Results 211 to 220 of about 557,643 (265)
Comprehensive Learning Fungal Growth Optimizer for Numerical Optimization and Reservoir Production Optimization. [PDF]
Biomimetics (Basel)Gong M, Song Z, Zhang X, Tang Y.
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Best-first search-based approach for mining top-k closed frequent itemsets from uncertain databases. [PDF]
PLoS OneLe N, Vo H, Nguyen T.
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SIAM Journal on Optimization, 2004
Summary: A new variant of pure random search (PRS) for function optimization is introduced. The basic finite-descent accelerated random search (ARS) algorithm is simple: the search is confined to shrinking neighborhoods of a previous record-generating value, with the search neighborhood reinitialized to the entire space when a new record is found ...
M. J. Appel, R. LaBarre, D. Radulovic
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Summary: A new variant of pure random search (PRS) for function optimization is introduced. The basic finite-descent accelerated random search (ARS) algorithm is simple: the search is confined to shrinking neighborhoods of a previous record-generating value, with the search neighborhood reinitialized to the entire space when a new record is found ...
M. J. Appel, R. LaBarre, D. Radulovic
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Minimization by Random Search Techniques
Mathematics of Operations Research, 1981We give two general convergence proofs for random search algorithms. We review the literature and show how our results extend those available for specific variants of the conceptual algorithm studied here. We then exploit the convergence results to examine convergence rates and to actually design implementable methods.
FRANCISCO Javier Solís Lozano +1 more
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IEEE Transactions on Computers, 1972
A random search technique for function minimization is proposed that incorporates the step-size and direction adaptivity of Hooke and Jeeves' [1] pattern search. Experimental results for a variety of functions indicate that the random pattern search is more effective than the corresponding deterministic method for a class of problems with hard ...
J. P. Lawrence, Kenneth Steiglitz
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A random search technique for function minimization is proposed that incorporates the step-size and direction adaptivity of Hooke and Jeeves' [1] pattern search. Experimental results for a variety of functions indicate that the random pattern search is more effective than the corresponding deterministic method for a class of problems with hard ...
J. P. Lawrence, Kenneth Steiglitz
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30th Annual Symposium on Foundations of Computer Science, 1989
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Seidel, Raimund, Aragon, Cecilia R.
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Seidel, Raimund, Aragon, Cecilia R.
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A note on randomized mutual search
Information Processing Letters, 1999zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Zvi Lotker, Boaz Patt-Shamir
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Randomizing Reductions of Search Problems
SIAM Journal on Computing, 1993The paper, with a foundational character, provides mathematically sound and robust definitions for the notion of ``feasible solution for a search problem'' and ``many-one randomized reduction'' in the context of the theory of average-case complexity.
Andreas Blass, Yuri Gurevich
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Search for the maximum of a random walk
Random Structures & Algorithms, 1992AbstractThis paper examines the efficiency of various strategies for searching in an unknown environment. The model is that of the simple random walk, which can be taken as a representation of a function with a bounded derivative that is difficult to compute. Let X1, X2+,. be independent and identically distributed with Prob(Xj = 1) = Prob(Xj = ‐1) = 1/
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