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AUSAI
2009
Springer
14 years 2 months ago
A Distance Metric for Evolutionary Many-Objective Optimization Algorithms Using User-Preferences
In this paper we propose to use a distance metric based on user-preferences to efficiently find solutions for many-objective problems. In a user-preference based algorithm a decis...
Upali K. Wickramasinghe, Xiaodong Li
GECCO
2009
Springer
146views Optimization» more  GECCO 2009»
14 years 2 months ago
Using a distance metric to guide PSO algorithms for many-objective optimization
In this paper we propose to use a distance metric based on user-preferences to efficiently find solutions for manyobjective problems. We use a particle swarm optimization (PSO) a...
Upali K. Wickramasinghe, Xiaodong Li
STOC
2004
ACM
126views Algorithms» more  STOC 2004»
14 years 8 months ago
Bypassing the embedding: algorithms for low dimensional metrics
The doubling dimension of a metric is the smallest k such that any ball of radius 2r can be covered using 2k balls of raThis concept for abstract metrics has been proposed as a na...
Kunal Talwar
PR
2006
76views more  PR 2006»
13 years 7 months ago
Extending the relevant component analysis algorithm for metric learning using both positive and negative equivalence constraints
Relevant component analysis (RCA) is a recently proposed metric learning method for semi-supervised learning applications. It is a simple and efficient method that has been applie...
Dit-Yan Yeung, Hong Chang
GECCO
2008
Springer
117views Optimization» more  GECCO 2008»
13 years 8 months ago
Is "best-so-far" a good algorithmic performance metric?
In evolutionary computation, experimental results are commonly analyzed using an algorithmic performance metric called best-so-far. While best-so-far can be a useful metric, its u...
Nathaniel P. Troutman, Brent E. Eskridge, Dean F. ...