1Foundations of Learning and Adaptive SystemsEvolutionary Algorithms
2Evolutionary AlgorithmsGenetic ProgrammingEvolution StrategiesGenetic AlgorithmsEvolutionaryProgrammingClassifier SystemsEach of these constitutes a different approach, however, they are inspired by the same principles of natural evolution.
3Literature•Goldberg, D. “Genetic Algorithms in Search and OptimizationAddison-Wesley, Reading MA, 1989•Mitchell, M. “An Introduction to Genetic Algorithms”MIT Press, Cambridge, MA, 1996•Koza, J. “Genetic Programming II”MIT Press, Cambridge, MA, 1994•Holland, J. “Adaptation in Natural and Artificial Systems”Univeristy of Michigan Press, Ann Arbor, 1975•Bäck, Th. “Evolutionary Algorithms in Theory and PracticOxford University Press, New York, 1996
5Biological Terminology•Gene•functional entity that codes for a specific feature e.g. skin complexion, eye color •set of possible alleles•Allele•value of a gene e.g. blue, green, brown•codes for a specific variation of the gene/feature•Locus•position of a gene on the chromosome•Genome•set of all genes that define a species•the genome of a specific individual is called genotype•the genome of a living organism is composed of several chromosomes •Population•set of competing genomes/individuals
6Genotype versus Phenotype•Genotype•blue print that contains the information to construct an organism e.g. human DNA•genetic operators such as mutation and recombinationmodify the genotype during reproduction•genotype of an individual is immutable (no Lamarckian evolution)Phenotype•physical make-up of an organism•selection operates on phenotypes Darwin’s principle : “survival of the fittest”
7Genetic Algorithms OverviewGenetic algorithms are inspired by Darwin's theory of natural selection and based on the principle of survival of the fittest Basic components of genetic algorithmsA representation of solutions to the problem