Evolutionary Computation is an innovative field of computer science dealing with algorithms gleaned from the model of organic evolution. In other words, we are trying to let the computer evolve solutions to problems (in a very general sense) rather than trying to “calculate” them.
Evolutionary algorithms do this by using the principles of evolution such as, for example, selection, mutation, and recombination among a population of simulated individuals. The evolutionary approach is used today in a variety of fields for solving problems that require intelligent behaviour, adaptive learning, and optimisation. These fields include e.g. engineering optimisation, artificial life, automatic programming, autonomous agents, and evolutionary economics.
Due to the already large diversity of the field, we will focus the course on the central material regarding the biological motivation, the main classes of evolutionary algoritms, application possibilities, and some outlook into new fields related to evolutionary computation.
The course will cover, among others, the following topics:
Computer demos of evolutionary algorithms “in action”.
The biological background: Organic evolution.
What is an evolutionary algorithm.
What is optimisation/adaptation.
The main instances of classical evolutionary algorithms, namely
Genetic algorithms,
evolution strategies,
evolutionary programming, and
genetic programming.
These will all be discussed algorithmically and regrding some of the available theorectical knowledge.
Special topics, including
different selection operators,
noisy environments,
dynamic environments,
multicriteria decision making.
New fields, such as e.g.
evolutionary molecular computing,
evolutionary economics,
evolutionary automous agents,
Interactive evolution.
In addition to following the course, students will be offered the possibility to do a voluntary practical programming assignment, which in addition to the exam contributes to the final grade. However, it is your decision whether you weant to do the assignment or not.
During the course, copies of the transparencies will be distributed to all students.
*Literature: * Concerning the literature, the following books are recommended, but they are not mandatory for the course:
Th.Bäck: Evolutionary Algorithms in Theory and Practice: Oxford University Press, New York, 1996.
Th. Bäck, D,D. Fogel, Z. Michalewicz: Handbook of Evolutionary Computation, Vols. 1 and 2, Institute of Physics Publishing, Bristol, Ukm 2000.
W. Banzhaf, P. Nordin, R.E. keller, F.D. Francone: Genetic Programming, Morgan Kaufman Publishers, San Francisco, CA, 1998.
Z. Michalewicz: Genetic Algorithms + Data Structures = Evolution Programs, Springer, Berlin, 1996.
*Website: * http://natcomp.liacs.nl/EA