Evolutionary Computation
​אלגוריתמים אבולוציוניים

202-2-5651, ​Spring 2021 סמסטר ב

Lecturer: Prof. Moshe Sipper

Administrative Details

• Prerequisites: Automata, Systems Programming, Algorithms, PPL
• Highly recommended prerequisite: Python programming
• Credits: 4
• ​​Grade:
  • 18%: Midterm 1
  • 19%: Midterm 2
  • 29%: Presentation
  • 34%: Project
• You must pass all above 4 course components in order to pass the course.
• Midterm:​
  • If you miss a midterm due to a valid reason according to the university regulations (see Section 7.2), then you will take an oral makeup exam at the lecturer's office, at a date and time decided by the lecturer.
  • If you miss a midterm due to an invalid reason then the midterm's grade will count as 0.
  • ​Sample midterm questions.
• Presentation:
  • Each student will present, on their own, a topic/paper from the research literature.
  • The presentation topic/paper must be approved by the lecturer.
  • You must make a selection by April 4.
  • If you do not make a selection by April 11, 10 points will be taken off the final grade.
  • You can pick a time slot through moodle.
  • Presentation length: 8-10 minutes.
  • Scoring rubric: Organization (6), Knowledge (6), Text (6), Graphics (6), Elocution (4), Eye Contact (1).
• Project:
  • The project must be done in pairs or threesomes.
  • The topic must be approved by the lecturer.
  • A report must be submitted by the end of the semester (June 18).
  • The report must include the following seven sections:
    1. A short introduction of the domain being investigated.
    2. A description of the problem or phenomenon studied.
    3. An explanation of the methods and algorithms employed.
    4. An overview of the software (not a listing of the code).
    5. An account of the results obtained.
    6. Some interesting conclusions.
    7. Bibliographic references.
  • Language: English or Hebrew.
  • Length: 6-8 pages.
  • Don't include the code in the report.
  • Upload the report to the course moodle as a PDF file.

Schedule (may change)
Mar 1/ lecture
Mar 3 / lecture
Mar 8/ lecture
Mar 10/ lecture
Mar 15/ lecture
Mar 17/ lecture
Mar 22/ lecture
Mar 24 / pesach
Mar 29/ pesach
Mar 31/ pesach
Apr 5/ lecture
Apr 7/ midterm 1+lecture
Apr 12/ lecture
Apr 14/ yom hazikaron
Apr 19/ lecture
Apr 21/ lecture
Apr 26/ lecture
May 3/ lecture
May 5/ lecture
May 10/ lecture
May 12/ lecture
May 17/ shavuot
May 19/ midterm 2+lecture
May 24/ presentations
May 26/ presentations
May 31/ presentations
Jun 2/ presentations
Jun 7/ project
Jun 9/ project
​Jun 14/ project
Jun 16/ project

Class Material

• Slides of all chapters of "Introduction to Evolutionary Computation"
• ​tiny_ga
• ​NSGA II​
• Schema theorem
• Local Search Algorithms
• GP: Koza, Koza Tutorial, Koza & Poli, Yoo, Herrmann
• tiny_gp
• Linear GP / Cartesian GP / Grammatical Evolution
• ​Koza's vids
• GP Theory: Poli's Tutorial (see Field Guide, Ch. 11) / ​GP Exact Schema Theorem​
• Evolution of Emergent Cooperative Behavior​
• Games: Adversarial Search / Attaining Human-Competitive Game-Playing with GP / GP-Robocode / GP-RARS / GP-Rush & GP-FreeCell
• Novelty search
• Machine learning: logistic regression, Python, evaluation, dataset splits, cross-validation, performance measures, bias/variance tradeoff, visualization, ROC-AUC
• Supervised learning: models, features, objectives, model training, overfitting, regularization, classification, regression, gradient descent, KNN, linear/logistic regression, decision trees, bagging, ensembling, boosting
• ​Unsupervised learning: clustering, k-means​
• ML as part of (the) data science (pipeline)
• Darwinian Software Engineering
• Varieties of GP
• Architecture-Altering Operations
• Coevolving Sorting Networks
• Competitive Coevolution
• Coevolving Solutions to the SCS Problem​

Literature, vids, datasets, and other resources