title: Evolutionary algorithms and a fractal inverse problem.
abstract: Over the past 30 years, algorithms that model natural evolution have generated robust search methods. These so-called evolutionary algorithms have been successfully applied to a wide range of problems. This paper discusses two types of evolutionary algorithms and their application to a problem in shape representation. Genetic algorithms and evolutionary programming, although both based on evolutionary principles, each place different emphasis on what drives the evolutionary process. While genetic algorithms rely on mimicking specific genotypic transformations, evolutionary programming emphasizes phenotypic adaptation. Results presented show the success of evolutionary programming in solving an example of a fractal inverse problem, but indicate that a genetic algorithm is not as successful. Reasons for this disparity are discussed.
authors.affiliation: Department of Computer Science, University of Durham Science Site, UK.
authors.forename: D J
authors.initials: DJ
authors.lastname: Nettleton
authors.forename: R
authors.initials: R
authors.lastname: Garigliano
mesh_terms: D000465:Algorithms; D005075:Biological Evolution; D017709:Fractals; D008957:Models, Genetic