Results 1 to 10 of about 305,470 (143)
Upper and Lower Bounds for Randomized Search Heuristics in Black-Box Optimization [PDF]
Theory of Computing Systems, 2004 Randomized search heuristics like local search, tabu search, simulated annealing, or all kinds of evolutionary algorithms have many applications. However, for most problems the best worst-case expected run times are achieved by more problem-specific algorithms. This raises the question about the limits of general randomized search heuristics.
Thomas Jansen +2 more
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Error analysis of elitist randomized search heuristics [PDF]
Swarm and Evolutionary Computation, 2021 When globally optimal solutions of complicated optimization problems cannot be located by evolutionary algorithms (EAs) in polynomial expected running time, the hitting time/running time analysis is not flexible enough to accommodate the requirement of ...
Cong Wang +3 more
semanticscholar +4 more sources
Finding large cliques in sparse semi-random graphs by simple randomized search heuristics
Theoretical Computer Science, 2007 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Tobias Storch
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Worst-Case and Average-Case Approximations by Simple Randomized Search Heuristics
Lecture Notes in Computer Science, 2005 In recent years, probabilistic analyses of algorithms have received increasing attention. Despite results on the average-case complexity and smoothed complexity of exact deterministic algorithms, little is known about the average-case behavior of randomized search heuristics (RSHs).
Carsten Witt, Witt Carsten
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An Extended Jump Functions Benchmark for the Analysis of Randomized Search Heuristics [PDF]
Algorithmica, 2021 Jump functions are the most-studied non-unimodal benchmark in the theory of randomized search heuristics, in particular, evolutionary algorithms (EAs). They have significantly improved our understanding of how EAs escape from local optima. However, their
Henry Bambury +2 more
semanticscholar +5 more sources
Theory of Randomized Search Heuristics [PDF]
Algorithmica, 2012 Randomized search heuristics such as evolutionary algorithms, evolution strategies, ant colony optimizers etc. are optimization algorithms that can be applied to a wide class of problems ranging from combinatorial to continuous optimization. They are popular in practice because they are generally easy to implement, their application requires little ...
A. Auger, C. Witt
semanticscholar +3 more sources
Exponential Upper Bounds for the Runtime of Randomized Search Heuristics [PDF]
Theoretical Computer Science, 2020 We argue that proven exponential upper bounds on runtimes, an established area in classic algorithms, are interesting also in heuristic search and we prove several such results.
Benjamin Doerr
semanticscholar +7 more sources
Parameterized Complexity Analysis of Randomized Search Heuristics [PDF]
Theory of Evolutionary Computation, 2019 This chapter compiles a number of results that apply the theory of parameterized algorithmics to the running-time analysis of randomized search heuristics such as evolutionary algorithms.
Frank Neumann, Andrew M. Sutton
semanticscholar +5 more sources
Runtime Performances of Randomized Search Heuristics for the Dynamic Weighted Vertex Cover Problem [PDF]
Algorithmica, 2020 Randomized search heuristics such as evolutionary algorithms are frequently applied to dynamic combinatorial optimization problems. Within this paper, we present a dynamic model of the classic weighted vertex cover problem and analyze the runtime ...
Feng Shi, Frank Neumann
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On the size of weights in randomized search heuristics [PDF]
Proceedings of the tenth ACM SIGEVO workshop on Foundations of genetic algorithms, 2009 Runtime analyses of randomized search heuristics for combinatorial optimization problems often depend on the size of the largest weight. We consider replacing the given set of weights with smaller weights such that the behavior of the randomized search heuristic does not change. Upper bounds on the size of the new, equivalent weights allow us to obtain
Joachim Reichel, Martin Skutella
semanticscholar +3 more sources