Design and Architectures for Embedded Systems Prof. Dr. J. Henkel - - PowerPoint PPT Presentation

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Design and Architectures for Embedded Systems Prof. Dr. J. Henkel - - PowerPoint PPT Presentation

Sep 22, 2023 423 likes •501 views

GA 1 Design and Architectures for Embedded Systems Prof. Dr. J. Henkel Prof. Dr. J. Henkel CES - - Chair for Embedded Systems Chair for Embedded Systems CES University of Karlsruhe, Germany University of Karlsruhe, Germany Add- -on

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  • J. Henkel, Univ. of Karlsruhe, WS0809

http://ces.univ-karlsruhe.de GA 1

Design and Architectures for Embedded Systems

  • Prof. Dr. J. Henkel
  • Prof. Dr. J. Henkel

CES CES -

  • Chair for Embedded Systems

Chair for Embedded Systems University of Karlsruhe, Germany University of Karlsruhe, Germany

Add Add-

  • on Slides: Genetic/Evolutionary Algorithm
  • n Slides: Genetic/Evolutionary Algorithm
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SLIDE 2
  • J. Henkel, Univ. of Karlsruhe, WS0809

http://ces.univ-karlsruhe.de GA 2

Genetic & Evolutionary Algorithms

  • Genetic Algorithms belong to the area of evolutionary computing

Genetic Algorithms belong to the area of evolutionary computing which which itself is part of AI itself is part of AI

  • Short history

Short history

  • 1960s: I.

1960s: I. Rechenberg Rechenberg (“Evolution Strategies”) (“Evolution Strategies”)

  • 1975: Genetic Algorithms by John Holland “Adaption in natural an

1975: Genetic Algorithms by John Holland “Adaption in natural and artificial d artificial systems” systems”

  • 1992: J.

1992: J. Koza Koza, Genetic Programming , Genetic Programming

  • Background

Background

  • Rooted in the mechanism of evolution and natural genetics

Rooted in the mechanism of evolution and natural genetics

  • Draws inspirations from the natural search and selection process

Draws inspirations from the natural search and selection process -

  • >

> “survival of the fittest” “survival of the fittest”

  • Based on sequences of the following mechanisms:

Based on sequences of the following mechanisms:

  • Selection

Selection

  • Crossover

Crossover

  • Mutation

Mutation

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SLIDE 3
  • J. Henkel, Univ. of Karlsruhe, WS0809

http://ces.univ-karlsruhe.de GA 3

Selection

A1 A2 . . . AN C1 C2 . . . CN D1 D2 . . . DN B1 B2 . . . BN C1 C2 . . . CN D1 D2 . . . DN B1 B2 . . . BN

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SLIDE 4
  • J. Henkel, Univ. of Karlsruhe, WS0809

http://ces.univ-karlsruhe.de GA 4

Crossover

A1 A2 . . . AN C1 C2 . . . CN B1 B2 . . . BN

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SLIDE 5
  • J. Henkel, Univ. of Karlsruhe, WS0809

http://ces.univ-karlsruhe.de GA 5

Mutation

A1 A2 . . . AN C1 C2 . . . CN D1 D2 . . . DN B1 B2 . . . BN A1 A2 . . . AN C1 C2 . . . CN D1 D2 . . . DN B1 B2 . . . BN X Y

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SLIDE 6
  • J. Henkel, Univ. of Karlsruhe, WS0809

http://ces.univ-karlsruhe.de GA 6

Simple generic GA

Begin Initialize population; While (termination criterion is not satisfied) End Evaluate population; Evaluate population; Select solutions for next population; Perform crossover; Perform mutation;

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SLIDE 7
  • J. Henkel, Univ. of Karlsruhe, WS0809

http://ces.univ-karlsruhe.de GA 7

Summary GA, EA

  • Resembles evolution in natural genetics

Resembles evolution in natural genetics

  • Difference GA <

Difference GA <-

  • > EA

> EA

  • GA: use

GA: use crossover crossover as primary search strategy as primary search strategy

  • EA: use

EA: use mutation mutation as primary search strategy as primary search strategy

  • There is no mathematical foundation for how well GA/EA

There is no mathematical foundation for how well GA/EA

  • ptimize
  • ptimize
  • Try yourself … for example, the “Traveling Sales Man

Try yourself … for example, the “Traveling Sales Man Problem” … Problem” …