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An OOP MATLAB Extensible Framework for the Implementation of Genetic Algorithms. Part I: The Framework

机译:OOP MATLAB可扩展框架,用于实施遗传算法。第一部分:框架

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Genetic algorithms are already a well-established method for structural or general optimization. There is a large number of libraries and frameworks available assisting the researchers and engineers alike to implement genetic algorithm codes. However, there isn't a satisfactory tool available for the de facto engineering programming language: MATLAB. The existing tools only use procedural programming and offer some flexibility but not to the extent an object oriented approach can provide. This paper presents the work of the authors regarding the design and implementation of a tool aimed to fill this void. The result is a fully object oriented framework for MATLAB, similar to a toolbox. This offers the general layout of any genetic algorithm, along with typical implementations. Its architecture is designed to offer full flexibility and extensibility. The base layout allows researchers to easily study, test and implement new or improved genetic operators or concepts, while the implementation of basic algorithms offers the practitioners a ready to use tool. The first part of the paper presents in some detail the concepts and structure of the framework, while the second part deals with a case study to illustrate the effectiveness of the approach.
机译:遗传算法已经是结构或一般优化的良好方法。有大量的库和框架可提供帮助研究人员和工程师实现遗传算法代码。但是,没有令人满意的工具可用于De Facto工程编程语言:Matlab。现有工具仅使用程序编程并提供一些灵活性,但不提供面向对象的方法可以提供的。本文介绍了作者关于旨在填补这一空隙的工具的设计和实现的工作。结果是MATLAB的完全面向对象的框架,类似于工具箱。这提供了任何遗传算法的一般布局以及典型的实现。其架构旨在提供充分的灵活性和可扩展性。基础布局允许研究人员轻松学习,测试和实现新的或改进的遗传运营商或概念,而基本算法的实现为从业者提供了准备使用工具。纸张的第一部分详细介绍了框架的概念和结构,而第二部分涉及案例研究以说明该方法的有效性。

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