Feature Selection Method Based on Reinforcement Learning Bacteria Foraging Algorithm

A feature selection method and learning technology, applied in biological models, computing, computational models, etc., to achieve the effect of improving classification accuracy and foraging efficiency

Active Publication Date: 2020-11-10
NORTHEASTERN UNIV LIAONING
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Although BFO exhibits fine search characteristics and global optimization capabilities in low-dimensional continuous optimization problems; however, when faced with high-dimensional discrete problems, it is easy to fall into local optimal solutions and cause pre-convergence

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  • Feature Selection Method Based on Reinforcement Learning Bacteria Foraging Algorithm
  • Feature Selection Method Based on Reinforcement Learning Bacteria Foraging Algorithm
  • Feature Selection Method Based on Reinforcement Learning Bacteria Foraging Algorithm

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Embodiment Construction

[0046] In order to better explain the present invention and facilitate understanding, the present invention will be described in detail below through specific embodiments in conjunction with the accompanying drawings.

[0047] In the following description, various aspects of the present invention will be described. However, those skilled in the art can implement the present invention by using only some or all of the structures or processes of the present invention. For clarity of explanation, specific numbers, arrangements and sequences are set forth, but it will be apparent that the invention may be practiced without these specific details. In other instances, well-known features have not been described in detail in order not to obscure the invention.

[0048] At present, the key to designing a robust swarm intelligence algorithm lies in how to balance the exploration and development process in the optimization process. Theoretically, solving this problem can be attributed t...

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Abstract

The invention discloses a feature selection method based on an enhanced learning type bacterial foraging algorithm. The method comprises a step of initializing the position of a bacterial community, amaximum circulation value and an initial value of an iteration number, wherein each bacterial body in the bacterial community represents a weight vector of a feature vector to be selected, a step ofselecting an exercise behavior for each bacterial body and executing the exercise behavior according to a maximal historical experience value strategy in RL, and obtaining an updated position of eachbacterial body after updating and a fitness value of each bacterial body after updating the position, a step of obtaining a feedback value for the change of the fitness value of each bacterial body based on an RL rule, and a step of updating a historical experience value accumulated by each bacterial body according to the feedback value, adding 1 to an iteration number, repeating the above processuntil the number of iterations is greater than a maximum circulation value, and outputting the bacterial community. According to the method of the invention, an enhanced learning optimization mode isused to substitute a traditional probabilistic optimization mode, a better recognition result can be obtained, and less time is taken.

Description

technical field [0001] The invention belongs to the feature selection technology, and in particular relates to a feature selection method based on an enhanced learning type flora foraging algorithm. Background technique [0002] Biologically-inspired computing has grown tremendously in recent years. Inspired by the robustness and adaptability of biological systems in dealing with complex environments, researchers have proposed many computational models and algorithms for simulating biological foraging behaviors to solve various complex optimization problems in complex engineering, which can be conveniently It is used in networked engineering computing, image processing and other fields. [0003] The swarm intelligence algorithm belongs to the biological heuristic optimization algorithm. This new type of heuristic optimization algorithm has the characteristics of potential parallelism, distribution and reconfigurability. It is a mathematical model established by simulating...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06N3/00
CPCG06N3/006
Inventor 姜慧研董万鹏马连博
Owner NORTHEASTERN UNIV LIAONING
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