Genetic Algorithms (GAs)

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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/tutori
  al.htmlanchor160803
• Simple Symbolic Regression Using Genetic
  Programming John Koza
• http//www.ifh.ee.ethz.ch/gerber/approx/default.
  html

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Genetic Algorithms

• Genetic Algorithms
• Genetic Programming
• Models of Evaluation And Learning

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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.

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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.

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A Prototypical GA
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Representing Hypotheses
Rule Precondition

• Attribute 1 Outlook Values Sunny, Overcast or
  Rainy

100 -gt Outlook Sunny
011-gt Outlook Overcast ? Rainy
Attribute 2 Wind Values Strong or Weak
Outlook Wind 011 10
(Outlook Overcast ? Rainy) ? (Wind Strong) ?
Rule Postcondition
Attribute 3 PlayTennis Values Yes or No ?
1 bit
Example of Bit String
IF Wind Strong THEN PlayTennis No
Outlook Wind PlayTennis 111
10 0
? bit string 111100
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Genetic Operators