兔子進化的例子. 物競天擇 (Selection) 交配 ( 換 )(Cross Over) 突變 (Mutation)

Slides:



Advertisements
Similar presentations
Genetic Algorithms Chapter 3. A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing Genetic Algorithms GA Quick Overview Developed: USA in.
Advertisements

化 合 价 化 合 价 +11+11 Na +17 Cl Na +17 Cl +11 Na +17 Cl +11.
第二章 研究主題(研究題 目)與研究問題.
Divide-and-Conquer. 什麼是 divide-and-conquer ? Divide 就是把問題分割 Conquer 則是把答案結合起來.
布林代數的應用--- 全及項(最小項)和全或項(最大項)展開式
Advanced Chemical Engineering Thermodynamics
Speaker: Pei-Ni Tsai. Outline  Introduction  Fitness Function  GA Parameters  GA Operators  Example  Shortest Path Routing Problem 2.
McGraw-Hill/Irwin © 2003 The McGraw-Hill Companies, Inc.,All Rights Reserved. 參 實驗法.
1.1 電腦的特性 電腦能夠快速處理資料:電腦可在一秒內處理數百萬個 基本運算,這是人腦所不能做到的。原本人腦一天的工 作量,交給電腦可能僅需幾分鐘的時間就處理完畢。 電腦能夠快速處理資料:電腦可在一秒內處理數百萬個 基本運算,這是人腦所不能做到的。原本人腦一天的工 作量,交給電腦可能僅需幾分鐘的時間就處理完畢。
雄性美麗的廢物:生存無益. Sexual dimorphism a) Male, with mane b) Female.
COMP305. Part II. Genetic Algorithms. Genetic Algorithms.
圖片索引專題 指導教授:陳淑媛 教授 黃伯偉 林育瑄. 動機 & 理念  目前圖像檢索系統中使用的大多都為利用文字 標籤圖像或是圖像輪廓特徵來進行搜尋,然而 輪廓特徵的缺點卻是所有組成圖像的線條都要 逐一處理相當耗時。  所以本研究的目標在於,提出一個以像素點為 特徵的有效率與正確率的圖像檢索演算法實作。
Introduction to Java Programming Lecture 17 Abstract Classes & Interfaces.
:Problem D: Bit-wise Sequence ★★★☆☆ 題組: Problem Set Archive with Online Judge 題號: 10232: Problem D: Bit-wise Sequence 解題者:李濟宇 解題日期: 2006 年 4 月 16.
彈性變形 點的位移 線的旋轉.
2017/4/16 第 7 章 ER與EER對應到關聯式.
各種線上電子資源的特異功能 SpringerLINK 的 Alert, Serials Update, News 2003/4/28 修改.
Fugacity Coefficient and Fugacity
生產系統導論 生產系統簡介 績效衡量 現代工廠之特徵 管理機能.
GA Genetic Algorithm.  Introduction  Algorithm  GA Operations  Chromosome representation  Fitness function  Demonstration of LibGA.
Management Abstracts Retrieval System; MARS 檢索操作.
Genetic Algorithm for Variable Selection
演算法 8-1 最大數及最小數找法 8-2 排序 8-3 二元搜尋法.
Fractal Image Compression Lossy Looking for “local” similarities PIFS -- Partitioned Iteration Function system High compression ratio and high quality.
Intro to AI Genetic Algorithm Ruth Bergman Fall 2002.
複雜 Complexity 陳慶瀚 機器智慧與自動化技術 (MIAT) 實驗室 2005 年 10 月 11 日 產業研發碩士專班課程 當代系統科學思想.
數位暗房 講師:阿魯米. 常用軟體 1. 光影魔術手:簡單、方便、輕巧好用 2.Lightroom :管理照片方便容易 3.Photoshop :進階修圖技巧 示範軟體:光影魔術手.
11.1 Genetic Variation Within Population KEY CONCEPT A population shares a common gene pool.
Computer Science Genetic Algorithms10/13/10 1 An Investigation of Niching and Species Formation in Genetic Function Optimization Kalyanmoy Deb David E.
: Wine trading in Gergovia ★★☆☆☆ 題組: Contest Volumes with Online Judge 題號: 11054: Wine trading in Gergovia 解題者:劉洙愷 解題日期: 2008 年 2 月 29 日 題意:在 Gergovia.
NVivo 7在文件分析應用.
1 Chemical and Engineering Thermodynamics Chapter 1 Introduction Sandler.
Chapter 6: Transform and Conquer Genetic Algorithms The Design and Analysis of Algorithms.
物件導向實習 極高的忘記答題率 … AB 卷都有的題目 : 4(1). Define method overloading 明明寫出了方法, 卻不回答老師的題目 (1)( 只要 寫出定義就好了 ) 另外, 讀題一定要仔細 : Two overloaded methods average.
SOFT COMPUTING (Optimization Techniques using GA) Dr. N.Uma Maheswari Professor/CSE PSNA CET.
Intro. ANN & Fuzzy Systems Lecture 36 GENETIC ALGORITHM (1)
Genetic Algorithms Michael J. Watts
Ming-Feng Yeh Genetic Algorithm Genetic algorithms are a part of evolutionary computing, which is a rapidly growing area of artificial intelligence.
Last lecture summary. SOM supervised x unsupervised regression x classification Topology? Main features? Codebook vector? Output from the neuron?
1 Genetic Algorithms K.Ganesh Introduction GAs and Simulated Annealing The Biology of Genetics The Logic of Genetic Programmes Demo Summary.
如何學好高中英文 -- 由幾個例子談起 國立屏東女中 英文老師 詹莉莉. 試指出下列畫底線部份字的意思 1. Why is a river so rich? That’s because a river has two banks. 2. Since no one wants to answer the.
Genetic Algorithms What is a GA Terms and definitions Basic algorithm.
Genetic Algorithms. 2 Overview Introduction To Genetic Algorithms (GAs) GA Operators and Parameters Genetic Algorithms To Solve The Traveling Salesman.
The cycle of a Genetic Algorithms is presented below Each cycle in Genetic Algorithms produces a new generation of possible solutions for a given problem.
Intelligent Database Systems Lab 國立雲林科技大學 National Yunlin University of Science and Technology 1 A Multistrategy Approach for Digital Text Categorization.
Objective: What is genetic variation and how does it affect a population?
Neural Networks And Its Applications By Dr. Surya Chitra.
Genetic Search Algorithms Matt Herbster. Why Another Search?  Designed in the 1950s, heavily implemented under John Holland (1970s)  Genetic search.
Genetic Algorithm Dr. Md. Al-amin Bhuiyan Professor, Dept. of CSE Jahangirnagar University.
Overview Last two weeks we looked at evolutionary algorithms.
Genes and Variation Genotypes and phenotypes in evolution Natural selection acts on phenotypes and does not directly on genes. Natural selection.
Using GA’s to Solve Problems
KEY CONCEPT A population shares a common gene pool.
V. How Does Evolution Work?
Genetic Variation Within Populations
KEY CONCEPT A population shares a common gene pool.
KEY CONCEPT A population shares a common gene pool.
KEY CONCEPT A population shares a common gene pool.
KEY CONCEPT A population shares a common gene pool.
Genetic Algorithms Chapter 3.
KEY CONCEPT A population shares a common gene pool.
Genetic algorithms: case study
KEY CONCEPT A population shares a common gene pool.
V. How Does Evolution Work?
KEY CONCEPT A population shares a common gene pool.
Steady state Selection
A population shares a common gene pool.
KEY CONCEPT A population shares a common gene pool.
GA.
Presentation transcript:

兔子進化的例子

物競天擇 (Selection)

交配 ( 換 )(Cross Over)

突變 (Mutation)

進化

Genetic Algorithms 利用自然進化原理的一種搜尋方法。 利用自然進化原理的一種搜尋方法。 Three Three Operators(ex. 兔子 ) 1.Selection( 腿、耳好 ) 2.Crossover 3.Mutation Two Two issues 1.Encoding 1.Encoding the problem into a chromosome 2.Defining 2.Defining the fitness function

一個簡單的例子 String No. Initial population X value F(x)=x 2 ( fitness) F i /Σf F i /avg.(f) (expected) Roulette wheel (actual) Sum Average Max

一個簡單的例子 (continued) Mating Pool Mate Crossover site New population X value F(x)=x Sum=1754Average=439Max=729

名詞對照 (cf. Goldberg 1989) Natural Genetic Algorithm ChromosomeString Gene Feature, character Allele Feature value Locus String position GenotypeStructure Phenotype A decoded structure