GRAPE:Python中用于进化的语法算法

Signals Pub Date : 2022-09-15 DOI:10.3390/signals3030039
Allan de Lima, Samuel Carvalho, D. Dias, Enrique Naredo, Joseph P. Sullivan, C. Ryan
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引用次数: 4

摘要

GRAPE是语法进化(GE)在DEAP中的实现,DEAP是Python中的进化计算框架,它由必要的类和函数组成,以进化基于语法的解决方案,同时报告基本度量。该工具是在通用电气的诞生地生物计算与开发系统(BDS)研究小组开发的,是一种易于使用的工具(与标准C++实现libGE相比),它继承了DEAP的所有优点,如选择方法、并行性和多重搜索技术,所有这些都可以与GRAPE一起使用。在本文中,我们解决了一些问题,以举例说明GRAPE的使用,并与PonyGE2(GE在Python中的现有实现)进行比较。结果表明,GRAPE具有类似的性能,但能够利用DEAP框架中的所有额外设施和功能。我们进一步证明了GRAPE使GE能够应用于系统识别问题,并在两个基准问题上证明了这一点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
GRAPE: Grammatical Algorithms in Python for Evolution
GRAPE is an implementation of Grammatical Evolution (GE) in DEAP, an Evolutionary Computation framework in Python, which consists of the necessary classes and functions to evolve a population of grammar-based solutions, while reporting essential measures. This tool was developed at the Bio-computing and Developmental Systems (BDS) Research Group, the birthplace of GE, as an easy to use (compared to the canonical C++ implementation, libGE) tool that inherits all the advantages of DEAP, such as selection methods, parallelism and multiple search techniques, all of which can be used with GRAPE. In this paper, we address some problems to exemplify the use of GRAPE and to perform a comparison with PonyGE2, an existing implementation of GE in Python. The results show that GRAPE has a similar performance, but is able to avail of all the extra facilities and functionality found in the DEAP framework. We further show that GRAPE enables GE to be applied to systems identification problems and we demonstrate this on two benchmark problems.
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来源期刊
CiteScore
3.20
自引率
0.00%
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审稿时长
11 weeks
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