Syllabus
Models of evolution, basic approaches and notions. Population, recombination, fitness evaluation.
Genetic algorithms, solution encoding in a chromozome, basic operators of selection, mutation, crossover.
Selection, objective function, dynamic vs. static, roulette-wheel selection, tournaments, elitism.
Schema theorem, building block hypotheses, implicit paralallelism.
Probabilistic models of simple genetic algorithm, finite and infinite population.
Machine learning and data mining, evoluion of expert systems, internal representation, Michigan vs. Pittsburg approach.
Clasifier systems, if-then rules, bucket brigade algorithm, Q-learning, production systems.
Annotation
Models of evolution, genetic algorithms, representation and operators of selection, mutation and crossover. Problem solving by means of evolutionary computation.
Theoretical properties of simple genetic algorithm. Schemata theorem and building block hypothesis, probabilistic models.
Evolutionarz machine learning, Michigan vs. Pittsburg approach, classifier systems.