Resource:《Introduction to Evolutionary Computing》


1. What is an evolutionary algorithm?

There are many different variants of evolutionary algorithms. The common underlying behind all these techniques is the same: given a population of individuals within some environment that has limited resources, competition for those resources causes natural selection (survival of the fittest)

2. Components of Evolutionary Algorithms

  • Representation (definition of individuals)
  • Evalution function (or fitness function)
  • Population
  • Parent selection mechanism
  • Variation operators, recombination and mutation
  • Survivor selection mechanism (replacement)
  • Initialisation procedure
  • Termination condition

The general scheme of an evolutionary algorithm as a flowchart:

  

The general scheme of an evolutionary algorithm in pseudocode:

  

3. Genetic Algorithms

3.1 Introduction

This is commonly referred as a means of generating new candidate solutions.

This has:

  • a binary representation
  • fitness proportionate selection
  • a low probability of mutation
  • an emphasis on genetically inspired recombination as a means of generating new candidate solutions.

An introductory example: f(x) = x^2

3.2 Representation of Individuals

  • binary representations
  • integer representations
  • real-valued or floating-point representation
  • permutation representation

3.3 Mutation

  • mutation for binary representations
  • mutation operators for integer representations
  • mutation operators for floating-point representations
  • mutation operators for permutation representations

3.4 Recombination

  • recombination operators for binary representations
  • recombination operators for integer representations
  • recombination operators for floating-point representations
  • recombination operators for permutation representations
  • multiparent recombination

3.5 Population models

  • generational model
  • steady-state model

generational model: In each generation we begin with a population of size μ, from which a mating pool of μ parents is selected. Next, λ (=μ) offspring are created from the mating pool by the application of variantion operators, and evaluated. After each generation, the whole population is replaced by its offspring, which is called the "next generation".

steady state model: The entire population is not changed at once, but rather a part of it. In this case, λ (<μ) old individuals are replaced by  λ new ones, the offspring. The percentage of the population that is replaced is called the generational gap, and is equal to  λ/μ. Usually,  λ = 1 and a corresponding generation gap of 1/μ.

3.6 Parent Selection

  • fitness proportional selection
  • ranking selection
  • implementing selection probabilities
  • tournament selection

3.7 Survivor Selection

The survivor selection mechanism is responsible for managing the process whereby the working memory of the GA is reduced from a set of μ parents and  λ offspring to produce the set of μ individuals for the next generation.

This step in the main evolutionary cycle is also called replacement.

age-based replacement

fitness-based replacement

Evolutionary Computing: 4. Review的更多相关文章

  1. Evolutionary Computing: 5. Evolutionary Strategies(2)

    Resource: Introduction to Evolutionary Computing, A.E.Eliben Outline recombination parent selection ...

  2. Evolutionary Computing: 5. Evolutionary Strategies(1)

    resource: Evolutionary computing, A.E.Eiben Outline What is Evolution Strategies Introductory Exampl ...

  3. Evolutionary Computing: 1. Introduction

    Outline 什么是进化算法 能够解决什么样的问题 进化算法的重要组成部分 八皇后问题(实例) 1. 什么是进化算法 遗传算法(GA)是模拟生物进化过程的计算模型,是自然遗传学与计算机科学相互结合的 ...

  4. Evolutionary Computing: [reading notes]On the Life-Long Learning Capabilities of a NELLI*: A Hyper-Heuristic Optimisation System

    resource: On the Life-Long Learning Capabilities of a NELLI*: A Hyper-Heuristic Optimisation System ...

  5. Evolutionary Computing: Assignments

    Assignment 1: TSP Travel Salesman Problem Assignment 2: TTP Travel Thief Problem The goal is to find ...

  6. Evolutionary Computing: multi-objective optimisation

    1. What is multi-objective optimisation [wikipedia]: Multi-objective optimization (also known as mul ...

  7. Evolutionary Computing: 3. Genetic Algorithm(2)

    承接上一章,接着写Genetic Algorithm. 本章主要写排列表达(permutation representations) 开始先引一个具体的例子来进行表述 Outline 问题描述 排列表 ...

  8. Evolutionary Computing: 2. Genetic Algorithm(1)

    本篇博文讲述基因算法(Genetic Algorithm),基因算法是最著名的进化算法. 内容依然来自博主的听课记录和教授的PPT. Outline 简单基因算法 个体表达 变异 重组 选择重组还是变 ...

  9. [Z] 计算机类会议期刊根据引用数排名

    一位cornell的教授做的计算机类期刊会议依据Microsoft Research引用数的排名 link:http://www.cs.cornell.edu/andru/csconf.html Th ...

随机推荐

  1. 服务设计模式一:Web服务概述

    目录 1. Web服务是什么 2.为什么要使用Web服务 3.Web服务考虑的因素和替代方案 4.SOA是什么 Web服务是什么? 所谓服务,通俗的理解就是别人帮你做一些事情,比如说,腰酸背痛了,找个 ...

  2. webService 发送soap请求,并解析返回的soap报文

    本例应用场景:要做一个webService测试功能,不局限于任何一种固定格式的webService,所以像axis,cxf等框架就不好用了.只有深入到webService的原理,通过发收soap报文, ...

  3. Excel Interior.ColorIndex色彩列表

    Microsoft.Office.Interop.Excel.Range range; ; i < dt.Columns.Count; i++) { worksheet.Cells[, i + ...

  4. Maven-007-Nexus 用户添加,用户角色分配,用户修改密码,管理员重置用户密码

    配置好 maven nexus 私服后,默认的用户可通过查看[Users]查看当前私服中所存在的用户,如下图所示:

  5. 优化SQL Sever查询语句的几个要点

    1.不要在Where子句中的“=”左边进行函数.算术运算或其他表达式运算,否则系统将可能无法正确使用索引. 2.尽量不要在Where条件中使用函数,否则将不能利用索引. 3.在Where条件中使用明确 ...

  6. ionic2安装时报错

    Installing npm packages...Error with start undefinedError Initializing app: There was an error with ...

  7. 怎样实现excel隔行隔列变色效果的方法

    大家在使用excel的过程中,一定见过别人编排的excel文档,隔行添加单元格背景颜色,也就是通常所说的隔行变色效果.Excel中隔行变色效果的好处是:当在Excel中浏览一个非常大的工作簿中的数据时 ...

  8. js的实参是按值传递还是按引用传递

    1.如果是基本类型,则是按值传递 var str = 'one';function f(string) {    string = 'two';}f(str);console.log(str); // ...

  9. SynchronousQueue应用

    SynchronousQueue是无界的,是一种无缓冲的等待队列,但是由于该Queue本身的特性,在某次添加元素后必须等待其他线程取走后才能继续添加:可以认为SynchronousQueue是一个缓存 ...

  10. bootstrap入门-4.排版及其他固定样式

    本篇包括以下内容:排版.代码.表格.表单. 总结:超无聊,弃更. · 排版样式                                     标题 h1-h6 取消加粗,字体大小也有一定变化 ...