Evolutionary Computation   אלגוריתמים אבלוציוניים

202-2-5651, Semester Beit, 2011-2012

Lecturer: Prof. Moshe Sipper

Teaching Assistant: Mati Bot

Important Dates
April 30	14-16	Lecture class	Midterm 1
June 25	14-16	Lecture class	Midterm 2
June 27	14-16	Lecture class	Homework Presentations

Announcements
May 13	Assignment 4 has been published
May 13	Assignment 2 grades have been published
Mar 3	Assignment 1 grades have been updated
Mar 1	Assignment 1 grades have been published
Apr 18	Assignment 3 has been published
Apr 2	Assignment 2 has been published
Mar 18	Assignment 1 has been published
Mar 14	Beginning of semester beit

Course Syllabus

evolutionary algorithms, genetic algorithms, genetic programming, evolutionary game design, Darwinian software engineering, fuzzy systems, artificial neural networks

Administrative Details

• Web: http://www.cs.bgu.ac.il/~sipper/courses/ec122/
• Prerequisites (for undergraduates): algorithms, ppl, spl, automata
• Credits: 4.
• Time & Place:
  • Monday 14-16, 34/207
  • Wednesday 14-16, 34/105
• Grade:
  • 29%: Midterm 1
  • 29%: Midterm 2
  • 42%: Mandatory Assignments
• Midterms:
  • You must pass both midterms in order to pass the course.
  • If you miss one midterm, due to valid reasons according to the university regulations (see Section 7.2), then the one midterm you took will be worth 58%.
  • If you miss both midterms, due to valid reasons according to the university regulations (see Section 7.2), then you will have to take an oral exam, which will be worth 58%.
  • If you miss a midterm due to an invalid reason then the midterm's grade will count as 0.
• Assignments:
  • Assignments must be done in pairs or threesomes.
  • You must submit all mandatory assignments (on time) in order to pass the course.
  • Some assignments may be subjected to frontal examination.
  • Assignment weights are not necessarily equal.
  • Extensions will be granted only due to a valid reason as stated in the takanon (extended miluim/hospitalization).
  • Late-submission policy: 1 day late – 5 points off, 2 days late – 10 points off, 3 days late – assignment grade will be 0.
• Sample exam questions.

Introductory Material

• Evolutionary Algorithms
• Genetic and Evolutionary Algorithms and Programming
• הרצאת מבוא של גיא כתבי , בוגר הקורס "אלגוריתמים אבולוציוניים וחיים מלאכותיים", 2006

Popular Press

• Some reports in the popular press

Bibliography

• M. Sipper, Evolved to Win, Lulu, 2011. (freely downloadable)
• M. Sipper, Machine Nature: The Coming Age of Bio-Inspired Computing, McGraw-Hill, New York, 2002.
• A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing, Springer, 1st edition, 2003, Corr. 2nd printing, 2007.
• R. Poli, B. Langdon, & N. McPhee, A Field Guide to Genetic Programming, 2008. (freely downloadable)
• J. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection, MIT Press, Cambridge, MA, 1992.

Also:

• S. Luke, Essentials of Metaheuristics, 2010. (freely downloadable)
• Z. Michalewicz & D.B. Fogel, How to Solve It: Modern Heuristics, 2nd ed. Revised and Extended, 2004.
• Z. Michalewicz. Genetic Algorithms + Data Structures = Evolution Programs. Springer-Verlag, Berlin, 3rd edition, 1996.
• D. Floreano & C. Mattiussi, Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies, MIT Press, 2008.
• A. Tettamanzi & M. Tomassini, Soft Computing: Integrating Evolutionary, Neural, and Fuzzy Systems, Springer-Verlag, Heidelberg, 2001.

Assignments
Read the instructions!
No.	Due	Grades	Hall of Fame
1	Apr 3		Q1: Hershman & Mannasan Q2: Shtechman & Simandoyev Q3: Hershman & Mannasan Q4: Hershman & Mannasan Q5: Hershman & Mannasan
2	Apr 22		Kenan & Drory
3	May 13
4	Jun 3
5	Jun 24[?]

Lessons
Topic		Link(s)
Intro. Evolutionary Computation		Introduction.ppt
Evolutionary Algorithms		What_is_an_EA.ppt
Genetic Algorithms		Genetic_Algorithms.ppt
Local Search Algorithms		Local Search Algorithms.ppt
Evolution Strategies		Evolution_strategies.ppt
How to Work with EAs		Working_with_EAs.ppt
Parameter Control		Parameter_control.ppt
GA Theory		Theory.ppt, Holland's Schema Theorem, Exact Schema Theorem
Intro. Genetic Programming		Genetic_programming.ppt, gecco2003tutorial.pdf, c2003lecture1modified.ppt, burke2003tutorial.pdf
GP Examples		Evolution of Emergent Cooperative Behavior, Genetic_programming.ppt
Bloat, Some GP Theory		Poli's GECCO tutorial (see Field Guide, Ch. 11)
Advanced GP		ADFs, Varieties of GP, Architecture-Altering Operations
Excerpts from Koza's DVD
Coevolving Solutions to the SCS Problem		ecal-puzzle.ppt
Coevolving Sorting Networks		lec06.ppt, [on competitive coevolution: tutorial.floreano.ppt]
Adversarial Search		Game Playing: Adversarial Search
Evolving Game-Playing Strategies		Attaining Human-Competitive Game-Playing with Genetic Programming, GP-Robocode, GP-Rush & GP-FreeCell, GP-RARS
Fuzzy Systems		fuzzy_lecon1_part2.pdf, (ynet link on fuzzy systems)
(Co)Evolving Fuzzy Systems		lecon2.pdf
Multimodal Problems and Spatial Distribution		Multi.ppt
Hybrid Evolutionary Algorithms		Memetic_Algorithms.ppt, memetic agorithms (natalio's slides)
Artificial Neural Networks		Perceptrons & Backpropagation, Self-Organizing Feature Maps (also here and here), Evolving Artificial Neural Networks
Evolving Efficient List Search Algorithms		sublinear-gpea.ppt
Flight of the FINCH through the Java Wilderness		gptp-pr.pdf
Evolving Hyper-Heuristics using Genetic Programming		Hyper-Heuristics.pptx
Evolving Bayesian Networks		evolving bayesian networks.pptx
Evolving Boundary Detectors		BoundaryDetection.pdf