# On the efficiency of Gaussian adaptation

@article{Kjellstrm1991OnTE, title={On the efficiency of Gaussian adaptation}, author={Gregor Kjellstr{\"o}m}, journal={Journal of Optimization Theory and Applications}, year={1991}, volume={71}, pages={589-597} }

Gaussian Adaptation (GA) is a stochastic process that adapts a Gaussian distribution to a region or set of feasible points in parameter space. As a result of the adaptation, GA becomes a maximum dispersion process extending the sampling over the largest possible volume in parameter space while keeping the probability of finding feasible points at a suitable level. For such a process, a general measure of efficiency is defined and an efficiency theorem is proved.

## 26 Citations

### Gaussian Adaptation Revisited - An Entropic View on Covariance Matrix Adaptation

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Gaussian Adaptation is revisited, a black-box optimizer for discrete and continuous problems that has been developed in the late 1960’s and its theoretical foundations are described and some key features of this algorithm are analyzed.

### Gaussian Adaptation as a unifying framework for continuous black-box optimization and adaptive Monte Carlo sampling

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- 2010

Modifications are presented that turn GaA into both a robust continuous black-box optimizer and an adaptive Random Walk Monte Carlo sampler that is conceptually similar to the seminal Adaptive Proposal (AP) algorithm.

### Exploring the common concepts of adaptive MCMC and Covariance Matrix Adaptation schemes

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- 2010

This extended abstract summarizes the common concepts in adaptive MCMC and co- variance matrix adaptation schemes and presents how both types of methods can be unified within the Gaussian Adaptation framework and proposes a unification of both fields as “grand challenge” for future research.

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This work aimed to show how genetic algorithms can be used to solve hard problems on gene sequence analysis.

### State of Art in Genetic Algorithms for Agricultural Systems

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- 2010

Studies on the development and use of genetic algorithms in solving hard problems in the field of agricultural systems were identified, analyzed and are presented here.

### A genetic Algorithm-Based feature selection

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The main contributions of this article are the development of a GA-based feature selector using a novel fitness function (kNN-based classification error) which enabled the GA to obtain a combinatorial set of feature giving rise to optimal accuracy.

### Parameter Estimation of the Burr Type XII Distribution with a Progressively Interval-Censored Scheme Using Genetic Algorithm

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- 2015

An extensive Monte Carlo simulation was conducted to evaluate the estimation performance of the typical maximum likelihood estimation method (TMLEM) and GA, and results show that the GA is competitive with the TMLEM in terms of resulting in a smaller bias and MSE in parameter estimation.

### What Is Autonomous Search

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- 2011

A general definition and a taxonomy of search processes with respect to their computation characteristics are proposed and some computation rules between computation stages are used to formalize the solver modifications and adaptations.

### Global parameter identification of stochastic reaction networks from single trajectories

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- 2012

A novel combination of an adaptive Monte Carlo sampler, called Gaussian Adaptation (GaA), and efficient exact stochastic simulation algorithms (SSA) that allows parameter identification from single Stochastic trajectories are proposed.

### Heuristic Artificial Intelligent Algorithm for Genetic Algorithm

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A genetic algorithm is a search technique used in computing to find exact or approximate solutions to optimization and search problems that use techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover.

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