Use this URL to cite or link to this record in EThOS:
Title: Constructivist and spiking neural learning classifier systems
Author: Howard, Gerard David
ISNI:       0000 0004 2739 3545
Awarding Body: University of the West of England, Bristol
Current Institution: University of the West of England, Bristol
Date of Award: 2011
Availability of Full Text:
Access from EThOS:
This thesis investigates the use of self-adaptation and neural constructivism within a neural Learning Classifier System framework. The system uses a classifier structure whereby each classifier condition is represented by an artificial neural network, which is used to compute an action in response to an environmental stimulus. We implement this neural representation in two modem Learning Classifier Systems, XCS and XCSF. A classic problem in neural networks revolves around network topology considerations; how many neurons should the network consist of? How should we configure their topological arrangement and inter-neural connectivity patterns to ensure high performance? Similarly in Learning Classifier Systems, hand-tuning of parameters is sometimes necessary to achieve acceptable system performance. We employ a number of mechanisms to address these potential deficiencies. Neural Constructivism is utilised to automatically alter network topology to reflect the complexity of the environment. It is shown that appropriate internal classifier complexity emerges during learning at a rate controlled by the learner. The resulting systems are applied to real-valued, noisy simulated maze environments and a simulated robotics platform. The main areas of novelty include the first use of self-adaptive constructivism within XCSF, the first implementation of temporally-sensitive spiking classifier representations within this constructive XC SF, and the demonstration of temporal functionality of such representations in noisy continuous-valued and robotic environments.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available