CSCI 6506 - Genetic Algortihms and Programming

Final Project

• Basically takes the form of a 5 page (max) 2 column IEEE formated paper due at the last lecture of term.
• List of Example projects.

General Course Information

• Course Description
  
• Short GA/ GP Bibliography
  
• Web Resources
  

Assignments

• Assignment 1: Generic ML Concepts - Due Sept 22
• Assignment 2: GP sand pit! -
  • Iris training and test partitions, plus corresponding perl script
  Due Oct 13

Group Presentations

• The presentation ``policy''
• Presentation Groups
• Presentation Material
  • Group 1: possible benefits of a assuming a population based search, Presenting Oct 11.
  • Group 2: symbiotic GA, Presenting Oct 18.
  • Group 3: Coevolution through an 'ideal' opponent, Presenting Oct 25.
  • Group 4: host-parasite model of competitive coevolution, Presenting Nov 1.
  • Group 5: 'reinforced' GP, Presenting Nov 8
  • Group 6: Policy search versus Value function, Presenting Nov 15
  • Group 7: Novelty as an objective, Presenting Nov 22
  • Group 8: Developmental evolution and Task transfer, Presenting Nov 29

Course Presentation Slides

• Machine Learning `101' - Basic ML context (#1)
  
• Simple GA - Goldberg's simple GA by hand (#2)
  
• Job-Shop Scheduling - Significance of Representation (#3)
  
• Subspace clustering - Modularity and Symbiosis (#3)
  
• GP Intro - basic GP (#5)
  
• GP detector for Intrustion Detection System on KDD data set - Efficient training on half a million patterns (#9)
  
• Packet Routing - Insect metaphor verses distributed GA (#10)
  
• Crossover Biases in GP - Code Bloat and evolving parsimonious GP solutions (#11)
  
• Active Learning - Exemplar subsampling under GP (#11)
  
• Coevolutionary Multi-objective GP - Co-operative problem decomposition under supervised learning while scaling to large data sets(#12)
  
• Symbiosis - third form of coevolution. Also related to macro search operators and group fitness/ selection.
• Symbiotic Bid Based GP - hierarchical complexification and context learning in Reinforcement Learning.

Course Notes

• Terminology and Metaphors - Search Spaces (#1)
  
• Holland's Schema Theorem - Why should stochastic algorithms work! (#2)
• GA Application example - Hard C-means Clustering (#3)
  
• Species and Niching - Multimodal problem solving (#4)
  
• Deceptive Problems - Static Formulation and Significance of Deceptive Landscapes(#4)
  
• Grammatical Evolution - GA selection of CFG Production rules for automatic program design(#5)
  
  • Home page for Grammatical Evolution
• Generic solutions through recursion - Problem solving with recursion, without explicit support for iterative instructions(#6)
  
• Performance Metrics for GP - Computational Effort and Statistical metrics for qualifying GP performance(#6)
  
• GP schema thoery - Lower bound GP Schema Theorem for 1-point crossover(#7)
  
• Exact GP Schema Theory - Poli's work on exact schema theorems for 1-point crossover(#8)
  
• Empirical Evidence for GP Schema Theory - Empirical Evidence for GP Lower Bound Schema Theorem(week #8)
  

Course Assignments

• Assignment #1 - Generic machine learning and Evolutionary Computation
  
• Assignment #2 - GP sand pit
  
• Project milestone 1 - presentation: background, definition of outcomes and outline of approach;
• Project milestone 2 - basic implementation;
• Project milestone 3 - update of findings and recommendations;
• Project milestone 4 - presentation of final results and future work.
• Project milestone 5 - due date for final report.

Benchmark Dataset Repositories

• Concise Data Repository - collection of 46 different classification problems from Universal Problem Solvers Inc.
• UCI Machine Learning Repository - If you want the definitive article...