Neuro-genetic Hybrids

The Neural Networks and Genetic Algorithms represent two distinct methodologies.

Neural Networks: can learn various tasks from examples; classify phenomena and model nonlinear relationships.

Genetic Algorithms: have offered themselves as potential candidates for the optimization of parameters of NN.

Integration of GAs and NNs has turned out to be useful.

− Genetically evolved nets have reported comparable results against their conventional counterparts.

− The gradient descent learning algorithms have reported difficulties in leaning the topology of the networks whose weights they optimize.

− GA based algorithms have provided encouraging results especially with regard to face recognition, animal control, and others.

− Genetic algorithms encode the parameters of NNs as a string of properties of the network, i.e. chromosomes. A large population of chromosomes representing many possible parameters sets, for the given NN, is generated.

− GA-NN is also known as GANN have the ability to locate the neighborhood of the optimal solution quicker than other conventional search strategies.

− The drawbacks of GANN algorithms are : large amount of memory required to handle and manipulate chromosomes for a given network; the question is whether this problem scales as the size of the networks become large.