Statistical Inference in Global Random Search

1991 ◽  
pp. 114-185
Author(s):  
Anatoly A. Zhigljavsky ◽  
J. Pintér
Keyword(s):  
Statistical Inference ◽  
Random Search ◽  
Global Random Search

Semiparametric statistical inference in global random search

1993 ◽  
Vol 33 (1) ◽  
pp. 69-88 ◽  
Author(s):  
Anatoly A. Zhigljavsky
Keyword(s):  
Statistical Inference ◽  
Random Search ◽  
Global Random Search

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

Semi-Global Random Search Applied to Seismic Coherence Inversion in Complex Media

1997 ◽  
Author(s):  
P. Amand ◽  
M. Frattini ◽  
J. Virieux ◽  
A. Zollo
Keyword(s):  
Random Search ◽  
Complex Media ◽  
Global Random Search

Progressive global random search of continuous functions

1978 ◽  
Vol 15 (1) ◽  
pp. 330-342 ◽  
Author(s):  
Luc P. Devroye
Keyword(s):  
Random Search ◽  
Continuous Functions ◽  
Global Random Search

Theory of Global Random Search

1991 ◽  
Author(s):  
Anatoly A. Zhigljavsky
Keyword(s):  
Random Search ◽  
Global Random Search

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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