Introduction to Neuroevolutionary Methods
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.
KeywordsNeural Network Genetic Programming Mutation Operator Synaptic Weight Memetic Algorithm
- 1.Stanley KO, Risto M (2002) Efficient Reinforcement Learning through Evolving Neural Network Topologies. In Proceedings of the Genetic and Evolutionary Computation Conference.Google Scholar
- 2.Sher GI (2012) Evolving Chart Pattern Sensitive Neural Network Based Forex TradingAgents. Available at: http://arxiv.org/abs/1111.5892.
- 3.Cormen TH, Leiserson CE, Rivest RL, Stein C (2001) Introduction to Algorithms T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein, eds. (MIT Press).Google Scholar
- 7.Fleischer K, Barr AH (1993) A Simulation Testbed for The Study of Multicellular Development: The Multiple Mechanisms of Morphogenesis. In C. G. Langton (Ed.), Artificial life III, 389â€“416.Google Scholar
- 12.Schwartz TJ (1990) A Neural Chips Survey. AI Expert 5, 34-38.Google Scholar
- 13.Heemskerk JNH (1995) Overview of Neural Hardware. Neurocomputers for BrainStyle Processing Design Implementation and Application, 1-23.Google Scholar
- 16.Matsuzawa M, Krauthamer V, Richard S (1999) Fabrication of Biological Neuronal Networks for the Study of Physiological Information Processing. Johns Hopkins APL Tech. Dig. 20(3), 262-270Google Scholar