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This paper introduces an evolutionary algorithm which uses multiple chromosomes to evolve solutions to a symbolic regression problem. Inspiration for this algorithm is provided by the existence of multiple chromosomes in natural evolution, particularly in plants. A multi-chromosomal system usually requires a dominance system and subsequently dominance in nature and in previous artificial evolutionary systems has also been considered. An implementation of a multi-chromosomal system is presented with initial results which support the use of multi-chromosomal techniques in evolutionary algorithms.

The functionality model is a new approach in enzyme genetic programming which enables the evolution of variable length solutions whilst preserving local context. This paper introduces the model and presents an analysis of crossover and the evolution of program size.

The original mechanism by which evolutionary algorithms were to solve problems was to allow for the gradual discovery of sub-solutions to sub-problems, and the automated combination of these sub-solutions into larger solutions. This latter property is particularly challenging when recombination is performed on genomes encoded as trees, as crossover events tend to greatly alter the original genomes and therefore greatly reduce the chance of the crossover event being beneficial. A number of crossover operators designed for tree-based genetic encodings have been proposed, but most consider crossing genetic components based on their structural similarity. In this work we introduce a tree-based crossover operator that probabilistically crosses branches based on the behavioral similarity between the branches. It is shown that this method outperforms genetic programming without crossover, random crossover, and a deterministic form of the crossover operator in the symbolic regression domain.

Alzheimer’s is a chronic debilitating neurodegenerative disease that is difficult to diagnose; conventional approaches are subjective and can be unreliable. This paper describes work towards an objective assessment that uses an evolutionary algorithm to assess an important symptom of the disease, the loss of visuo-spatial ability. Results are presented for application of the system in assessing the immature visuo-spatial ability of 7-11 year old children, which are used as a model for Alzheimer’s disease patients.

Modelling biological evolvability: implicit

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“Only a life lived for others is a life worthwhile”, Albert Einstein. Population Variation in canonical GP 3Regard man as a mine rich in gems of inestimable value. Education can, alone, cause it to reveal its treasures, and enable mankind to benefit therefrom. Bahá’u’llah The Genetic Programming paradigm, which applies the Darwinian principle of evolution to hierarchical computer programs, has produced promising breakthroughs in various scientific and engineering applications. However, one of the main drawbacks of Genetic Programming has been the often large amount of computational effort required to solve complex problems. There have been various amounts of research conducted to devise innovative methods to improve the efficiency of Genetic Programming. This thesis has three main contributions. It firstly provides a comprehensive overview of the related work to improve the performance of Genetic Programming and classifies these various proposed approaches into categories. Secondly, a new static population variation scheme (PV) is