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Erschienen in:
2013 | OriginalPaper | Buchkapitel
Convergence Rates of Evolutionary Algorithms for Quadratic Convex Functions with Rank-Deficient Hessian
verfasst von : Günter Rudolph
Erschienen in: Adaptive and Natural Computing Algorithms
Verlag: Springer Berlin Heidelberg
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The best achievable convergence rates of mutation-based evolutionary algorithms are known for various characteristic test problems. Most results are available for convex quadratic functions with Hessians of full rank. Here, we prove that linear convergence rates are achievable for convex quadratic functions even though the Hessians are rank-deficient. This result has immediate implications for recent convergence results for certain evolutionary algorithms for bi-objective optimization problems.