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A simple modification is introduced to a recently developed global optimization algorithm, the Adaptive Partitioned Random Search (APRS). Improvement in the performance of the algorithm has been observed numerically and supported analytically
The work deals with analysis of robusiness of stochastic optimization subrotines based on adaptive r...
This thesis addresses aspects of stochastic algorithms for the solution of global optimisation probl...
A new algorithm, dubbed memory-based adaptive partitioning (MAP) of search space, which is intended ...
The Optimality theorem presented in the above paper(1) is incorrect. A counterexample is presented
In this paper, a new random search technique which facilitates the determination of the global minim...
The Nested Partition (NP) method is efficient in large-scale optimization problems. The most promisin...
Controlled Random Search (CRS) is a simple population based algorithm which despite its attractivene...
Conventional random search techniques take a lot of time to reach optimum-like solutions. Thus, rand...
In this paper several probabilistic search techniques are developed for global optimization under th...
Feature selection is one of important and frequently used techniques in data preprocessing. It can i...
This dissertation is motivated by the problem of finding a global minimizer or a feasible argument f...
We consider a combination of state space partitioning and random search methods for solving determin...
Global optimization problems are relevant in various fields of research and industry, such as chemis...
A modified version of a common global optimization method named controlled random search is presente...
The paper studies the optimal sequential sampling policy of the partitioned random search (PRS) and ...
The work deals with analysis of robusiness of stochastic optimization subrotines based on adaptive r...
This thesis addresses aspects of stochastic algorithms for the solution of global optimisation probl...
A new algorithm, dubbed memory-based adaptive partitioning (MAP) of search space, which is intended ...
The Optimality theorem presented in the above paper(1) is incorrect. A counterexample is presented
In this paper, a new random search technique which facilitates the determination of the global minim...
The Nested Partition (NP) method is efficient in large-scale optimization problems. The most promisin...
Controlled Random Search (CRS) is a simple population based algorithm which despite its attractivene...
Conventional random search techniques take a lot of time to reach optimum-like solutions. Thus, rand...
In this paper several probabilistic search techniques are developed for global optimization under th...
Feature selection is one of important and frequently used techniques in data preprocessing. It can i...
This dissertation is motivated by the problem of finding a global minimizer or a feasible argument f...
We consider a combination of state space partitioning and random search methods for solving determin...
Global optimization problems are relevant in various fields of research and industry, such as chemis...
A modified version of a common global optimization method named controlled random search is presente...
The paper studies the optimal sequential sampling policy of the partitioned random search (PRS) and ...
The work deals with analysis of robusiness of stochastic optimization subrotines based on adaptive r...
This thesis addresses aspects of stochastic algorithms for the solution of global optimisation probl...
A new algorithm, dubbed memory-based adaptive partitioning (MAP) of search space, which is intended ...
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