Abstract
Complex systems are characterized by a level of nonlinear coupling among system components. A graph representation in general, and complex networks in specific, have been extensively used for understanding complex systems. In this chapter, we offer a gentle introduction to complex networks, a discussion of their properties and metrics to measure these properties, and algorithms for network generation that could be used to generate synthetic networks with real-world or theoretical properties. We then introduce a variety of evolutionary algorithms where each individual in a population inhibits a node in a complex network. This representation limits the other individuals that each individual is allowed to interact with. This localized interaction warrants a discussion on the selection pressure that each network topology implicitly imposes on its population.
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Liu, J., Abbass, H.A., Tan, K.C. (2019). Complex Networks. In: Evolutionary Computation and Complex Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-60000-0_2
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DOI: https://doi.org/10.1007/978-3-319-60000-0_2
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