Genetic Algorithms (GAs) by Jia-Huei Liao Source: Chapter 9, Machine Learning, Tom M. Mitchell, 1997 The Genetic Programming Tutorial Notebook

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Presentation transcript:

Genetic Algorithms (GAs) by Jia-Huei Liao Source: Chapter 9, Machine Learning, Tom M. Mitchell, 1997 The Genetic Programming Tutorial Notebook Simple Symbolic Regression Using Genetic Programming John Koza

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 : 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 Rule Postcondition: Attribute 3 : PlayTennisValues : Yes or No  1 bit IF Wind = Strong THEN PlayTennis = No Outlook WindPlayTennis  bit string: Example of Bit String:

Genetic Operators