Artificial Intelligence
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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"). [ edit ] Performance
Network topology is the arrangement of the various elements ( links , nodes , etc.) of a computer [ 1 ] [ 2 ] or biological network . [ 3 ] Essentially, it is the topological [ 4 ] structure of a network, and may be depicted physically or logically. Physical topology refers to the placement of the network's various components, including device location and cable installation, while logical topology shows how data flows within a network, regardless of its physical design. Distances between nodes, physical interconnections, transmission rates, and/or signal types may differ between two networks, yet their topologies may be identical. A good example is a local area network (LAN): Any given node in the LAN has one or more physical links to other devices in the network; graphically mapping these links results in a geometric shape that can be used to describe the physical topology of the network.
I created this page because of growing interest in the use and implementation of the NEAT method. I have been corresponding with an expanding group of users. Because the same points come up more than once, it makes sense to have a place where people can come and tap into the expanding knowledge we have about the software and the method itself.
