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» Short Term Unit-Commitment Using Genetic Algorithms
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ADVAI
2010
110views more  ADVAI 2010»
13 years 2 months ago
Using Genetic Algorithms to Represent Higher-Level Planning in Simulation Models of Conflict
The focus of warfare has shifted from the Industrial Age to the Information Age, as encapsulated by the term Network Enabled Capability. This emphasises information sharing, comma...
James Moffat, Susan Fellows
GECCO
2008
Springer
261views Optimization» more  GECCO 2008»
13 years 8 months ago
SSNNS -: a suite of tools to explore spiking neural networks
We are interested in engineering smart machines that enable backtracking of emergent behaviors. Our SSNNS simulator consists of hand-picked tools to explore spiking neural network...
Heike Sichtig, J. David Schaffer, Craig B. Laramee
GECCO
2009
Springer
142views Optimization» more  GECCO 2009»
14 years 10 days ago
Evolution, development and learning using self-modifying cartesian genetic programming
Self-Modifying Cartesian Genetic Programming (SMCGP) is a form of genetic programming that integrates developmental (self-modifying) features as a genotype-phenotype mapping. This...
Simon Harding, Julian Francis Miller, Wolfgang Ban...
IJCAI
1989
13 years 9 months ago
Training Feedforward Neural Networks Using Genetic Algorithms
Multilayered feedforward neural networks possess a number of properties which make them particularly suited to complex pattern classification problems. However, their application ...
David J. Montana, Lawrence Davis
WSC
2007
13 years 10 months ago
Optimal scheduling of probabilistic repetitive projects using completed unit and genetic algorithms
In this paper we introduce the completed unit algorithm (CU-AL), a probabilistic scheduling methodology for repetitive projects. The algorithm has two main advantages, simplicity ...
Chachrist Srisuwanrat, Photios G. Ioannou