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GECCO
2006
Springer
170views Optimization» more  GECCO 2006»
13 years 11 months ago
How an optimal observer can collapse the search space
Many metaheuristics have difficulty exploring their search space comprehensively. Exploration time and efficiency are highly dependent on the size and the ruggedness of the search...
Christophe Philemotte, Hugues Bersini
NIPS
2007
13 years 9 months ago
Collective Inference on Markov Models for Modeling Bird Migration
We investigate a family of inference problems on Markov models, where many sample paths are drawn from a Markov chain and partial information is revealed to an observer who attemp...
Daniel Sheldon, M. A. Saleh Elmohamed, Dexter Koze...
GECCO
2008
Springer
120views Optimization» more  GECCO 2008»
13 years 9 months ago
A robust evolutionary framework for multi-objective optimization
Evolutionary multi-objective optimization (EMO) methodologies, suggested in the beginning of Nineties, focussed on the task of finding a set of well-converged and well-distribute...
Kalyanmoy Deb
CEC
2010
IEEE
13 years 9 months ago
Iterated local search vs. hyper-heuristics: Towards general-purpose search algorithms
An important challenge within hyper-heuristic research is to design search methodologies that work well, not only across different instances of the same problem, but also across di...
Edmund K. Burke, Timothy Curtois, Matthew R. Hyde,...
PODS
2008
ACM
159views Database» more  PODS 2008»
14 years 8 months ago
Approximation algorithms for clustering uncertain data
There is an increasing quantity of data with uncertainty arising from applications such as sensor network measurements, record linkage, and as output of mining algorithms. This un...
Graham Cormode, Andrew McGregor