Minimization by Random Search Techniques

By F. Solis, R. Wets. 1981

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Citations in the corpus

Benchmark and Survey of Automated Machine Learning Frameworks (2019, background): “Random search is straightforward to implement and parallelize and well suited for gradient-free functions with many local minima (Solis & Wets, 1981).”
Survey on Automated Machine Learning (2019, background): “Random search is straightforward to implement and parallelize and well suited for gradient-free functions with many local minima (Solis and Wets, 1981).”
Handbook of Intelligent Control: Neural, Fuzzy, and Adaptive Approaches (1992, methodology): “Baba [41] has pointed out that the random search algorithm by Solis and Wets [42] can lead to a global minimum for training a multilayer perceptron.”
An analysis of particle swarm optimizers (2002, methodology): “Formal proofs are presented to support these claims, using the technique for proving convergence properties of stochastic algorithms used by Solis and Wets [128].”
Real-Coded Memetic Algorithms with Crossover Hill-Climbing (2004, background): “Every new chromosome generated by PBX-(cid:11) ( (cid:11) = 1 ) and BGA mutation undergoes the Solis and Wets’s LS procedure (Solis and Wets, 1981) with p LS = 0 : 0625 .”
Optimizing 0/1 Loss for Perceptrons by Random Coordinate Descent (2007, unknown): “Next we analyze the convergence properties of RCD algorithms, which follows from random search techniques in optimization [12].”
Accelerating AutoDock4 with GPUs and Gradient-Based Local Search (2019, methodology): “For the local search, we newly incorporate the ADADELTA (19) gradientbased method, while also including the original random numerical optimizer Solis-Wets (20).”
Improving Wireless Physical Layer Security via Cooperating Relays (2010, methodology): “For the case of one eavesdropper, we can use the solution for multiple eavesdroppers as the initial value and use a conventional local random search algorithm [39] to improve it.”
A Cooperative approach to particle swarm optimization (2004, methodology): “Consider a basic stochastic global search algorithm (as defined by Solis and Wets [1]) that generates a sample for a uniform distribution that covers the entire search space.”
AutoDockFR: Advances in Protein-Ligand Docking with Explicitly Specified Binding Site Flexibility (2015, methodology): “ADFR encodes the docking problem into a list of variables describing a docking solution and optimizes it for the AutoDock4 force field using a Genetic Algorithm (GA) combined with a Solis-Wets local search [31].”
An Electromagnetism-like Mechanism for Global Optimization (2003, methodology): “− The combination of the general scheme with other powerful local search methods, such as Solis and Wets (1981), may increase the efficiency of the algorithm.”