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Artificial Neural Network
Neuroevolution of augmenting topologies - Wikipedia, the free encyclopedia
NeuroEvolution of Augmenting Topologies ( NEAT ) is a genetic algorithm for the generation of evolving artificial neural networks (a neuroevolution technique) developed by Ken Stanley in 2002 while at The University of Texas at Austin . It alters both the weighting parameters and structures of networks, attempting to find a balance between the fitness of evolved solutions and their diversity. It is based on applying three key techniques: tracking genes with history markers to allow crossover among topologies, applying speciation (the evolution of species) to preserve innovations, and developing topologies incrementally from simple initial structures ("complexifying").Artificial neural network - Wikipedia, the free encyclopedia
An artificial neural network (ANN) , usually called neural network (NN) , is a mathematical model or computational model that is inspired by the structure and/or functional aspects of biological neural networks . A neural network consists of an interconnected group of artificial neurons , and it processes information using a connectionist approach to computation . In most cases an ANN is an adaptive system that changes its structure based on external or internal information that flows through the network during the learning phase.
Compositional pattern-producing networks (CPPNs) , are a variation of artificial neural networks (ANNs) which differ in their set of activation functions and how they are applied. While ANNs often contain only sigmoid functions (and sometimes Gaussian functions ), CPPNs can include both types of functions and many others. The choice of functions for the canonical set can be biased toward specific types of patterns and regularities. For example, periodic functions such as sine produce segmented patterns with repetitions, while symmetric functions such as Gaussian produce symmetric patterns. Linear functions can be employed to produce linear or fractal -like patterns. Thus, the architect of a CPPN-based genetic art system can bias the types of patterns it generates by deciding the set of canonical functions to include.
