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ICML
1998
IEEE
14 years 9 months ago
An Efficient Boosting Algorithm for Combining Preferences
We study the problem of learning to accurately rank a set of objects by combining a given collection of ranking or preference functions. This problem of combining preferences aris...
Yoav Freund, Raj D. Iyer, Robert E. Schapire, Yora...
EVOW
2006
Springer
14 years 4 days ago
On Interactive Evolution Strategies
Abstract. In this paper we discuss Evolution Strategies within the context of interactive optimization. Different modes of interaction will be classified and compared. A focus will...
Ron Breukelaar, Michael Emmerich, Thomas Bäck
GECCO
2010
Springer
157views Optimization» more  GECCO 2010»
13 years 10 months ago
Mirrored variants of the (1, 4)-CMA-ES compared on the noiseless BBOB-2010 testbed
Derandomization by means of mirrored samples has been recently introduced to enhance the performances of (1, λ)Evolution-Strategies (ESs) with the aim of designing fast and robus...
Anne Auger, Dimo Brockhoff, Nikolaus Hansen
ECAI
2004
Springer
14 years 1 months ago
Learning Techniques for Automatic Algorithm Portfolio Selection
The purpose of this paper is to show that a well known machine learning technique based on Decision Trees can be effectively used to select the best approach (in terms of efficien...
Alessio Guerri, Michela Milano
GECCO
2004
Springer
107views Optimization» more  GECCO 2004»
14 years 1 months ago
An Evolution Strategy Using a Continuous Version of the Gray-Code Neighbourhood Distribution
Abstract. We derive a continuous probability distribution which generates neighbours of a point in an interval in a similar way to the bitwise mutation of a Gray code binary string...
Jonathan E. Rowe, Dzena Hidovic