Object Oriented Programming-An Evolutionary Approach

2017 A Maximal Clique Based Multiobjective Evolutionary algorithm for overlapping community detection 论文PPT讲解

The increasing availability of molecular and genetic databases coupled with the growing power of computers gives biologists opportunities to address new issues, such as the patterns of molecular evolution, and re-assess old ones, such as the role of adaptation in species diversification.In the second edition, the book continues to integrate a wide variety of data analysis methods into a single and flexible interface: the R language. This open source language is available for a wide range of computer systems and has been adopted as a computational environment by many authors of statistical software. Adopting R as a main tool for phylogenetic analyses will ease the workflow in biologists' data analyses, ensure greater scientific repeatability, and enhance the exchange of ideas and methodological developments. The second edition is completely updated, covering the full gamut of R packages for this area that have been introduced  to the market since its previous publication five years ago. There is also  a new chapter on the simulation of evolutionary data.  Graduate students and researchers in evolutionary biology can use this book as a reference for data analyses, whereas researchers in bioinformatics interested in evolutionary analyses will learn how to implement these methods in R. The book starts with a presentation of different R packages and gives a short introduction to R for phylogeneticists unfamiliar with this language. The basic phylogenetic topics are covered: manipulation of phylogenetic data, phylogeny estimation, tree drawing, phylogenetic comparative methods, and estimation of ancestral characters. The chapter on tree drawing uses R's powerful graphical environment. A section deals with the analysis of diversification with phylogenies, one of the author's favorite research topics. The last chapter is devoted to the development of phylogenetic methods with R and interfaces with other languages (C and C++). Some exercises conclude these chapters.

随机演化博弈经典教程。非常适合初学者。里面有相对应代码。

进化多目标优化平台 由安徽大学BIMK（生物启发情报与挖掘知识研究所）和萨里大学NICE（自然启发计算与工程小组）共同开发 150多种开源进化算法 300多个开源基准测试问题 强大的GUI可并行执行实验 一键式生成Excel或LaTeX表格式的结果 最先进的算法将不断被包括在内 非常感谢您使用PlatEMO。 PlatEMO的版权属于BIMK集团。 该工具主要用于研究和教育目的。 这些代码是根据我们对论文中发布的算法的理解而实现的。 您不应以网站上的材料或信息为依据来做出任何业务，法律或任何其他决定。 我们对您在工具中使用任何算法所造成的任何后果不承担任何责任。 使用该平台的所有出版物都应承认使用“ PlatEMO”并参考以下文献： 版权 PlatEMO的版权属于BIMK组。 您可以自由地用于研究目的。 使用此平台或平台中任何代码的所有出版物都应承认使用“ PlatEMO”，并引用“田野

经典动态多目标优化算法

提出了一种用于全局优化的混合差分进化算法。 在新算法中，混沌系统的随机性被用来在搜索空间中尽可能多地散布个体，模式搜索方法被用来加速局部开发，而DE算子被用来跳到一个更好的点。 证明了全局收敛。 详细研究了三种典型的混沌系统。 在包含13个高维函数的基准示例上的数值实验表明，该新方法以较少的计算量实现了更高的成功率和最终解决方案。

Universal Perturbation Generation for Black-box Attack Using Evolutionary Algorithms

网络水平分子进化分析揭示动物TLR信号通路的进化和起源，宋晓军，金萍，目的：基因行使其生物学功能往往是通过与其相互作用的网络进行的。研究TLR信号通路的进化和起源，以揭示动物先天性免疫信号通路在

主要的计算智能方法，包括遗传编程、遗传策略和遗传算法。Evolutionary programming (EP), evolution strategies (ESs), and genetic algorithms (GAs),