TY - GEN
T1 - A network theoretic analysis of evolutionary algorithms
AU - Kuber, Karthik
AU - Card, Stuart W.
AU - Mehrotra, Kishan G.
AU - Mohan, Chilukuri K.
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
KW - Dynamic Networks
KW - Early Detection of Convergence
KW - Evolutionary Algorithms
KW - Genetic Algorithms
UR - http://www.scopus.com/inward/record.url?scp=84871538843&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84871538843&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-35380-2_68
DO - 10.1007/978-3-642-35380-2_68
M3 - Conference contribution
AN - SCOPUS:84871538843
SN - 9783642353796
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 585
EP - 593
BT - Swarm, Evolutionary, and Memetic Computing - Third International Conference, SEMCCO 2012, Proceedings
T2 - 3rd International Conference on Swarm, Evolutionary, and Memetic Computing, SEMCCO 2012
Y2 - 20 December 2012 through 22 December 2012
ER -