Abstract
Persistence is a characteristic that distinguishes between different types of objects in our world: alive or dead, stable or unstable. The fact that some objects are able to persist, both in static and dynamic ways, and others are not, appears to be a cornerstone of why interesting things happen in our universe. Hence, the class of dynamical systems in which persistence plays an important role should be a very interesting class of systems. Persistence also appears to be an important aspect of the dynamics displayed by evolutionary search algorithms. In this paper I shall introduce a new measure of static persistence that is more sophisticated than the measure used in previous work. It is shown how this new measure may be useful in distinguishing between dynamics that are performing search and those that are not.
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References
N. Jacobi. Minimal Simulations for Evolutionary Robotics. PhD thesis, School of Cognitive and Computing Sciences, Sussex University, 1998.
M. McIlhagga, P. Husbands, and R. Ives. A comparison of search techniques on a wing-box optimisation problem. In Parallel Problem Solving from Nature IV, 1996.
O. Sharpe. Continuing beyond nfl: Dissecting real world problems. In GECCO-99: Proceedings of the Genetic and Evolutionary Computation Conference, 1999.
O. Sharpe. Evolution at the origins of life: Modelling simple persistent replicating objects. In European Conference of Artificial Life, 1999.
O. Sharpe. Persistence, search and autopoiesis. In GECCO-99: Proceedings of the Genetic and Evolutionary Computation Conference, 1999.
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© 2000 Springer-Verlag Berlin Heidelberg
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Sharpe, O. (2000). Introducing a New Persistence Measure. In: Schoenauer, M., et al. Parallel Problem Solving from Nature PPSN VI. PPSN 2000. Lecture Notes in Computer Science, vol 1917. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45356-3_15
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DOI: https://doi.org/10.1007/3-540-45356-3_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-41056-0
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