- author = "Rachel Cavill",
- title = "Multi-Chromosomal Genetic Programming",
- school = "Department of Electronics, University of York",
- type = "{PhD} Dissertation",
- year = "2006",
- address = "UK",
- keywords = "genetic algorithms, genetic programming",
-
URL = "
http://ethos.bl.uk/OrderDetails.do?did=7&uin=uk.bl.ethos.437617",
- abstract = "Typically, computational models inspired by evolution have comprised a single large structure, such as a tree, string or graph, representing a single chromosome. Through the use of evolutionary operators, as mutation and recombination, over a number of generations a satisfactory solution to the problem may be found and the evolution halted. In natural, biological systems, it is not so common to find organisms which have only a single chromosome. Indeed, it is only bacteria and other relatively simple life-forms which can survive with only a single chromosome structure. All animals and plants have a much richer and more complex chromosome space, with not only multiple chromosomes, but multiple copies of each chromosome. Within artificial systems, sometimes, often for very problem-specific reasons, multiple structures are used which each make up part of the final solution. More recently, with the area of co-evolution successfully exploring the evolution of teams, further steps along this path towards a richer representation space have been investigated. This thesis investigates the exploitation of evolution with multiple chromosomes within computational models. By studying the biological model presented to us in nature, and attempting to extract the key mechanisms of multi chromosomal evolution an artificial system which imitates these mechanisms is developed. The system is designed to allow evolution with any number of chromosomes, so that experiments comparing evolution with a single chromosome to that with many may be performed. This work is not attempting to model biological evolution but is inspired by it. As well as presenting a richer representation space, the presence of multiple chromosomes also permits more complex evolutionary operators. For instance crossover may work between any pair of chromosomes, or may be restricted to be allowed only to occur between particular pairs of chromosomes. Natural systems too, display a range of crossover operators acting in different ways; therefore it is important to study the implications of using different crossover operators and to assess their relative characteristics and advantages. To this end, this thesis presents a system which allows evolution to occur with a specified number of chromosomes, conforming to k sets of n chromosomes. Using this system, experiments are done over a range of standard genetic programming benchmark problems to ascertain the affects of increasing the number of chromosomes along each of these two axis of variation. Further experiments are conducted into the behaviour of the crossover operator with this more complex representation and various crossover operators are evaluated within the system. Overall, it was found that multiple chromosomes increase the performance of the evolutionary system, insofar as better solutions were obtained more quickly in the simulations. However, in order to attain optimal increases both the number of chromosomes and the number of copies of copies of each in the system, need to be considered. The optimal number of chromosomes is shown to be problem dependent, but initial conclusions about how many chromosomes different types of problems are likely to use are also presented. Additionally, the crossover operator is shown to work best when it is restricted only to work with the exact same chromosome from the other parent.",
-
notes = "
not online? Access from EThOS: Order from print.
uk.bl.ethos.437617",
