检索结果-内蒙古大学图书馆

15th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2020

作者： Garcia-Vico, Angel Miguel Carmona, Cristobal J. Gonzalez, Pedro del Jesus, Maria Jose Interuniversity Andalusian Institute on Data Science and Computation Intelligence University of Jaén Jaén23071 Spain

ISBN: (纸本)9783030578015

A preliminary many objective algorithm for extracting fuzzy emerging patterns is presented in this contribution. The proposed algorithm employs fuzzy logic together with an evolutionary algorithm. The aim is to expand the complex search space that we have in emerging pattern mining. The experimental study presented in this paper faces this new proposal regarding an ensemble of one of the most used algorithms within supervised descriptive rule discovery. Results presents a set of patterns with a major interpretability and precision for the new proposal which could be interesting for experts in real-world applications. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Evolutionary algorithms

来源：评论

学校读者我要写书评

暂无评论

Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration versus Algorithmic Behavior, Critical Analysis and Recommendations (from 2020 to 2024)

arXiv

引用

arXiv 2020年

作者： Molina, Daniel Poyatos, Javier Del Ser, Javier Garcia, Salvador Hussain, Amir Herrera, Francisco Department of Computer Science and Artificial Intelligence DaSCI Andalusian Institute of Data Science and Computation Intelligence University of Granada Spain Spain Spain Edinburgh Napier University United Kingdom

In recent years, bio-inspired optimization has garnered significant attention in the literature. This algorithmic family mimics various biological processes observed in nature to effectively tackle complex optimization problems. The proliferation of nature- and bio-inspired algorithms, accompanied by a plethora of applications, tools, and guidelines, underscores the growing interest in this field. However, the exponential rise in the number of bio-inspired algorithms poses a challenge to the future trajectory of this research domain. Along the five versions of this document, the number of approaches grows incessantly, and where having a new biological description takes precedence over real problem-solving. This document, in its fifth revision since the original published version in [1], presents two comprehensive taxonomies. One is based on principles of biological similarity, and the other one is based on operational aspects associated with the iteration of population models that initially have a biological inspiration. Therefore, these taxonomies enable researchers to categorize existing algorithmic developments into well-defined classes, considering two criteria: the source of inspiration and the behavior exhibited by each algorithm. Using these taxonomies, we classify 518 algorithms based on nature-inspired and bio-inspired principles. Each algorithm within these categories is thoroughly examined, allowing for a critical synthesis of design trends and similarities, and identifying the most analogous classical algorithm for each proposal. From our analysis, we conclude that a poor relationship is often found between the natural inspiration of an algorithm and its behavior. Furthermore, similarities in terms of behavior between different algorithms are greater than what is claimed in their public disclosure: specifically, we show that more than one-fourth of the reviewed bio-inspired solvers are versions of classical algorithms. The conclusions from the analysis o

关键词： Iterative methods

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：