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» Metaphor for learning: an evolutionary algorithm
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GECCO
2005
Springer
153views Optimization» more  GECCO 2005»
14 years 1 months ago
Evolving neural network ensembles for control problems
In neuroevolution, a genetic algorithm is used to evolve a neural network to perform a particular task. The standard approach is to evolve a population over a number of generation...
David Pardoe, Michael S. Ryoo, Risto Miikkulainen
GECCO
2007
Springer
171views Optimization» more  GECCO 2007»
14 years 1 months ago
Toward a better understanding of rule initialisation and deletion
A number of heuristics have been used in Learning Classifier Systems to initialise parameters of new rules, to adjust fitness of parent rules when they generate offspring, and ...
Tim Kovacs, Larry Bull
CEC
2007
IEEE
13 years 11 months ago
Evolving hypernetworks for pattern classification
Abstract-- Hypernetworks consist of a large number of hyperedges that represent higher-order features sampled from training patterns. Evolutionary algorithms have been used as a me...
Joo-Kyung Kim, Byoung-Tak Zhang
CEC
2011
IEEE
12 years 7 months ago
Oppositional biogeography-based optimization for combinatorial problems
Abstract—In this paper, we propose a framework for employing opposition-based learning to assist evolutionary algorithms in solving discrete and combinatorial optimization proble...
Mehmet Ergezer, Dan Simon
ICML
1998
IEEE
14 years 8 months ago
Q2: Memory-Based Active Learning for Optimizing Noisy Continuous Functions
This paper introduces a new algorithm, Q2, foroptimizingthe expected output ofamultiinput noisy continuous function. Q2 is designed to need only a few experiments, it avoids stron...
Andrew W. Moore, Jeff G. Schneider, Justin A. Boya...