Genetic Artificial Neural Networks (gann) Modeling

Genetic algorithms and neural networks have received great acclaim in the computer science research community since the 1980s. For the most part, this results from successful applications of these new computing models, but also, because the concepts share the spirit of a movement that goes beyond science. The major principle of this movement is the idea that for a broad set of complex problems self-organization, the exploitation of the interaction of independent small units, is stronger than central control. The effect of this principle can be observed in several phenomena of human life like in politics, economics, in the human organism itself, etc. Both ANN and GA were invented in the spirit of a biological metaphor. The biological metaphor for neural networks is the human brain. Like the brain, this computing model consists of many small units that are interconnected. These units (or nodes) have very simple abilities. Hence, the power of the model derives from the interplay of these units. It depends on the structure of their connections. The biological metaphor for genetic algorithms is the evolution of the species by survival of the fittest, as described by Charles Darwin. In a population of animals or plants, a new individual is generated by the crossover of the genetic information of two parents. The genetic information for the construction of the individual is stored in the DNA.