Location: McCain 2198 2.35pm to 3.55pm

- 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.

- Assignment 1: Generic ML Concepts - Due 25 Sept (midnight; email mheywood@cs.dal.ca)
- Assignment 2: GP sand pit! -
- Koza's Canonical Tree structured GP
- Local copy of lilgp distribution
- Thyroid data set and default interface for lilgp (Part 2 of assignment)
- Some basic comments regarding the additional lilgp interface.

- The presentation ``policy''
- Presentation Groups
- Presentation Material
- Group 1: Symbiotic GA, Presenting Oct 16
- Group 2: Linkage learning and the Extended Compact GA, Presenting Oct 23
- Group 3: Strength Pareto Evolutionary Algorithm, Presenting Oct 30
- Group 4: GP and Generalization, Presenting Nov 1
- Group 5: On NeuroEvolution..., Presenting Nov 6
- Group 6: Evolving keepaway soccer players through task decomposition, Presenting Nov 20
- Group 7: Evolving Java bytecode, Presenting Nov 22

- 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 - GP and constructing models (#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.

- 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)

- 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)

- 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.

Deliverable | - Assign. 1 - | - Assign. 2 - | - Proj. 1 - | - Proj. 2 - | - Proj. 3 - | - Proj. 4 - | - Proj. 5 - |
---|---|---|---|---|---|---|---|

Due Date | Sept 25 | Oct 11 | Oct 11 | Nov 1 | Nov 22 | Dec 4 | Dec 11 |

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

Author - Malcolm Heywood