Evolution Strategies and Genetic Algorithms and their Parallelisation for Structural Optimization: Part 1 Fundamentals

2009 ◽  
pp. 187-194
Author(s):  
J. Cai ◽  
G. Thierauf
Keyword(s):  
Genetic Algorithms ◽  
Structural Optimization ◽  
Evolution Strategies

scholarly journals EVOLUTIONARY ALORITHMS AT CHOICE: FROM GA TO GP

2015 ◽  
Vol 2 ◽  
pp. 185 ◽  
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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