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» The State Problem for Evolutionary Testing
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GECCO
2006
Springer
134views Optimization» more  GECCO 2006»
15 years 1 months ago
Reference point based multi-objective optimization using evolutionary algorithms
: Evolutionary multi-objective optimization (EMO) methodologies have been amply applied to find a representative set of Pareto-optimal solutions in the past decade and beyond. Alth...
Kalyanmoy Deb, J. Sundar
GECCO
2007
Springer
186views Optimization» more  GECCO 2007»
15 years 4 months ago
ICSPEA: evolutionary five-axis milling path optimisation
ICSPEA is a novel multi-objective evolutionary algorithm which integrates aspects from the powerful variation operators of the Covariance Matrix Adaptation Evolution Strategy (CMA...
Jörn Mehnen, Rajkumar Roy, Petra Kersting, To...
ATAL
2008
Springer
14 years 12 months ago
Analysis of an evolutionary reinforcement learning method in a multiagent domain
Many multiagent problems comprise subtasks which can be considered as reinforcement learning (RL) problems. In addition to classical temporal difference methods, evolutionary algo...
Jan Hendrik Metzen, Mark Edgington, Yohannes Kassa...
ICASSP
2011
IEEE
14 years 1 months ago
A study of the effect of emotional state upon text-independent speaker identification
In this paper we evaluate the effect of the emotional state of a speaker when text-independent speaker identification is performed. The spectral features used for speaker recogni...
Marius Vasile Ghiurcau, Corneliu Rusu, Jaakko Asto...
GECCO
2009
Springer
107views Optimization» more  GECCO 2009»
15 years 4 months ago
Swarming along the evolutionary branches sheds light on genome rearrangement scenarios
A genome rearrangement scenario describes a series of chromosome fusion, fission, and translocation operations that suffice to rewrite one genome into another. Exact algorithmic ...
Nikolay Vyahhi, Adrien Goëffon, Macha Nikolsk...