Stochastic GO algorithms

Part of the Springer Optimization and Its Applications book series (SOIA, volume 37)


We consider stochastic methods as those algorithms that use (pseudo) random numbers in the generation of new trial points. The algorithms are used a lot in applications. Compared to deterministic methods they are often easy to implement. On the other hand, for many applied algorithms no theoretical background is given that the algorithm is effective and converges to a global optimum. Furthermore, we still do not know very well how fast the algorithms converge. For the effectiveness question, Törn and Žilinskas (1989) already stress that one should sample “everywhere dense”. This concept is as difficult with increasing dimension as doing a simple grid search. In Section 7.2 we describe some observations that have been found by several researchers on the question of increasing dimensions.


Local Search Minimum Point Genetic Algorithm Trial Point Global Minimum Point 
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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Department of Computer ArchitectureMálaga UniversityMálagaSpain
  2. 2.Department of Differential EquationsBudapest University of Technology and EconomicsBudapestHungary

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