NEAT: https://en.wikipedia.org/wiki/Neuroevolution_of_augmenting_topologies https://en.wikipedia.org/wiki/Neuroevolution_of_augmenting_t... NEAT > See also li

NEAT: https://en.wikipedia.org/wiki/Neuroevolution_of_augmenting_topologies https://en.wikipedia.org/wiki/Neuroevolution_of_augmenting_t... NEAT > See also links to EANT. EANT: https://en.wikipedia.org/wiki/Evolutionary_acquisition_of_neural_topologies https://en.wikipedia.org/wiki/Evolutionary_acquisition_of_ne... : > Evolutionary acquisition of neural topologies (EANT/EANT2) is an evolutionary reinforcement learning method that evolves both the topology and weights of artificial neural networks. It is closely related to the works of Angeline et al. [1] and Stanley and Miikkulainen. [2 [NEAT (2002)] Like the work of Angeline et al., the method uses a type of parametric mutation that comes from evolution strategies and evolutionary programming (now using the most advanced form of the evolution strategies CMA-ES in EANT2), in which adaptive step sizes are used for optimizing the weights of the neural networks. Similar to the work of Stanley (NEAT), the method starts with minimal structures which gain complexity along the evolution path. (in Hilbert space) > [EANT] introduces a genetic encoding called common genetic encoding (CGE) that handles both direct and indirect encoding of neural networks within the same theoretical framework. The encoding has important properties that makes it suitable for evolving neural networks: > It is complete in that it is able to represent all types of valid phenotype networks. > It is closed, i.e. every valid genotype represents a valid phenotype. (Similarly, the encoding is closed under genetic operators such as structural mutation and crossover.) > These properties have been formally proven. [3] Neither MOSES: Meta-Optimizing Semantic Evolutionary Search nor PLN: Probabilistic Logic Networks are formally proven FWIU. /? Z3 ... https://news.ycombinator.com/item?id=41944043 https://news.ycombinator.com/item?id=41944043 : > Does [formal verification [with Z3]] distinguish between xy+z and z+yx, in terms of floating point output? > math.fma() would be a different function call with the same or similar output. > (deal-solver is a tool for verifying formal implementations in Python with Z3.)