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

Active Publication Date: 2019-08-16
SOUTH CHINA UNIV OF TECH
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Problems solved by technology

Existing high-performance computing frameworks have achieved great success in algorithm acceleration problems, but they only focus on one computing resource, such as GPU

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  • Large-scale symbol regression method and system based on adaptive parallel genetic algorithm
  • Large-scale symbol regression method and system based on adaptive parallel genetic algorithm
  • Large-scale symbol regression method and system based on adaptive parallel genetic algorithm

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Embodiment Construction

[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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Abstract

The invention discloses a large-scale symbol regression method and system based on an adaptive parallel genetic algorithm, and the system comprises a main process module which is used for initializingand calling a CPU thread module and realizing a synchronization barrier and migration operation; a CPU thread module which is used for executing a genetic programming algorithm, realizing EV updatingand calling the GPU adaptive value evaluation module; and a GPU adaptive value evaluation module which comprises a CPU auxiliary thread, a CUDA library function and a CUDA self-defined function and is used for executing adaptive value evaluation. According to the invention, a self-adaptive multi-population evolution mechanism and a parallel computing system of heterogeneous computing resources are introduced into a genetic programming algorithm; effective construction elements are successfully extracted by applying an adaptive multi-population evolution mechanism, so that the performance of agenetic programming algorithm in a complex problem of the multi-construction elements is improved, and by designing a parallel computing system of heterogeneous computing resources, computing resources of a CPU (Central Processing Unit) and a GPU (Graphics Processing Unit) are fully utilized, and the searching efficiency is remarkably improved.

Description

technical field [0001] The invention relates to two fields of intelligent computing and high-performance computing, and mainly relates to a large-scale symbolic regression method and system based on an adaptive parallel genetic algorithm. Background technique [0002] Genetic programming algorithm is an algorithm widely used in symbolic regression problems, data knowledge discovery, and rule mining. As early as 2005, Koza and Poli had applied the genetic programming algorithm to the symbolic regression problem and achieved great success. After more than ten years of development, a large number of improved variants have emerged in the field of genetic programming algorithms, and these variants have proved the effectiveness and potential of genetic programming algorithms in various applications. At present, genetic programming algorithms are widely used in financial analysis, climate data analysis and other fields, and have huge economic value. [0003] However, at this stag...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/12
CPCG06N3/126
Inventor 钟竞辉黄至行
Owner SOUTH CHINA UNIV OF TECH
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