Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
Convergence Criteria for Genetic Algorithms
SIAM Journal on Computing
Genetic algorithms with multi-parent recombination
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Detecting communities in time-evolving proximity networks
NSW '11 Proceedings of the 2011 IEEE Network Science Workshop
Clustering-Based Adaptive Crossover and Mutation Probabilities for Genetic Algorithms
IEEE Transactions on Evolutionary Computation
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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.