Genetic Algorithms(GAs)

by Jia-Huei Liao

Source: Chapter 9, Machine Learning, Tom M. Mitchell, 1997

The Genetic Programming Tutorial Notebookhttp://www.geneticprogramming.com/Tutorial/tutorial.html#anchor160803

Simple Symbolic Regression Using Genetic Programming John Kozahttp://www.ifh.ee.ethz.ch/~gerber/approx/default.html

Genetic Algorithms

• Genetic Algorithms

• Genetic Programming

• Models of Evaluation And Learning

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.

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.

A Prototypical GA

Representing Hypotheses

Attribute 1 : Outlook Values : Sunny, Overcast or Rainy

100 -> Outlook = Sunny 011-> Outlook = Overcast Rainy

Attribute 2 : Wind Values : Strong or Weak

Rule Precondition:

(Outlook = Overcast Rainy) (Wind = Strong) Outlook Wind

011 10

Rule Postcondition:

Attribute 3 : PlayTennis Values : Yes or No 1 bit

IF Wind = Strong THEN PlayTennis = No

Outlook Wind PlayTennis

111 10 0 bit string: 111100

Example of Bit String:

Genetic Operators