When a genetic algorithm outperforms hill-climbing
Theoretical Computer Science
Comparison studies of LS_SVM and SVM on modeling for fermentation processes
ICNC'09 Proceedings of the 5th international conference on Natural computation
Benefits of a population: five mechanisms that advantage population-based algorithms
IEEE Transactions on Evolutionary Computation
An adaptive genetic algorithm for the minimal switching graph problem
EvoCOP'05 Proceedings of the 5th European conference on Evolutionary Computation in Combinatorial Optimization
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The dynamics of a genetic algorithm undergoing ranking selection, mutation, and two-point crossover for the ones-counting problem is studied using a statistical mechanics approach. This approach has been used previously to study this problem, but with uniform crossover. Two-point crossover induces additional linkage between nearby loci, which changes the dynamics significantly. To account for this linkage, the evolution of the autocorrelation function is incorporated into a model of the dynamics. This complicates the analysis and requires several additional approximations to be made. However, the model we derive is shown to capture the main features of the dynamics and is in good agreement with simulations