Genetic Algorithms in Search, Optimization & Machine Learning Lecture 1 David E. Goldberg Department of Industrial & Enterprise Systems Engineering University of Illinois at Urbana-Champaign Urbana, Illinois 61801 [email_address] © 2006 University of Illinois Board of Trustees, All Rights Reserved

Welcome to Genetic Algorithms Genetic Algorithms in Search, Optimization, and Machine Learning. 44 lectures recorded in Studio 403B2 in historic Engineering Hall at the University of Illinois at Urbana-Champaign. An introduction to one of the most exciting fields in engineering and computer science today. Used across the range of human endeavor.

Genetic Algorithms in Search, Optimization, and Machine Learning.

44 lectures recorded in Studio 403B2 in historic Engineering Hall at the University of Illinois at Urbana-Champaign.

An introduction to one of the most exciting fields in engineering and computer science today.

Used across the range of human endeavor.

Today’s Agenda Administrivia: Review course outline and procedures. Establish goals for the course: Yours and mine. Begin a gentle introduction to genetic algorithms.

Administrivia: Review course outline and procedures.

Establish goals for the course: Yours and mine.

Begin a gentle introduction to genetic algorithms.

Syllabus Reading: Course text and papers Homework: Problems & quicky computer assignments Topics Intro, Simple GA code Applicable theory Other Operators, Coding-Constraints Parade of Applications RCGA/ES, GP, GBML Competent GAs, Efficiency

Reading: Course text and papers

Homework: Problems & quicky computer assignments

Topics

Intro, Simple GA code

Applicable theory

Other Operators, Coding-Constraints

Parade of Applications

RCGA/ES, GP, GBML

Competent GAs, Efficiency

Administrative Info Web: http://www.engr.uiuc.edu/OCEE/webcourses/ge531/ TA: See web site for information Webboard: See e-mail instructions Office hours: See syllabus online Email: deg@uiuc.edu Office & phone: See campus directory. IlliGAL: http://www-illigal.ge.uiuc.edu/

Web: http://www.engr.uiuc.edu/OCEE/webcourses/ge531/

TA: See web site for information

Webboard: See e-mail instructions

Office hours: See syllabus online

Email: deg@uiuc.edu

Office & phone: See campus directory.

IlliGAL: http://www-illigal.ge.uiuc.edu/

Academic Info Homework/Project/Final: 10-40-50 Term project on topic of your choosing Computer language of your choice Final: In-class and take-home portion Traditional scale used One final word: late assignments

Homework/Project/Final: 10-40-50

Term project on topic of your choosing

Computer language of your choice

Final: In-class and take-home portion

Traditional scale used

One final word: late assignments

Cousin Bill

Goals: Yours & Mine Why are you here? What prompted you to take the course? What would you like to get out the course? Send short (less than 100 word) goal statement to me by next class period (send as ordinary text to [email_address] ). Mine I want you to be able to read the literature. Apply GAs & evolutionary computation to problems. Capable of original research in the field.

Why are you here? What prompted you to take the course?

What would you like to get out the course?

Send short (less than 100 word) goal statement to me by next class period (send as ordinary text to [email_address] ).

Mine

I want you to be able to read the literature.

Apply GAs & evolutionary computation to problems.

Capable of original research in the field.

A Gentle Introduction What is a GA? Motivation from nature, motivation from artificial search. A brief history. How GAs are different? Mechanics and power of a simple GA. GA terminology and the connection to the real thing.

What is a GA?

Motivation from nature, motivation from artificial search.

A brief history.

How GAs are different?

Mechanics and power of a simple GA.

GA terminology and the connection to the real thing.

What is a Genetic Algorithm (GA)? A genetic algorithm is an adaptation procedure based on the mechanics of natural genetics and natural selection. GAs have 2 essential components: Survival of the fittest Variation

A genetic algorithm is an adaptation procedure based on the mechanics of natural genetics and natural selection.

GAs have 2 essential components:

Survival of the fittest

Variation

Different Flavors of GA Will use the term genetic algorithm generically. Why? Objectively most popular term. Google hits: “ Genetic algorithm”: 3.24M “ Evolutionary computation”: 1.23M “ Genetic programming”: 1.01M “ Evolutionary programming”: 242k “ Evolution strategy”: 110k GA represents 56% of total. Names as historical “schools” of thought. Distinctions less important today.

Will use the term genetic algorithm generically. Why? Objectively most popular term.

Google hits:

“ Genetic algorithm”: 3.24M

“ Evolutionary computation”: 1.23M

“ Genetic programming”: 1.01M

“ Evolutionary programming”: 242k

“ Evolution strategy”: 110k

GA represents 56% of total.

Names as historical “schools” of thought.

Distinctions less important today.

Nature as Problem Solver Beauty-of-nature argument How Life Learned to Live (Tributsch, 1982, MIT Press, Published originally as Wie das Leben leben lernte. Physikalische Technik in der Natur ) Example: Nature as structural engineer (p. 30) stem bamboo insect trachea

Beauty-of-nature argument

How Life Learned to Live (Tributsch, 1982, MIT Press, Published originally as Wie das Leben leben lernte. Physikalische Technik in der Natur )

Example: Nature as structural engineer (p. 30)

stem bamboo insect trachea

The Picture’s Pretty Bleak, Gentlemen Gary Larson, The Far Side

The Range of Life Lichen (symbiosis between alga and fungus) within 400km of S. Pole (-15º C). Alga in hot springs (85ºC). Whales dive to depth of 1000m and stay submerged for 1-2 hours without air. Tuna - 40 knots. Cheetahs - 100 km/hr. Hawks - 200 km/hr. Golden plovers fly 3500 km nonstop across the pacific.

Lichen (symbiosis between alga and fungus) within 400km of S. Pole (-15º C).

Alga in hot springs (85ºC).

Whales dive to depth of 1000m and stay submerged for 1-2 hours without air.

Tuna - 40 knots. Cheetahs - 100 km/hr. Hawks - 200 km/hr.

Golden plovers fly 3500 km nonstop across the pacific.

The Scale of Life Size comparison of different macroscopic organisms. From Tributsch, H. (1982). How Life Learned to Live . Cambridge, MA: MIT Press (p. 26).

Size comparison of different macroscopic organisms. From Tributsch, H. (1982). How Life Learned to Live . Cambridge, MA: MIT Press (p. 26).

Composite Materials Brown Chilean alga uses sandwich structure to achieve stiffness. From Tributsch, H. (1982). How Life Learned to Live . Cambridge, MA: MIT Press (p. 37).

Brown Chilean alga uses sandwich structure to achieve stiffness. From Tributsch, H. (1982). How Life Learned to Live . Cambridge, MA: MIT Press (p. 37).

Natural Tools from Tributsch, H. (1982). How Life Learned to Live . Cambridge, MA: MIT Press (p. 43).

from Tributsch, H. (1982). How Life Learned to Live . Cambridge, MA: MIT Press (p. 43).

Beavers Control Environment from Tributsch, H. (1982). How Life Learned to Live . Cambridge, MA: MIT Press (pp. 18-19).

from Tributsch, H. (1982). How Life Learned to Live . Cambridge, MA: MIT Press (pp. 18-19).

Termite Roundabouts from Tributsch, H. (1982). How Life Learned to Live . Cambridge, MA: MIT Press (p. 43).

Moths: Stealth Technology from Tributsch, H. (1982). How Life Learned to Live . Cambridge, MA: MIT Press (p. 123).

from Tributsch, H. (1982). How Life Learned to Live . Cambridge, MA: MIT Press (p. 123).

Dive, Dive, Dive Species of seaweed uses air bladders to control sunlight received. From Tributsch, H. (1982). How Life Learned to Live . Cambridge, MA: MIT Press (p. 61).

Species of seaweed uses air bladders to control sunlight received. From Tributsch, H. (1982). How Life Learned to Live . Cambridge, MA: MIT Press (p. 61).

Even Surface Tension from Tributsch, H. (1982). How Life Learned to Live . Cambridge, MA: MIT Press (p. 178).

Surface Tension Locomotion Differences in surface tension propel the rove beetle. From Tributsch, H. (1982). How Life Learned to Live . Cambridge, MA: MIT Press (p. 180).

Differences in surface tension propel the rove beetle. From Tributsch, H. (1982). How Life Learned to Live . Cambridge, MA: MIT Press (p. 180).

Inspiration from Nature Bionics (Bionik in German) or Biomimicry as current craze. Use biological organisms as inspiration for design of artificial systems. Ingo Rechenberg at the Technical University of Berlin a longtime practitioner. GAs/EC a form of bionics or biomimicry. Genetic algorithms and evolutionary computation as Ingo Rechenberg

Bionics (Bionik in German) or Biomimicry as current craze.

Use biological organisms as inspiration for design of artificial systems.

Ingo Rechenberg at the Technical University of Berlin a longtime practitioner.

GAs/EC a form of bionics or biomimicry.

Genetic algorithms and evolutionary computation as

Swim of the Penguins: Rechenberg Live Drop Tank

And Then There is Homo Sapiens Gary Larson, The Far Side

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