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NPROS: A Not So Pure Random Orthogonal search algorithm—A suite of random optimization algorithms driven by reinforcement learning
We live in a world where waves of novel nature-inspired metaheuristic algorithms keep hitting the shore repeatedly. This never-ending surge of new...
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Depth-First Search Performance in a Random Digraph with Geometric Outdegree Distribution
We present an analysis of the depth-first search algorithm in a random digraph model with independent outdegrees having a geometric distribution. The...
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Pure Random Search with Virtual Extension of Feasible Region
We propose a modification of the pure random search algorithm for cases when the global optimum point can be located near the boundary of a feasible...
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On asymptotic convergence rate of random search
This paper presents general theoretical studies on asymptotic convergence rate (ACR) for finite dimensional optimization. Given the continuous...
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Peeling Random Planar Maps École d’Été de Probabilités de Saint-Flour XLIX – 2019
These Lecture Notes provide an introduction to the study of those discrete surfaces which are obtained by randomly gluing polygons along their sides...
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Hesitant adaptive search with estimation and quantile adaptive search for global optimization with noise
Adaptive random search approaches have been shown to be effective for global optimization problems, where under certain conditions, the expected...
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Random Search in Fluid Flow Aided by Chemotaxis
In this paper, we consider the dynamics of a 2D target-searching agent performing Brownian motion under the influence of fluid shear flow and...
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Random colorings in manifolds
We develop a general method for constructing random manifolds and sub-manifolds in arbitrary dimensions. The method is based on associating colors to...
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Tabu Search
Tabu search is a meta-heuristic that guides a local heuristic search procedure to explore the solution space beyond local optimality. One of the main...
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Learning to sample initial solution for solving 0–1 discrete optimization problem by local search
Local search methods are convenient alternatives for solving discrete optimization problems (DOPs). These easy-to-implement methods are able to find...
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Swarm-Based Optimization with Random Descent
We extend our study of the swarm-based gradient descent method for non-convex optimization, (Lu et al., Swarm-based gradient descent method for...
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Random Walks
We start with the random walk on the 1-d lattice of the integers of the real line. From this simple model we derive the equations for the process of...
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The simultaneous semi-random model for TSP
Worst-case analysis is a performance measure that is often too pessimistic to indicate which algorithms we should use in practice. A classical...
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Mixing time of random walk on dynamical random cluster
We study the mixing time of a random walker who moves inside a dynamical random cluster model on the d -dimensional torus of side-length n . In this...
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Random Walks
This chapter deals with the random walkRandom walks problem and its connections with the diffusion processes. Its first part is dedicated to an...
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Reconstructing Unknown Coefficients of Stochastic Differential Equations and Intelligently Predicting Random Processes with Directed Learning
AbstractA way of intelligently predicting random processes is described, based on more complete use of information about statistical patterns of the...
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Optimizing Data Augmentation Policy Through Random Unidimensional Search
It is no secret among deep learning researchers that finding the optimal data augmentation strategy during training can mean the difference between...