Evolutionary Scheduling: A Review

Early and seminal work which applied evolutionary computing methods to scheduling problems from 1985 onwards laid a strong and exciting foundation for the work which has been reported over the past decade or so. A survey of the current state-of-the-art was produced in 1999 for the European Network of Excellence on Evolutionary Computing EVONET—this paper provides a more up-to-date overview of the area, reporting on current trends, achievements, and suggesting the way forward.     

Conformational Modeling of Continuum Structures in Robotics and Structural Biology: A Review

Hyper-redundant (or snakelike) manipulators have many more degrees of freedom than required to position and orient an object in space. They have been employed in a variety of applications ranging from search-and-rescue to minimally invasive surgical procedures, and recently they even have been proposed as solutions to problems in maintaining civil infrastructure and the repair of satellites. The kinematic and dynamic properties of snakelike robots are captured naturally using a continuum backbone curve equipped with a naturally evolving set of reference frames, stiffness properties, and mass density. When the snakelike robot has a continuum architecture, the backbone curve corresponds with the physical device itself. Interestingly, these same modeling ideas can be used to describe conformational shapes of DNA molecules and filamentous protein structures in solution and in cells. This paper reviews several classes of snakelike robots: (1) hyper-redundant manipulators guided by backbone curves; (2) flexible steerable needles; and (3) concentric tube continuum robots. It is then shown how the same mathematical modeling methods used in these robotics contexts can be used to model molecules such as DNA. All of these problems are treated in the context of a common mathematical framework based on the differential geometry of curves, continuum mechanics, and variational calculus. Both coordinate-dependent Euler–Lagrange formulations and coordinate-free Euler–Poincaré approaches are reviewed.     

进化计算简要综述

介绍进化计算的起源与发展历史、进化计算的特点与分类、进化计算有关研究与应用现状、进化计算有关软件与国际信息交流等方面的基本情况。     

Evolutionary Computation for Expensive Optimization: A Survey

Machine Intelligence Research - Expensive optimization problem (EOP) widely exists in various significant real-world applications. However, EOP requires expensive or even unaffordable costs for...     

蝉鸣优化:一种新的仿生进化算法

借鉴秋蝉鸣叫中表现出的某种同步化以及蝉的生活习性提出了一种新的仿生优化算法:蝉鸣优化(CSO),分析并指出了CSO除具有一般进化算法的特性外还具有两点独特的特性,并基于有限Markov链理论证明了CSO的渐近收敛性。利用CSO、PSO和DE对9个高维Benchmark函数的仿真计算比较表明:CSO是一种非常适于求解数值最优化问题的进化算法。     

Evolutionary Optimization: Pitfalls and Booby Traps

Evolutionary computation (EC), a collective name for a range of metaheuristic black-box optimization algorithms, is one of the fastest-growing areas in computer science. Many manuals and "how-to"s on the use of different EC methods as well as a variety of free or commercial software libraries are widely available nowadays. However, when one of these methods is applied to a real-world task, there can be many pitfalls and booby traps lurking - certain aspects of the optimization problem that may lead to unsatisfactory results even if the algorithm appears to be correctly implemented and executed. These include the convergence issues, ruggedness, deceptiveness, and neutrality in the fitness landscape, epistasis, non-separability, noise leading to the need for robustness, as well as dimensionality and scalability issues, among others. In this article, we systematically discuss these related hindrances and present some possible remedies. The goal is to equip practitioners and researchers alike with a clear picture and understanding of what kind of problems can render EC applications unsuccessful and how to avoid them from the start.     

基于双群体进化规划的动态优化算法

动态优化问题的优化环境随时间变化导致了最优解随时间移动.为了有效地跟踪最优解,提出了一个基于双群体进化规划的动态优化算法.局部搜索群体运用高斯变异算子,并接受已有信息;全局搜索群体运用柯西变异算子,与已有信息隔离并传送较优个体至局部搜索群体.在进化过程中,它们的群体规模动态地变化.算法有效地利用了已有信息,实现了全局搜索与局部搜索的分离,适合于求解环境变化方式未知的动态优化问题.对三个动态优化模型进行了测试,并与随机初始化群体法进行了比较,仿真结果表明r提出的算法是有效的.     

Cybersecurity Landscape on Remote State Estimation: A Comprehensive Review

Cyber-physical systems(CPSs) have emerged as an essential area of research in the last decade, providing a new paradigm for the integration of computational and physical units in modern control systems. Remote state estimation(RSE) is an indispensable functional module of CPSs. Recently, it has been demonstrated that malicious agents can manipulate data packets transmitted through unreliable channels of RSE, leading to severe estimation performance degradation. This paper aims to present an over...     

Evolutionary Optimization Methods for High-Dimensional Expensive Problems: A Survey

Evolutionary computation is a rapidly evolving field and the related algorithms have been successfully used to solve various real-world optimization problems. The past decade has also witnessed their fast progress to solve a class of challenging optimization problems called high-dimensional expensive problems (HEPs). The evaluation of their objective fitness requires expensive resource due to their use of time-consuming physical experiments or computer simulations. Moreover, it is hard to traverse the huge search space within reasonable resource as problem dimension increases. Traditional evolutionary algorithms (EAs) tend to fail to solve HEPs competently because they need to conduct many such expensive evaluations before achieving satisfactory results. To reduce such evaluations, many novel surrogate-assisted algorithms emerge to cope with HEPs in recent years. Yet there lacks a thorough review of the state of the art in this specific and important area. This paper provides a comprehensive survey of these evolutionary algorithms for HEPs. We start with a brief introduction to the research status and the basic concepts of HEPs. Then, we present surrogate-assisted evolutionary algorithms for HEPs from four main aspects. We also give comparative results of some representative algorithms and application examples. Finally, we indicate open challenges and several promising directions to advance the progress in evolutionary optimization algorithms for HEPs.

     

Book Review: Evolutionary Game Theory

Autonomous Agents and Multi-Agent Systems -     

一个用于多目标优化的进化规划算法

进化计算的群体搜索机制为多目标优化问题的直接求解提供了途径.本文将多目标遗传算法中的一些技术用于进化规划,提出一个多目标进化规划算法,并给出计算实例.     

一个用于多目标优化的进化规划算法

进化计算的群体搜索机制为多目标优化问题的直接求解提供了途径。本文将多目标遗传算法中的一些技术用于进化规划,提出一个多目标进化规划算法,并给出计算实例。     

一种新的混合演化多目标优化算法

在KKT(Karush-Kuhn-Tucker)条件下,m维的连续多目标优化问题的Pareto解集在决策空间是一个(m-1)维的流形(manifold)。随着算法的迭代,当前种群将分布在流形的周围。为充分利用这一规则特性(regularity property)以解决具有复杂PS(Pareto set)的多目标优化问题,本文提出一种基于差分算子和分布估计算子的混合子代生成算法。首先,引入一个参数来指示当前种群的收敛程度,即当前种群解个体所构成的数据的协方差矩阵的前(m-1)个特征值的和与所有特征值的和的比,比值越大,收敛程度越高;进而,根据不同比值,自适应调节差分算子和分布估计算子生成新解的数量。将该算法在tec09系列测试函数上进行仿真实验,并与RM-MEDA、NSGA-II-DE两个算法进行对比,实验结果表明,RM-MEDA/DE算法优于与之比较的其他算法。     

A Study on Distribution Preservation Mechanism in Evolutionary Multi-Objective Optimization

This paper reviews a number of popular distribution preservation mechanisms and examines their characteristics and effectiveness in evolutionary multi-objective (MO) optimization. A conceptual framework consisting of solution assessment and elitism is presented to better understand the search guidance in evolutionary MO optimization. Simulation studies among different distribution preservation techniques are performed over fifteen representative distribution samples and the performances are compared based upon two distribution metrics proposed in this paper. The results and findings reported in this paper are valuable for better understanding of the working principle and characteristics of distribution preservation mechanisms, which are very useful for incorporating different distribution preservation features into MO evolutionary algorithms in a modular fashion or improving the effectiveness of existing preservation approaches.     

一种新的全局优化演化算法

演化算法在求解大型复杂多极值问题的过程中经常容易陷入局部最优,该文提出了一种变换目标函数法来消除早熟收敛。当演化算法检测出局部最优点时,使用填充函数构造变换目标函数,将局部极小点及其邻域提升,保留整体最小值点。从而新方法具有消除局部最优点而保留整体最优点的功能。通过对复杂的无约束优化问题和有约束优化问题的实验,结果显示了新方法具有搜索全局最优解的良好性能。     

进化算法中一种高级映射方式：胚胎生成

1.引言进化算法是借鉴生物界自然选择和自然界遗传机制的一类搜索算法,已在最优化、机器学习、人工智能、并行处理等领域得到了非常广泛的应用。它从任意个体组成的初始群体出发,通过选择策略,交叉和变异等遗传操作组成新的群体,群体经过若干代的进化,最终找到问题的最优解或次优解。目前它包括了四种类型:遗传算法(GA),演化规划(EP),演化策略(ES),遗传