The MODES Toolbox: Measurements of Open-Ended Dynamics in Evolving Systems.

Autor: Dolson EL; Michigan State University, BEACON Center for the Study of Evolution in Action, Department of Computer Science and Engineering Program in Ecology, Evolutionary Biology, and Behavior. dolsonem@msu.edu., Vostinar AE; Grinnell College, Department of Computer Science. vostinar@grinnell.edu., Wiser MJ; Michigan State University, BEACON Center for the Study of Evolution in Action Program in Ecology, Evolutionary Biology, and Behavior. mwiser@msu.edu., Ofria C; Michigan State University, BEACON Center for the Study of Evolution in Action, Department of Computer Science and Engineering Program in Ecology, Evolutionary Biology, and Behavior. ofria@msu.edu.
Jazyk: angličtina
Zdroj: Artificial life [Artif Life] 2019 Winter; Vol. 25 (1), pp. 50-73.
DOI: 10.1162/artl_a_00280
Abstrakt: Building more open-ended evolutionary systems can simultaneously advance our understanding of biology, artificial life, and evolutionary computation. In order to do so, however, we need a way to determine when we are moving closer to this goal. We propose a set of metrics that allow us to measure a system's ability to produce commonly-agreed-upon hallmarks of open-ended evolution: change potential, novelty potential, complexity potential, and ecological potential. Our goal is to make these metrics easy to incorporate into a system, and comparable across systems so that we can make coherent progress as a field. To this end, we provide detailed algorithms (including C++ implementations) for these metrics that should be easy to incorporate into existing artificial life systems. Furthermore, we expect this toolbox to continue to grow as researchers implement these metrics in new languages and as the community reaches consensus about additional hallmarks of open-ended evolution. For example, we would welcome a measurement of a system's potential to produce major transitions in individuality. To confirm that our metrics accurately measure the hallmarks we are interested in, we test them on two very different experimental systems: NK landscapes and the Avida digital evolution platform. We find that our observed results are consistent with our prior knowledge about these systems, suggesting that our proposed metrics are effective and should generalize to other systems.
Databáze: MEDLINE
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