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Artificial Neural Network

Neuroevolution of augmenting topologies. 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.

Neuroevolution of augmenting topologies

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. An artificial neural network is an interconnected group of nodes, akin to the vast network of neurons in a brain.

Artificial neural network

Here, each circular node represents an artificial neuron and an arrow represents a connection from the output of one neuron to the input of another. In computer science and related fields, artificial neural networks are computational models inspired by animals' central nervous systems (in particular the brain) that are capable of machine learning and pattern recognition. Compositional pattern-producing network. 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.

Compositional pattern-producing network

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. Basic Pathfinding and Unit Navigation.