Evolutionary Computation, 2017/2018

Index

Instructor

Fernando Lobo (fernando.lobo@gmail.com), Ed. I, room 1.64.

Course description

Evolutionary Computation can be considered as a sub-field of Artificial Intelligence. Evolutionary algorithms use Nature as a metaphor and are inspired in the principles of natural selection and genetics. These algorithms have been applied successfully for solving difficult problems across a broad spectrum of fields, including engineering, economics and finance, architecture, design, automatic programming, art generation, and many others. In this course, you will learn the basic working principles of these algorithms.

Syllabus

Bibliography

I will not follow a particular book for this course. But I will indicate specific chapters from the books below.

Class notes and documentation

Slides

  1. A very quick introduction to Evolutionary Computation
  2. Genetic Algorithms: Introduction
  3. Genetic Algorithms: Commonly Used Selection, Replacement, and Variation Operators
  4. Evolution Strategies, a tutorial by Thomas Bäck, presented at the ACM GECCO Conference in 2010.
  5. Powerpoint file for chapter 4 of IEC book by A. E. Eiben, additional slides from that book made by the same author are available at http://www.cs.vu.nl/~gusz/ecbook/ecbook-course.html

Articles

For those articles without a link, I can provide you a copy upon request. Some of the articles only have free access inside the university.

  1. Genetic Algorithms, by Martin Pelikan.

Outline lecture by lecture

Main lectures

Note: In the suggested reading colum,

lecture # date contents suggested reading
01 19/Sep/17 Course introduction. What is evolutionary computation? What is it good for? Analogies between the natural and the artificial. IEC (chap 1,2).
EC-1 (chap 1,2,6).
02 26/Sep/17 Illustration of a sample application: evolving an image with polygons. Introduction to genetic algorithms. Major components: selection, recombination, mutation, replacement. Mechanics of binary tournament selection, one-point crossover, and bit-flip mutation. Simulating a genetic algorithm by hand on the onemax problem. IEC (chap 3).
Pelikan's tech report.
GASOML (chap 1,3).
03 27/Sep/17 Another application example: a network expansion problem.
Proportionate selection methods: roulette wheel, stochastic universal sampling (SUS). Drawbacks of proportionate-based selection methods.
Readings from lecture 2 + EC-1 (chap 24, 25).
04 03/Oct/17 Ordinal-based selection methods: tournament selection (with and without replacement), truncation selection. Replacement strategies: random and worst. Readings from lecture 2 + EC-1 (chap 24, 25).
05 04/Oct/17 Commonly used variation operators in genetic algorithms for binary and k-ary string codes. Approaches for handling other representations. Mapping real-valued representations to binary codes and its limitations. EC-1 (chap 32.2, 33.2).
06 10/Oct/17 Commonly used variation operators for manipulating real-valued vectors directly: Gaussian mutation. Discrete, Arithmetic, and Simulated Binary Crossover (SBX). EC-1 (chap 32.2, 33.2).
SBX paper.
07 11/Oct/17
08 17/Oct/17 Variation operators for manipulating permutations. Swap, Inversion, and Scramble mutation. Partially Matched Crossover (PMX), Uniform Order-Based Crossover. Edge Recombination: a problem specific operator for the Traveling Salesman Problem. EC-1 (chap 32.3, 33.3). IEC (chap 3.5.4).
09 18/Oct/17 Introduction to Evolution Strategies. Historical perspective. Major differences with respect to genetic algorithms. The (1+1)-ES algorithm and the 1/5-success rule. ES(mu+lambda) and ES(mu,lambda). EC-1 (chap 6.4, 9).
IEC (chap 4).
ES paper by Hansel et al.

Labs

lecture # date contents
01 26/Sep/17 Introduction to the Mooshak platform for programming assignments. Programming assignment 1.
02 03/Oct/17 Programming assignment 2.
03 10/Oct/17 Programming assignment 3.
04 17/Oct/17 Time to finish previous programming assignments.

Programming Assignments

Here's the link to mooshak.

Grading

The grading of this course is based on programming assignments, a final exam and a project. The project consists of applying evolutionary computation to an application domain of your own choice. You can also choose to work on a theory-related topic in case you want to. The project is individual.

For the project component, you'll have to:

This process mimics what happens when researchers (professors, doctoral and master students) submit their work to scientific conferences worldwide. This should be helpful for getting you started in doing research.

Your project will be graded based on the quality of your writing, the proper design, application, and testing of EC techniques, the quality of your code, the clarity of your oral presentation, and the quality of the review.

The final grade will be computed as follows:

Tips for your project

You may want to have a look at the following links to get ideas for you class project.

As a final note, you may want to check David Goldberg's Technical Writing for Fun & Profit for tips on writing technical reports.