H02D1A
Genetic Algorithms and Evolutionary Computing
Dirk Roose
1st semester ; 4 ECTS points
Context: search and optimization
H02D1A
Genetic Algorithms and Evolutionary Computing
1st semester ; 4 ECTS points
Aims
t to describe genetic algorithms and other evolutionary
strategies for search and optimisation
t to analyse their performance (quality of results, cost)
t to discuss some implementation issues
t to illustrate the methods by solving some model problems
(e.g. traveling salesman problem)
t to present some case studies
(e.g. concept learning, timetabling, ‘artificial life’)
t the student will be able to decide whether these methods are
suited to solve a particular search or optimisation problem,
and how to choose appropriate genetic operators
Genetic Algorithms and Evolutionary Computing
Teaching activities
• 10 lectures (Wed. at 9h)
• 4 exercise & lab sessions (2 groups)
Evaluation / exam
• open book (theory + exercise)
• short presentation of report on project
Project
• important part of the course
• ‘classic’ form: genetic algorithm for traveling salesman problem;
experiments with Matlab (?) code; in groups of 2 students ;
40 hours
• for ambitious students: genetic algorithm for a chosen problem
Prerequisite
basic (bachelor) knowledge in programming, design of algorithms,
and mathematics (analysis, statistics)
Course Material
– Book: Introduction to Evolutionary Computing (A. Eiben and J. Smith)
Natural Computing Series, 2d edition, 2015, Springer (book ; e-book)
– some papers