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The road to everywhere: Evolution, complexity and progress in natural and artificial systems

Miconi, Thomas (2008)
Ph.D. thesis, University of Birmingham.

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Evolution is notorious for its creative power, but also for giving rise to complex, unpredictable dynamics. As a result, practitioners of artificial evolution have encountered difficulties in predicting, analysing, or even understanding the outcome of their experiments. In particular, the concept of evolutionary "progress" (whether in the sense of performance increase or complexity growth) has given rise to much debate and confusion. After a careful description of the mechanisms of evolution and natural selection, we provide usable concepts of performance and progress in coevolution. In particular, we introduce a distinction between three types of progress: local, historical, and global, which we suggest underlies much of the confusion that surrounds coevolutionary dynamics. Similarly, we provide a comprehensive answer to the question of whether an "arrow of complexity" exists in evolution. We introduce several methods to detect and analyse performance and progress in coevolutionary experiments. We propose a statistical measure (Fitness Transmission) to detect the presence of adaptive Darwinian evolution in a reproducing population, based solely on genealogic records; we also point out the limitations of a popular method (the Bedau-Packard statistics of evolutionary activity) for this purpose. To test and illustrate our results, we implement a rich experimental system, inspired by the seminal work of Karl Sims, in which virtual creatures can evolve and interact under various conditions in a physically realistic three-dimensional (3D) environment. To our knowledge, this is the first complete reimplementation and extension of Sims' results. We later extend this system with the introduction of physical combat between creatures, also a first. Finally, we introduce Evosphere, an open, planet-like environment in which 3D artificial creatures interact, reproduce and evolve freely. We conclude our discussion by using Fitness Transmission to detect the onset of adaptive evolution in this system.

Type of Work:Ph.D. thesis.
Supervisor(s):Channon, Alastair D.
School/Faculty:Schools (1998 to 2008) > School of Computer Science
Department:Computer Science
Subjects:QA75 Electronic computers. Computer science
Institution:University of Birmingham
Library Catalogue:Check for printed version of this thesis
ID Code:148
This unpublished thesis/dissertation is copyright of the author and/or third parties. The intellectual property rights of the author or third parties in respect of this work are as defined by The Copyright Designs and Patents Act 1988 or as modified by any successor legislation. Any use made of information contained in this thesis/dissertation must be in accordance with that legislation and must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the permission of the copyright holder.
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