Design Optimisation of a Simple 2-D Aerofoil Using Stochastic Search Methods

Let [An, Bn ] be random subintervals of [0, 1] defined recursively as follows. Let A 1 = 0, B 1 = 1 and take Cn , D n to be the minimum and maximum of k, i.i.d. random points uniformly distributed on [An, Bn ]. Choose [An+1, Bn+ 1] to be [Cn , Bn ], [Any Dn ] or [Cn , Dn ] with probabilities p, q, r respectively, p + q + r = 1. It is shown that the limiting distribution of [Any Bn ] has the beta distribution on [0,1] with parameters k(p + r) and k(q + r). The result is used to consider a randomized version of Golden Section search.

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On the synthesis and optimization of liquid–liquid extraction processes using stochastic search methods

Computers & Chemical Engineering ◽

10.1016/j.compchemeng.2004.06.008 ◽

2004 ◽

Vol 28 (11) ◽

pp. 2391-2406 ◽

Cited By ~ 29

Author(s):

Athanasios I. Papadopoulos ◽

Patrick Linke

Keyword(s):

Liquid Extraction ◽

Stochastic Search ◽

Search Methods ◽

Liquid Liquid Extraction ◽

Extraction Processes

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Adaptive stochastic search methods for parameter adaptation of simulation models

Proceedings First International IEEE Symposium Intelligent Systems IS-02 ◽

10.1109/is.2002.1042568 ◽

2003 ◽

Cited By ~ 1

Author(s):

G.D. Magoulas ◽

T. Eldabi ◽

R.J. Paul

Keyword(s):

Simulation Models ◽

Stochastic Search ◽

Search Methods ◽

Parameter Adaptation

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EVOLUTIONARY ALORITHMS AT CHOICE: FROM GA TO GP

Environment Technology Resources Proceedings of the International Scientific and Practical Conference ◽

10.17770/etr2009vol2.1023 ◽

2015 ◽

Vol 2 ◽

pp. 185 ◽

Cited By ~ 1

Author(s):

Peter Grabusts

Keyword(s):

Genetic Algorithms ◽

Evolutionary Algorithms ◽

Genetic Programming ◽

Evolutionary Programming ◽

Stochastic Search ◽

Evolution Strategies ◽

Natural Evolution ◽

Search Methods ◽

The World ◽

Classification Tasks

Nowadays the possibilities of evolutionary algorithms are widely used in many optimization and classification tasks. Evolutionary algorithms are stochastic search methods that try to emulate Darwin’s principle of natural evolution. There are (at least) four paradigms in the world of evolutionary algorithms: evolutionary programming, evolution strategies, genetic algorithms and genetic programming. This paper analyzes present-day approaches of genetic algorithms and genetic programming and examines the possibilities of genetic programming that will be used in further research. The paper presents implementation examples that show the working principles of evolutionary algorithms.

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