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GECCO
2007
Springer
212views Optimization» more  GECCO 2007»
15 years 6 months ago
An experimental analysis of evolution strategies and particle swarm optimisers using design of experiments
The success of evolutionary algorithms (EAs) depends crucially on finding suitable parameter settings. Doing this by hand is a very time consuming job without the guarantee to ...
Oliver Kramer, Bartek Gloger, Andreas Goebels
GECCO
2007
Springer
185views Optimization» more  GECCO 2007»
15 years 6 months ago
An informed convergence accelerator for evolutionary multiobjective optimiser
A novel optimisation accelerator deploying neural network predictions and objective space direct manipulation strategies is presented. The concept of directing the search through ...
Salem F. Adra, Ian Griffin, Peter J. Fleming
GECCO
2007
Springer
163views Optimization» more  GECCO 2007»
15 years 6 months ago
Choice and development
The process of development creates a phenotype from one or more genotypes of an individual through interaction with an environment. The opportunity for development to choose a phe...
Arthur M. Farley
GECCO
2004
Springer
15 years 5 months ago
Evolving a Roving Eye for Go
Go remains a challenge for artificial intelligence. Currently, most machine learning methods tackle Go by playing on a specific fixed board size, usually smaller than the standa...
Kenneth O. Stanley, Risto Miikkulainen
GECCO
2010
Springer
233views Optimization» more  GECCO 2010»
15 years 4 months ago
Evolutionary-based conflict-free scheduling of collective communications on spidergon NoCs
The Spidergon interconnection network has become popular recently in multiprocessor systems on chips. To the best of our knowledge, algorithms for collective communications (CC) h...
Jirí Jaros, Vaclav Dvorak