In this paper, a new random search technique which facilitates the determination of the global minimum, is presented. This method, called Adaptive Random Search Technique (ARSET), is experimented on test problems, and successful results are obtained. ARSET algorithm, outcome of which is observed to be relatively better, is also compared with other methods. In addition, applicability of the algorithm on artificial neural network training is tested with XOR problem. © 2005 Elsevier Inc. All rights reserved
The work deals with analysis of robusiness of stochastic optimization subrotines based on adaptive r...
Various heuristic optimization methods have been developed in artificial intelligence. These methods...
We propose a new general-purpose algorithm for locating global minima of differentiable and nondiffe...
This dissertation is motivated by the problem of finding a global minimizer or a feasible argument f...
Conventional random search techniques take a lot of time to reach optimum-like solutions. Thus, rand...
Absiraci-Fixed step size random search for minimization of functions of several parameters is descri...
A simple modification is introduced to a recently developed global optimization algorithm, the Adapt...
Controlled Random Search (CRS) is a simple population based algorithm which despite its attractivene...
Training a neural network is a difficult optimization problem because of numerous local minimums. M...
In this paper several probabilistic search techniques are developed for global optimization under th...
This thesis addresses aspects of stochastic algorithms for the solution of global optimisation probl...
In this paper we develop a methodology for defining stopping rules in a general class of global rand...
It is difficult to evaluate a random search algorithms, because regardless of a chosen method of eff...
A modified version of a common global optimization method named controlled random search is presente...
Abstract – Training a neural network is a difficult optimization problem because of numerous local m...
The work deals with analysis of robusiness of stochastic optimization subrotines based on adaptive r...
Various heuristic optimization methods have been developed in artificial intelligence. These methods...
We propose a new general-purpose algorithm for locating global minima of differentiable and nondiffe...
This dissertation is motivated by the problem of finding a global minimizer or a feasible argument f...
Conventional random search techniques take a lot of time to reach optimum-like solutions. Thus, rand...
Absiraci-Fixed step size random search for minimization of functions of several parameters is descri...
A simple modification is introduced to a recently developed global optimization algorithm, the Adapt...
Controlled Random Search (CRS) is a simple population based algorithm which despite its attractivene...
Training a neural network is a difficult optimization problem because of numerous local minimums. M...
In this paper several probabilistic search techniques are developed for global optimization under th...
This thesis addresses aspects of stochastic algorithms for the solution of global optimisation probl...
In this paper we develop a methodology for defining stopping rules in a general class of global rand...
It is difficult to evaluate a random search algorithms, because regardless of a chosen method of eff...
A modified version of a common global optimization method named controlled random search is presente...
Abstract – Training a neural network is a difficult optimization problem because of numerous local m...
The work deals with analysis of robusiness of stochastic optimization subrotines based on adaptive r...
Various heuristic optimization methods have been developed in artificial intelligence. These methods...
We propose a new general-purpose algorithm for locating global minima of differentiable and nondiffe...