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» On the Brittleness of Evolutionary Algorithms
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IROS
2006
IEEE
193views Robotics» more  IROS 2006»
16 years 16 days ago
Adaptive Human-Robot Interaction System using Interactive EC
— We created a human-robot communication system that can adapt to user preferences that can easily change through communication. Even if any learning algorithms are used, evaluat...
Yuki Suga, Chihiro Endo, Daizo Kobayashi, T. Matsu...
GECCO
2004
Springer
132views Optimization» more  GECCO 2004»
15 years 12 months ago
Optimizing Topology and Parameters of Gene Regulatory Network Models from Time-Series Experiments
Abstract. In this paper we address the problem of finding gene regulatory networks from experimental DNA microarray data. Different approaches to infer the dependencies of gene r...
Christian Spieth, Felix Streichert, Nora Speer, An...
GECCO
2003
Springer
108views Optimization» more  GECCO 2003»
15 years 11 months ago
Evolution Strategies with Exclusion-Based Selection Operators and a Fourier Series Auxiliary Function
Abstract. To improve the efficiency of the currently known evolutionary algorithms, we have proposed two complementary efficiency speed-up strategies in our previous research work ...
Kwong-Sak Leung, Yong Liang
GECCO
2006
Springer
133views Optimization» more  GECCO 2006»
15 years 10 months ago
Selecting for evolvable representations
Evolutionary algorithms tend to produce solutions that are not evolvable: Although current fitness may be high, further search is impeded as the effects of mutation and crossover ...
Joseph Reisinger, Risto Miikkulainen
HIS
2008
15 years 8 months ago
Evaluating Ranking Composition Methods for Multi-Objective Optimization of Knowledge Rules
Most symbolic classifiers aim at building sets of rules with good coverage and precision. While this is suitable for most applications, they tend to neglect other desirable proper...
Rafael Giusti, Gustavo E. A. P. A. Batista, Ronald...