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View all- Ma YBai Y(2020)A multi-population differential evolution with best-random mutation strategy for large-scale global optimizationApplied Intelligence10.1007/s10489-019-01613-2Online publication date: 25-Jan-2020
We extend the theory of non-elitist evolutionary algorithms (EAs) by considering the offspring population size in the (1,@l) EA. We establish a sharp threshold at @l=log"e"e"-"1n~5log"1"0n between exponential and polynomial running times on OneMax. For ...
Evolutionary algorithms (EAs) form a popular optimisation paradigm inspired by natural evolution. In recent years the field of evolutionary computation has developed a rigorous analytical theory to analyse the runtimes of EAs on many illustrative ...
We extend the theory of non-elitist evolutionary algorithms (EAs) by considering the offspring population size in the (1,λ) EA. We establish a sharp threshold at λ = log{\frac{e}{e-1}} n ≈5 log10 n between exponential and polynomial running times on ...
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