Results 41 to 50 of about 34,025 (156)
New Approximability Results for the Robust k-Median Problem
, 2013 We consider a robust variant of the classical $k$-median problem, introduced by Anthony et al. \cite{AnthonyGGN10}. In the \emph{Robust $k$-Median problem}, we are given an $n$-vertex metric space $(V,d)$ and $m$ client sets $\set{S_i \subseteq V}_{i=1 ...
C. Lund +10 more
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Direct block scheduling technology: Analysis of Avidity
REM: International Engineering JournalThis study is focused on Direct Block Scheduling testing (Direct Multi-Period Scheduling methodology) which schedules mine production considering the correct discount factor of each mining block, resulting in the final pit.
Felipe Ribeiro Souza +8 more
doaj +1 more source
Runtime analysis of randomized search heuristics for dynamic graph coloring [PDF]
Proceedings of the Genetic and Evolutionary Computation Conference, 2019 We contribute to the theoretical understanding of randomized search heuristics for dynamic problems. We consider the classical graph coloring problem and investigate the dynamic setting where edges are added to the current graph. We then analyze the expected time for randomized search heuristics to recompute high quality solutions. This includes the (1+
Bossek, J. +3 more
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An Extended Jump Functions Benchmark for the Analysis of Randomized Search Heuristics
Algorithmica, 2022 Extended version of a paper that appeared in the proceedings of GECCO 2021.
Henry Bambury +2 more
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Black-Box Complexity: Breaking the $O(n \log n)$ Barrier of LeadingOnes
, 2012 We show that the unrestricted black-box complexity of the $n$-dimensional XOR- and permutation-invariant LeadingOnes function class is $O(n \log (n) / \log \log n)$. This shows that the recent natural looking $O(n\log n)$ bound is not tight.
B. Doerr, S. Droste, S. Droste
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how randomized search heuristics find maximum cliques in planar graphs [PDF]
Proceedings of the 8th annual conference on Genetic and evolutionary computation, 2006 Surprisingly, general search heuristics often solve combinatorial problems quite sufficiently, although they do not outperform specialized algorithms. Here, the behavior of simple randomized optimizers on the maximum clique problem on planar graphs is investigated rigorously. The focus is on the worst-, average-, and semi-average-case behaviors.
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Analyzing randomized search heuristics via stochastic domination
Theoretical Computer Science, 2019 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Particle algorithms for optimization on binary spaces
, 2011 We discuss a unified approach to stochastic optimization of pseudo-Boolean objective functions based on particle methods, including the cross-entropy method and simulated annealing as special cases.
Schäfer, Christian
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OneMax in Black-Box Models with Several Restrictions
, 2015 Black-box complexity studies lower bounds for the efficiency of general-purpose black-box optimization algorithms such as evolutionary algorithms and other search heuristics.
Doerr, Carola, Lengler, Johannes
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Randomized Search Heuristics as an Alternative to Exact Optimization [PDF]
, 2004 There are many alternatives to handle discrete optimization problems in applications. Problem-specific algorithms vs. heuristics, exact optimization vs. approximation vs. heuristic solutions, guaranteed run time vs. expected run time vs. experimental run time analysis. Here, a framework for a theory of randomized search heuristics is presented. After a
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