Optimizing linear functions with the \((1 + \lambda)\) evolutionary algorithm -- different asymptotic runtimes for different instances
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Publication:477078
DOI10.1016/j.tcs.2014.03.015zbMath1303.68120OpenAlexW2015766439MaRDI QIDQ477078
Benjamin Doerr, Marvin Künnemann
Publication date: 2 December 2014
Published in: Theoretical Computer Science (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.tcs.2014.03.015
Approximation methods and heuristics in mathematical programming (90C59) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20)
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The interplay of population size and mutation probability in the \((1+\lambda )\) EA on OneMax ⋮ A runtime analysis of parallel evolutionary algorithms in dynamic optimization ⋮ Does comma selection help to cope with local optima? ⋮ Fast mutation in crossover-based algorithms ⋮ Runtime analysis for self-adaptive mutation rates ⋮ A tight runtime analysis for the \((\mu + \lambda)\) EA ⋮ Analyzing randomized search heuristics via stochastic domination ⋮ Self-adjusting population sizes for the (1,\( \lambda )\)-EA on monotone functions ⋮ The \((1+\lambda)\) evolutionary algorithm with self-adjusting mutation rate ⋮ Solving problems with unknown solution length at almost no extra cost ⋮ Island models meet rumor spreading ⋮ Multiplicative up-drift ⋮ Optimal static and self-adjusting parameter choices for the \((1+(\lambda ,\lambda ))\) genetic algorithm ⋮ Optimal mutation rates for the \((1+\lambda)\) EA on OneMax through asymptotically tight drift analysis ⋮ Working principles of binary differential evolution ⋮ The univariate marginal distribution algorithm copes well with deception and epistasis
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