
Average Convergence Rate of Evolutionary Algorithms II: Continuous Optimization
A good convergence metric must satisfy two requirements: feasible in cal...
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On the convergence rate of some nonlocal energies
We study the rate of convergence of some nonlocal functionals recently c...
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Improving Convergence Rate Of IC3
IC3, a wellknown model checker, proves a property of a state system ξ b...
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Neurons learn slower than they think
Recent studies revealed complex convergence dynamics in gradientbased m...
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Noisy Optimization: Convergence with a Fixed Number of Resamplings
It is known that evolution strategies in continuous domains might not co...
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Novel Analysis of Population Scalability in Evolutionary Algorithms
Populationbased evolutionary algorithms (EAs) have been widely applied ...
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Convergence rate of optimal quantization grids and application to empirical measure
We study the convergence rate of optimal quantization for a probability ...
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Average Convergence Rate of Evolutionary Algorithms
In evolutionary optimization, it is important to understand how fast evolutionary algorithms converge to the optimum per generation, or their convergence rate. This paper proposes a new measure of the convergence rate, called average convergence rate. It is a normalised geometric mean of the reduction ratio of the fitness difference per generation. The calculation of the average convergence rate is very simple and it is applicable for most evolutionary algorithms on both continuous and discrete optimization. A theoretical study of the average convergence rate is conducted for discrete optimization. Lower bounds on the average convergence rate are derived. The limit of the average convergence rate is analysed and then the asymptotic average convergence rate is proposed.
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