Created by W.Langdon from gp-bibliography.bib Revision:1.5776
We define Evolutionary Algorithms to be those algorithms which employ or model natural evolution. Generally, when an Evolutionary Algorithm fails to produce a satisfactory solution to a problem, it is because the population has prematurely converged to a suboptimal solution. This thesis seeks to improve the performance of Evolutionary Algorithms by reducing the occurrence of premature convergence. All the extensions presented in this thesis are either naturally occurring phenomena, or are methods employed by biologists and / or plant and animal breeders. In all the cases examined in this thesis, it is shown that the less human control there is with evolution, the better a population will perform. A number of standard benchmark problems are examined, and new, biologically-inspired approaches are presented. A new selection scheme involving multiple fitness functions is introduced. This scheme is applied to the optimisation of multi-objective functions and multi-modal functions. Genetic Programming is applied to a new problem area, the autoparallelisation of serial programs, through the use of techniques developed in this thesis. The notion of addditive diploidy, a type of diploidy that occurs naturally in biology, is introduced and applied to Genetic Algorithms. Additive diploidy is shown to outperform traditional, dominance-oriented, diploidy on a difficult test problem. A new benchmark problem for Genetic Programming is introduced. This competition-oriented benchmark permits the direct comparison of two or more possible solutions. In producing individuals for this benchmark, Genetic Programming is also shown to be suitable for the evolution of event driven programs.",
It is general insofar as it covers several EAs, but the algorithm that gets the most coverage is GP.
Supervisor: Gordon Oulsnam",
Genetic Programming entries for Conor Ryan