A Network Theoretic Analysis of Evolutionary Algorithms

Network theoretic analyses have been shown to be extremely useful in multiple fields and applications. We propose this approach to study the dynamic behavior of evolutionary algorithms, the first such analysis to the best of our knowledge. Evolving populations are represented as dynamic networks, and we show that changes in population characteristics can be recognized at the level of the networks representing successive generations, with implications for possible improvements in the evolutionary algorithm, e.g., in deciding when a population is prematurely converging, and when a reinitialization of the population may be beneficial to reduce computational effort. In this paper, we show that network-theoretic analyses of evolutionary algorithms help in: (i) studying community-level behaviors, and (ii) using graph properties and metrics to analyze evolutionary algorithms.

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