scholarly journals FEATURES OF APPLICATION OF METHODS OF GLOBAL RANDOM SEARCH IN SIMULATION SYSTEMS

2018 ◽  
pp. 85-89
Author(s):  
A.G. Pevneva ◽  
I.V. Ananchenko
Keyword(s):  
Random Search ◽  
Global Random Search ◽  
Simulation Systems

Stopping rules in k-adaptive global random search algorithms

2010 ◽  
Vol 48 (1) ◽  
pp. 87-97 ◽  
Author(s):  
Anatoly Zhigljavsky ◽  
Emily Hamilton
Keyword(s):  
Random Search ◽  
Search Algorithms ◽  
Stopping Rules ◽  
Global Random Search

scholarly journals Credibilistic fuzzy clustering based on evolutionary method of crazy cats

2021 ◽  
pp. 110-119
Author(s):  
Yevgeniy Bodyanskiy ◽  
Alina Shafronenko ◽  
Iryna Pliss
Keyword(s):  
Evolutionary Algorithms ◽  
Fuzzy Clustering ◽  
High Speed ◽  
Random Search ◽  
Goal Function ◽  
Large Datasets ◽  
Global Extremum ◽  
Mathematical Procedure ◽  
Global Random Search ◽  
Search Modes

The problem of fuzzy clustering of large datasets that are sent for processing in both batch and online modes, based on a credibilistic approach, is considered. To find the global extremum of the credibilistic fuzzy clustering goal function, the modification of the swarm algorithm of crazy cats swarms was introduced, that combined the advantages of evolutionary algorithms and a global random search. It is shown that different search modes are generated by a unified mathematical procedure, some cases of which are known algorithms for both local and global optimizations. The proposed approach is easy to implement and is characterized by the high speed and reliability in problems of multi-extreme fuzzy clustering.

scholarly journals Performance of global random search algorithms for large dimensions

2017 ◽  
Vol 71 (1) ◽  
pp. 57-71 ◽  
Author(s):  
Andrey Pepelyshev ◽  
Anatoly Zhigljavsky ◽  
Antanas Žilinskas
Keyword(s):  
Random Search ◽  
Search Algorithms ◽  
Global Random Search

Genetic Algorithms as Global Random Search Methods: An Alternative Perspective

1995 ◽  
Vol 3 (1) ◽  
pp. 39-80 ◽  
Author(s):  
Charles C. Peck ◽  
Atam P. Dhawan
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Random Search ◽  
Schema Theory ◽  
Global Optimum ◽  
Global Search ◽  
Search Process ◽  
Sampling Distributions ◽  
Alternative Perspective ◽  
Global Random Search

Genetic algorithm behavior is described in terms of the construction and evolution of the sampling distributions over the space of candidate solutions. This novel perspective is motivated by analysis indicating that the schema theory is inadequate for completely and properly explaining genetic algorithm behavior. Based on the proposed theory, it is argued that the similarities of candidate solutions should be exploited directly, rather than encoding candidate solutions and then exploiting their similarities. Proportional selection is characterized as a global search operator, and recombination is characterized as the search process that exploits similarities. Sequential algorithms and many deletion methods are also analyzed. It is shown that by properly constraining the search breadth of recombination operators, convergence of genetic algorithms to a global optimum can be ensured.

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