Alba, J. Francisco Chicano