Evolving Multilayer Per eptrons

A Re ognition-based Alternative to Dis rimination-based Multi-Layer Per eptrons

Evolving Multilayer Neural Networks Using Permutation free Encoding Technique

In this paper we present an evolutionary system using genetic algorithm (GA) for evolving artificial neural networks (ANNs). Existing genetic algorithms for evolving ANNs suffer from the permutation problem as a result of recombination. Here we propose a novel encoding scheme for representing ANNs which avoids the permutation problem while efficiently evolving multilayer ANN architectures. The ...

Evolving Connectionist Systems Evolving Connectionist and Fuzzy-Connectionist Systems for On-line Adaptive Decision Making and Control

The paper contains a discussion material and preliminary experimental results on a new approach to building on-line, adaptive decision making and control systems. This approach is called evolving connectionist systems (ECOS). ECOS evolve through incremental, on-line learning. They can accommodate any new input data, including new features, new classes, etc. New connections and new neurons are c...

Evolving Fuzzy Neural Networks for Adaptive, On-line Intelligent Agents and Systems

This paper discusses and illustrates one paradigm of neuro-fuzzy techniques for building on-line, adaptive intelligent agents and systems. This approach is called evolving connectionist systems (ECOS). ECOS evolve through incremental, on-line learning, both supervised and unsupervised. They can accommodate new input data including new features, new classes, etc. The ECOS framework is presented ...

Chapter 7. Evolving Connectionist and Fuzzy - Connectionist Systems: Theory and Applications for Adaptive, On-line Intelligent Systems

The paper introduces one paradigm of neuro-fuzzy techniques and an approach to building on-line, adaptive intelligent systems. This approach is called evolving connectionist systems (ECOS). ECOS evolve through incremental, online learning, both supervised and unsupervised. They can accommodate new input data, including new features, new classes, etc. New connections and new neurons are created ...