Overview: Genetic Algorithms
Genetic Algorithms has appeared recently. They
combine the best characteristics of other
optimization methods such as speedy work that
doesn’t depend on properties of optimization
criteria (like smoothness). They provide optimal
solution on a vast domain.
The name Genetic Algorithms is connected with the
fact that their work is similar to natural
selection in the Nature. Therefore it uses the
Biology and Genetics terms like gene, chromosome,
fitness, population etc. in the description of
Genetic Algorithms.
Genetic Algorithms work is similar to random sort
out (Monte Carlo method). In contrast to Monte
Carlo method the search is led purposefully. The
goal of Algorithm is to get some specimens
(population) with the best fitness (optimization
criteria) values.
Work of Genetic Optimizer can be considered as
the growth of the best population of Trading
Systems most adapted to the successful and stable
functioning according to the given fitness
criteria.
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