On the influence of different randomization and complex network analysis for differential evolution

Document Type

Conference Proceeding

Department or Administrative Unit

Computer Science

Publication Date



This research deals with the hybridization of the chaos driven heuristics concept and complex networks framework for evolutionary algorithms. This paper aims on the experimental investigations on the influence of different randomization types for chaos-driven Differential Evolution (DE) through the analysis of complex network as a record of population dynamics. The population is visualized as an evolving complex network, which exhibits non-trivial features. Complex network attributes such as adjacency graph gives interconnectivity, centralities give the overview of convergence and stagnation, clustering coefficient gives diversity of population whereas other attributes like network density, average number of neighbors within the population shows efficiency of the network. Experiments were performed for two different DE strategies, four different randomization types and two simple test functions.


This article was originally published in 2016 IEEE Congress on Evolutionary Computation (CEC). The full-text article from the publisher can be found here.

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2016 IEEE Congress on Evolutionary Computation (CEC)


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