ELECTIVE COURSE -> not offered every semester
Learning Objectives:
Knowledge
The students understand the structure of algorithms which rely on the the concept of evolution.
Skills
In the laboratory the students have learned to implement a genetic algorithm to solve an underlying search or optimization problem.
Competencies
The students have learned, how to solve optimization, search, and other problems with genetic algorithms and know how to deal with problem specific challenges.
Content:
Required key concepts from biology, such as evolution, chromosome, genotype, phenotype, etc..
The structure of a genetic algorithm and genetic operators.
Differences between genetic algorithms and other heuristics, such as hill climbing, simulated annealing, etc..
The theory behind genetic algorithms (schema theorem, implicit parallelism, etc.).
Practical applications for genetic algorithms and specialized genetic operators.
Genetic Programming as an advanced branch of genetic algorithms.
