Titlebook: Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heur; International Worksh Thomas Stützle,M

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Head Accelerations During Soccer Headingy Stützle and Hoos and consider their runtime on simple pseudo-Boolean functions such as OneMax and LeadingOnes. Interestingly, it turns out that a variant called 1-ANT is very sensitive to the choice of the evaporation factor while a recent technical report by Gutjahr and Sebastiani suggests partly

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https://doi.org/10.1007/978-0-387-45951-6es and the behavior of SLS algorithms, which —because of their stochastic nature— might need sophisticated statistical tools..In this paper we describe ., an object-oriented framework for the experimental analysis of SLS algorithms, developed in the C++ language. . integrates with ., a framework for

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Rebecca H. Shaw,James A. Sherwoodalgorithms are evaluated on the basis of solution quality attained..We discuss the experimental designs with special focus on the role of the test instances in the statistical analysis. Contrary to previous practice of modeling instances as a blocking factor, we treat them as a random factor. Togeth

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https://doi.org/10.1007/978-2-287-99056-4e method checks if a new association between two vertices is compatible by considering the structure of their local neighborhoods, represented as the number of limited-length paths of different type originating from each vertex. In addition, randomized versions of the algorithms are studied experime

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https://doi.org/10.1007/978-3-031-62937-2 science, this problem is known as the longest common subsequence problem. In this work we present a probabilistic beam search approach to solve this classical problem. To our knowledge, this algorithm is the first stochastic local search algorithm proposed for this problem. The results show the gre

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The English Civil War and after, 1642–1658get . has a closest possible approximation. This model has numerous applications in e.g. signal compression and statistical regression. It is well known that the problem is NP-hard. Based on elements from a forward and a backward greedy method, we develop a randomized search heuristic, which in some

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