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» Evolutionary Computation for Modeling and Optimization
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GECCO
2007
Springer
213views Optimization» more  GECCO 2007»
16 years 1 months ago
Genetically programmed learning classifier system description and results
An agent population can be evolved in a complex environment to perform various tasks and optimize its job performance using Learning Classifier System (LCS) technology. Due to the...
Gregory Anthony Harrison, Eric W. Worden
GECCO
2010
Springer
191views Optimization» more  GECCO 2010»
15 years 11 months ago
Toward comparison-based adaptive operator selection
Adaptive Operator Selection (AOS) turns the impacts of the applications of variation operators into Operator Selection through a Credit Assignment mechanism. However, most Credit ...
Álvaro Fialho, Marc Schoenauer, Michè...
GECCO
2006
Springer
159views Optimization» more  GECCO 2006»
15 years 10 months ago
Multi-step environment learning classifier systems applied to hyper-heuristics
Heuristic Algorithms (HA) are very widely used to tackle practical problems in operations research. They are simple, easy to understand and inspire confidence. Many of these HAs a...
Javier G. Marín-Blázquez, Sonia Schu...
BMCBI
2006
120views more  BMCBI 2006»
15 years 7 months ago
Projections for fast protein structure retrieval
Background: In recent times, there has been an exponential rise in the number of protein structures in databases e.g. PDB. So, design of fast algorithms capable of querying such d...
Sourangshu Bhattacharya, Chiranjib Bhattacharyya, ...
JACM
2006
99views more  JACM 2006»
15 years 6 months ago
Finding a maximum likelihood tree is hard
Abstract. Maximum likelihood (ML) is an increasingly popular optimality criterion for selecting evolutionary trees [Felsenstein 1981]. Finding optimal ML trees appears to be a very...
Benny Chor, Tamir Tuller