Evolvable hardware implementation method based on trend-type compact genetic algorithm

Abstract

The invention relates to an evolvable hardware implementation method based on a trend-type compact genetic algorithm, and the method comprises the following steps of: 1) obtaining configuration parameters of an actual programmable logic device; 2) mapping the configuration parameters of the actual programmable logic device and forming into chromosome individuals; 3) calculating the fitness of thecurrent chromosome individuals; and 4) identifying whether to terminate the evolvement according to the situation of the fitness. The evolvable hardware implementation method based on the trend-type compact genetic algorithm can be used for enhancing the search capability of a hardware configuration bit string, increasing the diversity of a candidacy solution space, obtaining high-quality and optimal hardware configuration structure bit string more fast, greatly increasing the convergence speed, reducing the time required by obtaining an optimal hardware circuit structure and enhancing the real-time performance of the actual evolvable hardware.

Description

technical field [0001] The invention belongs to the field of computer control, and relates to a method for realizing evolutionary hardware, in particular to a method for realizing evolutionary hardware based on a trend type compact genetic algorithm. Background technique [0002] Evolvable Hardware (Evolvable Hardware) is a hardware circuit or large-scale integrated circuit, which can change its structure to adapt to its living environment like a living thing, and has the functions of self-organization, self-adaptation and self-repair. [0003] Evolutionary hardware is to apply the idea of ​​evolutionary algorithm to the design of hardware physical structure by simulating the natural evolution process. It is mainly composed of two elements: one is the programmable logic device represented by CPLD and FPGA, and the other is the evolutionary algorithm. The realization of evolutionary hardware is based on the development of evolutionary computing and programmable logic devices....

AI Technical Summary

Problems solved by technology

[0007] Although the CGA algorithm has the advantage of being convenient for hardware implementation, it is only suitable for solving simple problems with obvious regularity. For slightly complex problems, the algorithm is prone to local convergence during the evolution process.

In addition, the CGA algorithm often loses excellent solutions obtained before the probability variable converges

The most important thing is that when using the CGA algorithm to implement evolutionary hardware, due to its insufficient search ability and slow convergence speed, it is difficult for the realized evolutionary hardware to meet the needs of practical applications.

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