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Soft Computing and Genetic Algorithms: Applications and Optimization Techniques, Slides of Artificial Intelligence

West Bengal University of Animal and Fishery SciencesArtificial Intelligence

An overview of soft computing, focusing on genetic algorithms (ga's). Soft computing is a problem-solving approach used when the solution permits imprecision and uncertainty. Ga's offer robust results, low cost, and easy tractability, making them beneficial for various applications such as optimization, automatic programming, machine learning, economic models, immune system models, ecological models, and models of social systems. Real-world applications include protein signature selection in mass spectrometry and job shop scheduling problems. The document also covers real-valued optimization and its application to the traveling salesman problem.

Typology: Slides

2012/2013

Uploaded on 04/29/2013

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Softcomputing
Softcomputing refers to a set of problem solving methods
used when the required solution
–Permits some degree of imprecision
–It is subject to uncertainty
–Deals with knowledge(beliefs, choices) rather than information(truth)
–A quasi optimized solution is acceptable
–Allows ample time to come with the solution
In this case soft computing such as GA’s and Learning
offers solutions with
–Robust results
–Low cost
–Easy tractability
1
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Download Soft Computing and Genetic Algorithms: Applications and Optimization Techniques and more Slides Artificial Intelligence in PDF only on Docsity!

Softcomputing

Softcomputing refers to a set of problem solving methods used when the required solution

  • Permits some degree of imprecision
  • It is subject to uncertainty
  • Deals with knowledge(beliefs, choices) rather than information(truth)
  • A quasi optimized solution is acceptable
  • Allows ample time to come with the solution In this case soft computing such as GA’s and Learning offers solutions with
  • Robust results
  • Low cost
  • Easy tractability

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Who benefits from Applying GA’s

  • The search space is large, complex or poorly

understood.

  • Domain knowledge is scarce or expert

knowledge is difficult to encode to narrow the

search space.

  • No mathematical analysis is available.
  • Traditional search methods fail..

Source: http://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol4/tcw2/report.htmlDocsity.com 2

Real valued optimization

Many problems occur as real valued problems, e.g. continuous

parameter optimization f : ℜ n  ℜ

Illustration: Ackley’s function

Docsity.com^4

5

Application: Protein Signature Selection in Mass Spectrometry

http://www.uni-mainz.de/~frosc000/fbg_po3.html^ molecular weight

relative intensity

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Real World Applications

  • Lockheed Martin Missiles and Space Co. - Near-Minimum-Time Spacecraft Maneuvers [Howley, 96]
  • GP applied to the problem of rest-to-rest reorientation maneuvers for satellites
  • Optimal time solution is a vector of nonlinear differential equations, which are difficult to solve
  • An approximate solution is necessary for a real-time controller
  • Results: Rest-to-Rest Maneuver Times (8 test cases)
    • Optimal Solution: 287.93 seconds
    • Expert Solution: 300.3 seconds
    • GP Solution: 292.8 seconds

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A Simple Example

The Traveling Salesman Problem:

Find a tour of a given set of cities so that

  • each city is visited only once
  • the total distance traveled is minimized

Source: Wendy Williams, web.umr.edu/~ercal/387/slides/GATutorial.pptDocsity.com 8

Crossover

Single point crossover

10

Two point crossover

11001011 + 11011111 = 11001001 (AND)

Uniform crossover

Logical crossover

Bit inversion

Source: http://cs.felk.cvut.cz/~xobitko/ga/ Docsity.com

Permutations

  • Single point crossover - one crossover point is selected, the permutation is copied from the first parent till the crossover point, then the other parent is scanned and if the number is not yet in the offspring, it is added
` ( 1 2 3 4 5 6 7 8 9) + ( 4 5 3 6 8 9 7 2 1) = ( 1 2 3 4 5 6 8 9 7)
  • Order changing Mutation - two numbers are selected and exchanged (1 2 3 4 5 6 8 9 7) => (1 8 3 4 5 6 2 9 7)

Source: http://cs.felk.cvut.cz/~xobitko/ga/ Docsity.com 11