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Please use this identifier to cite or link to this item: http://hdl.handle.net/10059/419
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Title: Evolution and devolved action: towards the evolution of systems.
Authors: MacLeod, Christopher
McMinn, David
Reddipogu, Ann
Capanni, Niccolo Francesco
Maxwell, Grant M.
Keywords: Artificial neural networks
Issue Date: 2001
Publisher: Robert Gordon University
Citation: MACLEOD, C., MCMINN, D., REDDIPOGU, A. B., CAPANNI, N. F. and MAXWELL, G. M., 2001. Evolution and devolved action: towards the evolution of systems. In: Appendix B of MCMINN, D. Using evolutionary artificial neural networks to design hierarchical animat nervous systems, Ph. D. thesis. Aberdeen : Robert Gordon University.
Abstract: The Artificial Neural Networks group at the Robert Gordon University has, over the last six years, built up considerable knowledge and practical experience in Evolutionary Artificial Neural Networks. This experience started with the PhD project by C MacLeod [1] and continued with the work of D McMinn [2] which is due for submission in June 2001. These are being followed up by the work of research students A B Reddipogu and N F Capanni. Initial work concentrated on Neural Networks that could grow to fulfil their function. C MacLeod, in his thesis on this topic, proposed a model of a robotic control system to be used as a vehicle for further research. This model formed the basis of D McMinn’s project. The robotic control system [2, 3, 4] has a defined modular structure that enables the researcher to create networks for particular tasks and so allow the robot to function. McMinn has used this structure successfully as a basis to develop Evolutionary ANNs implementing Central Pattern Generators and Reflexes for robot locomotion. It has become apparent, over the course of these projects, that a network that can evolve into a modular structure without the need for designed partitioning would be the next step forward for the group’s research. This should allow the network to develop naturally and in an open-ended way without the need to artificially constrain it. Such an approach needs an evolutionary algorithm that can automatically and naturally evolve a “system”: that is, a modular network rather than a fully interconnected homogenous structure. No acceptable Genetic or Evolutionary Techniques are currently available to do this. The group therefore needed to look to nature and discover the reasons why natural systems allowed such modularity to evolve and how it might be exploited in the course of future work. It was felt that this work would be a culmination of the group’s research to date, both furthering the work on robotic control systems and also including ideas from previous practical work, by MacLeod and McMinn, in terms of Evolutionary Networks and Incremental Evolution (Embryological Algorithms) [1]. This is because such a network will need to evolve and to “grow” from a simple to complex structure. The research and ideas that lay the foundations for this work are contained in this report. The report also considers two related aspects (neural learning and neural function), but as explained above, it is the layout topology or wiring which is felt to be the most important topic. After all, complex systems may be built up from simple elements, such as transistors or gates (or indeed, at the most basic level, atoms), providing that they are interconnected in the correct manner. Therefore, to summarise: We are concerned with discovering an evolutionary method capable of evolving systems. To establish a method capable of this, we must look again at the action of biological evolution and compare it with current artificial evolutionary methods.
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