1. Genetic Algorithms (GAs) by Jia-Huei Liao Source: Chapter 9, Machine Learning, Tom M. Mitchell, 1997 The Genetic Programming Tutorial Notebook http://www.geneticprogramming.com/Tutorial/tutorial.html#anchor160803 Simple Symbolic Regression Using Genetic Programming John Koza http://www.ifh.ee.ethz.ch/~gerber/approx/default.html

2. Genetic Algorithms Genetic Programming Models of Evaluation And Learning

3. Overview of GAs It is a kind of evolutionary computation. It is general optimization method that searches a large space of candidate objects (hypotheses, population) seeking one that performs best according to the fitness function (a predefined numerical measure ). It is NOT guaranteed to find an optimal object. It is broadly applied on optimization, machine learning, circuit layout, job-shop scheduling, and so on.

4. Motivation for GAs Evolution is know to be a successful, robust method for adaptation within biological systems. GAs can search spaces of hypotheses containing complex interacting models. GAs are easily parallelized and can take advantage of the decreasing costs of powerful computer hardware.

5. A Prototypical GA

6. Representing Hypotheses Attribute 1 : OutlookValues : Sunny, Overcast or Rainy 100 -> Outlook = Sunny 011-> Outlook = Overcast  Rainy Attribute 2 : WindValues : Strong or Weak Rule Precondition: (Outlook = Overcast  Rainy)  (Wind = Strong)  Outlook Wind 011 10 Rule Postcondition: Attribute 3 : PlayTennisValues : Yes or No  1 bit IF Wind = Strong THEN PlayTennis = No Outlook WindPlayTennis 111 10 0  bit string: 111100 Example of Bit String:

7. Genetic Operators