Multivariate integration problems arising in the real world often lead to computationally intensive numerical solutions. If the singularities and/or peaks in the integrand are not known a priori, the use of adaptive methods is recommended. The eeciency of adaptive methods depends heavily on focusing on the sub-regions that contain singularities or peaks in the integrands. In this paper, we pres...

This paper presents a self–adaptive bacteria swarm optimization algorithm, and its application in a suite of optimization benchmark problems, where the self–adaptive algorithm outperformed in most cases the non adaptive version. The algorithm follows a methodology that uses some concepts included in the Evolution Strategies for the parameter control, allowing the algorithm to select online the ...

We study the nonparametric covariance estimation of a stationary Gaussian field X observed on a regular lattice. In the time series setting, some procedures like AIC are proved to achieve optimal model selection among autoregressive models. However, there exists no such equivalent results of adaptivity in a spatial setting. By considering collections of Gaussian Markov random fields (GMRF) as a...

In this paper, a novel method for selecting a navigation satellite subset for a global positioning system (GPS) based on a genetic algorithm is presented. This approach is based on minimizing the factors in the geometric dilution of precision (GDOP) using a modified genetic algorithm (MGA) with an elite conservation strategy, adaptive selection, adaptive mutation, and a hybrid genetic algorithm...

This paper addresses the problem of robust 2D image motion estimation in natural environments. We develop an adaptive tracking-region selection and optical-flow estimation technique. The strategy of adaptive region selection locates reliable tracking regions and makes their motion estimation more reliable and computationally efficient. The multi-stage estimation procedure makes it possible to d...

Computerized adaptive testing is becoming increasingly popular due to advancement of modern computer technology. It differs from the conventional standardized testing in that the selection of test items is tailored to individual examinee’s ability level. Arising from this selection strategy is a nonlinear sequential design problem. We study, in this paper, the sequential design problem in the c...

Efficient and deadlock-free routing is critical to the performance of networks-on-chip. In this paper we present an approach that can be coupled to any adaptive routing algorithm to improve the performance with a minimal overhead on area and energy consumption. The proposed approach introduces the concept of Neighbors-on-Path to exploit the situations of indecision occurring when the routing fu...

We propose a reinforcement learning approach for an adaptive selection and application of 3D point cloud feature descriptors for the purpose of 3D object classification. The result of the learning process is an autonomously learned strategy of selection of descriptors with the property that the successive application of these descriptors to a 3D point cloud yields high classification rates amon...

The theory of evolutionary algorithms on continuous space gravitates around the evolution strategy with one individual, adaptive mutation and elitist selection, optimizing symmetric, quadratic SPHERE function. classic, normal consists three main building-blocks: i) a two-term formula for local (constant mutation) expected progress; ii) an exponential global behavior (adaptive algorithm; iii) li...