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NIPS
1993
13 years 9 months ago
Structural and Behavioral Evolution of Recurrent Networks
This paper introduces GNARL, an evolutionary program which induces recurrent neural networks that are structurally unconstrained. In contrast to constructive and destructive algor...
Gregory M. Saunders, Peter J. Angeline, Jordan B. ...
EVOW
2009
Springer
13 years 5 months ago
Conquering the Needle-in-a-Haystack: How Correlated Input Variables Beneficially Alter the Fitness Landscape for Neural Networks
Abstract. Evolutionary algorithms such as genetic programming and grammatical evolution have been used for simultaneously optimizing network architecture, variable selection, and w...
Stephen D. Turner, Marylyn D. Ritchie, William S. ...
IEAAIE
2005
Springer
14 years 1 months ago
Predicting Construction Litigation Outcome Using Particle Swarm Optimization
Construction claims are normally affected by a large number of complex and interrelated factors. It is highly desirable for the parties to a dispute to know with some certainty how...
Kwok-wing Chau
GECCO
2003
Springer
153views Optimization» more  GECCO 2003»
14 years 29 days ago
SEPA: Structure Evolution and Parameter Adaptation in Feed-Forward Neural Networks
Abstract. In developing algorithms that dynamically changes the structure and weights of ANN (Artificial Neural Networks), there must be a proper balance between network complexit...
Paulito P. Palmes, Taichi Hayasaka, Shiro Usui
IJCSA
2007
100views more  IJCSA 2007»
13 years 7 months ago
Using Artificial Neural networks for the modelling of a distillation column
The main aim of this paper is to establish a reliable model both for the steady-state and unsteady-state regimes of a nonlinear process. The use of this model should reflect the t...
Yahya Chetouani