Introduction to Neuroevolutionary Methods

  • Gene I. Sher


Neuroevolution is the machine learning approach through neural networks and evolutionary computation. Before a neural network can do something useful, before it can learn, or be applied to some problem, its topology and the synaptic weights and other parameters of every neuron in the neural network must be set to just the right values to produce the final functional system. Both, the topology and the synaptic weights can be set using the evolutionary process. In this chapter we discuss what Neuroevolution is, what Topology and Weight Evolving Artificial Neural Network (TWEANN) systems are, and how they function. We also discuss how this highly advanced approach to computational intelligence can be implemented, and what some of the problems that the evolved neural network based agents can be applied to.


Neural Network Genetic Programming Mutation Operator Synaptic Weight Memetic Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Gene I. Sher
    • 1
  1. 1.Department of Electrical Engineering and Computer ScienceUniversity of Central FloridaOrlandoUSA

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