Dual network representation applied to the evolution of neural controllers

Pujol, João Carlos Figueira; Poli, Roberto

doi:10.1007/bfb0040815

Cited by 2 publications

(1 citation statement)

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“…The genetic programming paradigm was developed from the genetic algorithm approach initially to generate computer programs to solve problems and for the discovery of suitable architectures for neural networks . In contrast to genetic algorithms, where a problem must be encoded as a fixed length vector, genetic programming can provide solutions of varying size .…”

Section: The Methodsmentioning

confidence: 99%

Genetic Programming for the Induction of Decision Trees to Model Ecotoxicity Data

Buontempo

Wang

Mwense

et al. 2005

J. Chem. Inf. Model.

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Automatic induction of decision trees and production rules from data to develop structure-activity models for toxicity prediction has recently received much attention, and the majority of methodologies reported in the literature are based upon recursive partitioning employing greedy searches to choose the best splitting attribute and value at each node. These approaches can be successful; however, the greedy search will necessarily miss regions of the search space. Recent literature has demonstrated the applicability of genetic programming to decision tree induction to overcome this problem. This paper presents a variant of this novel approach, using fewer mutation options and a simpler fitness function, demonstrating its utility in inducing decision trees for ecotoxicity data, via a case study of two data sets giving improved accuracy and generalization ability over a popular decision tree inducer.

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Section: The Methodsmentioning

confidence: 99%

Genetic Programming for the Induction of Decision Trees to Model Ecotoxicity Data

Buontempo

Wang

Mwense

et al. 2005

J. Chem. Inf. Model.

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Neuromorphic controllers

Pourboghrat

Proceedings of the 28th IEEE Conference on Decision and Control

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Dcpartmsnt OfmEtriml Engineering"het This paper investigates the feasibility of utilizing neural Aa such networks for the design of learning controllers. controllers, the design of both regdators and trackers are considered. M o m r , the relationship of these learning controllera to conventional adaptive controllers are invwtiated. This enables one to develop a learning meth &logy fo r these networks which guarantees the stability of the tmining pmce# under mild conditions, and reqaires "alinformationabouttheplantdynamim. Introdu.c;finaThe design of learnin controllers for dynamic q"s is a my impmtant and chdnging research area [l-21. Despite many sophtticated control strategies, the pressing need for high precision and impxwed performance still raises the important question of how to design a generic control architecture for a givm srpstem which is capable of learning the mapping from the sgpte"r desired output to its corresponding reqaind input Realizing the learning

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Dual network representation applied to the evolution of neural controllers

Cited by 2 publications

References 10 publications

Genetic Programming for the Induction of Decision Trees to Model Ecotoxicity Data

Genetic Programming for the Induction of Decision Trees to Model Ecotoxicity Data

Neuromorphic controllers

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