Introduction to Evolutionary Computing
A.E. Eiben and J.E. Smith

Outlines of an EC course 

In setting up these outlines we assume an academic course for students of exact sciences, e.g., computer science, artificial intelligence, mathematics, engineering, and alike, with a practical flavour. Obviously, a different audiance (biology students or a business course) requires another setup. Additionally, the duration of the course is also crucial is determining how much material can be treated. To this end we specify a course at three levels, basic, extended, and full. These are indicated by the following colour scheme:


Subject Corresponding book sections Corresponding slides
Introduction to EC Chapter 1 Introduction.ppt
Evolutionary Algorithms Chapter 2 (available as pdf file) What_is_an_EA.ppt
Genetic Algorithms Chapter 3 Genetic_Algorithms.ppt
Evolution Strategies Chapter 4 Evolution_strategies.ppt
Evolutionary Programming Chapter 5 Evolutionary_programming.ppt
Genetic Programming Chapter 6 Genetic_programming.ppt
How to work with EAs Chapter 14 Working_with_EAs.ppt
Parameter control in EC Chapter 8 Parameter_control.ppt
Multimodal & multiobjective problems Chapter 9 Multi.ppt
Memetic (or hybrid) algorithms Chapter 10 Memetic_Algorithms.ppt
Constraint handling Chapter 12 not on-line yet
Theory Chapter 11 Theory.ppt
Classifier systems Chapter 7 not on-line yet
Coevolution Section 13.2 not on-line yet
Interactive evolution Section 13.3 evol-art.ppt
Nonstationary optimisation Section 13.4 not on-line yet