Large-scale symbol regression method and system based on adaptive parallel genetic algorithm
A genetic algorithm and symbolic regression technology, applied in the field of intelligent computing and high-performance computing, to improve search efficiency and solve large-scale high-dimensional symbolic regression problems
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[0048] The method of the present invention will be further described below in conjunction with the accompanying drawings.
[0049] Assuming that the problem contains T(F) terminal symbols (functions), these terminal symbols and functions together constitute the building element set. Terminal symbols and functions have their corresponding EVs in each subpopulation. The genetic programming algorithm needs to use these given building elements to find a mathematical formula that satisfies the training data and the objective function.
[0050] A large-scale symbolic regression method based on an adaptive parallel genetic algorithm, comprising steps:
[0051] 1) Generate N according to the set of construction elements of the problem s A quasi-orthogonal sparse initial environment vector EV, and initialize N according to EV s subpopulations, each subpopulation contains M s individuals; create N TC CPU threads and apply N in GPU memory B GPU blocks, N in each block T GPU thread...
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