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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/tutorial.html#anchor160803 Simple Symbolic Regression Using Genetic Programming John Koza
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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/tutorial.html#anchor160803 Simple Symbolic Regression Using Genetic Programming John Koza http://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.
Representing Hypotheses Rule Precondition: 100 -> Outlook = Sunny 011-> Outlook = Overcast Rainy Attribute 1 : Outlook Values : Sunny, Overcast or 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
