By E. Vonk, L. C. Jain, R. P. Johnson
This publication describes the applying of evolutionary computation within the automated new release of a neural community structure. The structure has an important effect at the functionality of the neural community. it's the traditional perform to exploit trial and blunder to discover an appropriate neural community structure for a given challenge. the method of trial and blunder is not just time-consuming yet won't generate an optimum community. using evolutionary computation is a step in the direction of automation in neural community structure new release. an summary of the sphere of evolutionary computation is gifted, including the organic historical past from which the sphere used to be encouraged. the main frequent ways to a mathematical starting place of the sphere of genetic algorithms are given, in addition to an summary of the hybridization among evolutionary computation and neural networks. Experiments at the implementation of automated neural community iteration utilizing genetic programming and one utilizing genetic algorithms are defined, and the efficacy of genetic algorithms as a studying set of rules for a feedforward neural community can be investigated.
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Additional info for Automatic Generation of Neural Network Architecture Using Evolutionary Computation
Sample text
However, nearly all evolutionary computation algorithms so far have been limited to haploid chromosomes, where each locus can only contain one gene.
4. 5. randomly generate an initial population of chromosomes compute the fitness of every member of the current population make an intermediate population by extracting members out of the current population by means of the reproduction operator generate the new population by applying the genetic operators (crossover, mutation) to this intermediate population if there is a member of the current population that satisfies the problem requirements then stop, otherwise go to step 2 The reproduction (or selection) operator that is most commonly used is the Roulette wheel method, where members of a population are extracted using a probabilistic Monte Carlo procedure based on their average fitness.
In the course of evolution natural selection preserves the favourable part of the variation within a species. It often does this by letting the fittest individuals of a species produce the most offspring for the next generation. It provides a selective pressure that favours the fitter individuals of a population. The theory of natural selection is therefore often referred to as the survival of the fittest. This term is misleading for a number of reasons. Reproduction ('survival') of the organism itself is not the driving force of natural selection.