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Bioinspired algorithms and complex systems
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DOI：10.1016/j.jocs.2017.11.010.png)
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Abstract
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Bioinspired algorithms are search, optimization, and learning techniques whose functioning is based on some metaphor of a biological process. Prominent examples include evolutionary algorithms and swarm intelligence methods. The practical application of these techniques to real-world problems typically involves orchestrating the interplay among different algorithmic components in order to attain synergistic search capabilities. This is a common theme in complex systems, in which the whole is more than the sum of the parts due to the complex interaction patterns among system components, giving rise to emergent properties not anticipated at the base level. Indeed, such systems are prevalent in many contexts, both natural (biological systems, ecosystems, etc.) and artificial (social networks, finance markets, etc.). Analyzing and understanding such systems is not only of the foremost interest, but also constitutes in general a formidable task requiring powerful tools. Metaheuristics in general and bioinspired algorithms in particular can greatly contribute to this end. Furthermore, their intrinsic complex nature makes them prone to be also subject of analysis using a complex-system perspective. (C) 2017 Published by Elsevier B.V.
Keywords:
Complex systems
Bioinspired algorithms
Metaheuristics
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