Tester: Genetic Algorithms: Mathematics
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New article Genetic Algorithms: Mathematics has been published:
Genetic (evolutionary) algorithms are used for optimization purposes. An example of such purpose can be neuronet learning, i.e., selection of such weight values that allow reaching the minimum error. At this, the genetic algorithm is based on the random search method.
The principal trouble with random search is the fact that we cannot be aware of how much time it takes to solve the problem. To avoid significant wastes of time, they apply methods developed in biology, namely, the methods prepared in studies of origin of species and evolution. Only the fittest animals are known to survive during evolution. As a result, the fitness of the population grows what enables it to adjust the dynamic environment.
The algorithm of the kind was first proposed by John H. Holland, University of Michigan, USA, in 1975. It was named the Holland's Reproductive Plan, and this underlay almost all types of genetic algorithms. However, before we take a look more closely at this plan, we will discuss the matter how realities can be encoded to be used in genetic algorithms.
Author: MetaQuotes Software Corp.