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

By Thomas Miconi


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

Topics: QA75 Electronic computers. Computer science
Year: 2008
OAI identifier:

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