In this paper we develop a methodology for defining stopping rules in a general class of global random search algorithms that are based on the use of statistical procedures. To build these stopping rules we reach a compromise between the expected increase in precision of the statistical procedures and the expected waiting time for this increase in precision to occur
This paper presents some simple technical conditions that guarantee the convergence of a general cla...
This paper formalizes the problem of choosing online the number of explorations in a local search al...
By far the most efficient methods for global optimization are based on starting a local optimization...
In this paper we develop a methodology for defining stopping rules in a general class of global rand...
This thesis addresses aspects of stochastic algorithms for the solution of global optimisation probl...
Two common questions when one uses a stochastic global optimization algorithm, e.g., simulated annea...
Controlled Random Search (CRS) is a simple population based algorithm which despite its attractivene...
In this paper, a new random search technique which facilitates the determination of the global minim...
summary:The paper presents the stopping rule for random search for Bayesian model-structure estimati...
This article analyses a counting process associated with a stochastic process arising in global opti...
By far the most efficient methods for global optimization are based on starting a local optimization...
A greedy randomized adaptive search procedure (GRASP) is proposed for the approximate solution of ge...
We consider a combination of state space partitioning and random search methods for solving determin...
Abstract. We examine the local convergence properties of pattern search methods, complementing the p...
This dissertation is motivated by the problem of finding a global minimizer or a feasible argument f...
This paper presents some simple technical conditions that guarantee the convergence of a general cla...
This paper formalizes the problem of choosing online the number of explorations in a local search al...
By far the most efficient methods for global optimization are based on starting a local optimization...
In this paper we develop a methodology for defining stopping rules in a general class of global rand...
This thesis addresses aspects of stochastic algorithms for the solution of global optimisation probl...
Two common questions when one uses a stochastic global optimization algorithm, e.g., simulated annea...
Controlled Random Search (CRS) is a simple population based algorithm which despite its attractivene...
In this paper, a new random search technique which facilitates the determination of the global minim...
summary:The paper presents the stopping rule for random search for Bayesian model-structure estimati...
This article analyses a counting process associated with a stochastic process arising in global opti...
By far the most efficient methods for global optimization are based on starting a local optimization...
A greedy randomized adaptive search procedure (GRASP) is proposed for the approximate solution of ge...
We consider a combination of state space partitioning and random search methods for solving determin...
Abstract. We examine the local convergence properties of pattern search methods, complementing the p...
This dissertation is motivated by the problem of finding a global minimizer or a feasible argument f...
This paper presents some simple technical conditions that guarantee the convergence of a general cla...
This paper formalizes the problem of choosing online the number of explorations in a local search al...
By far the most efficient methods for global optimization are based on starting a local optimization...