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etimología

Qué (quién) es augmenting$509037$ - definición

GENETIC ALGORITHM FOR THE GENERATION OF EVOLVING ARTIFICIAL NEURAL NETWORKS DEVELOPED BY KEN STANLEY IN 2002

NeuroEvolution by Augmented Topologies; NeuroEvolution of Augmented Topologies; NeuroEvolution of Augmenting Topologies

Neuroevolution of augmenting topologies

NeuroEvolution of Augmenting Topologies (NEAT) is a genetic algorithm (GA) for the generation of evolving artificial neural networks (a neuroevolution technique) developed by Kenneth 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.

https://en.wikipedia.org/wiki/Neuroevolution_of_augmenting_topologies

Berge's theorem

THEOREM

Berge's lemma; Augmenting path theorem

In graph theory, Berge's theorem states that a matching M in a graph G is maximum (contains the largest possible number of edges) if and only if there is no augmenting path (a path that starts and ends on free (unmatched) vertices, and alternates between edges in and not in the matching) with M.

https://en.wikipedia.org/wiki/Berge's_theorem

SPASS

HUMAN SETTLEMENT IN VOLOGODSKY DISTRICT, VOLOGDA OBLAST, RUSSIA

SPASS theorem prover; Synergetic Prover Augmenting Superposition with Sorts

SPASS is an automated theorem prover for first-order logic with equality developed at the Max Planck Institute for Computer Science and using the superposition calculus. The name originally stood for Synergetic Prover Augmenting Superposition with Sorts.

https://en.wikipedia.org/wiki/SPASS

Wikipedia

Neuroevolution of augmenting topologies

NeuroEvolution of Augmenting Topologies (NEAT) is a genetic algorithm (GA) for the generation of evolving artificial neural networks (a neuroevolution technique) developed by Kenneth Stanley and Risto Miikkulainen 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").

https://en.wikipedia.org/wiki/Neuroevolution of augmenting topologies