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Ryan, Conor
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Ryan, Conor
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Ryan, Conor
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Now showing 1 - 10 of 12
- PublicationA GAuGE Approach to Learning DFA from Noisy Samples(2004-06-30)
; ; This paper describes the adaptation of the GAuGE system to classify binary sequences generated by random DFA. Experiments were conducted, which, although not highly successful, illustrate the potential of applying GAuGE like systems to this problem domain.104 - PublicationInvestigating Degenerate Code and Gene Dependency in the GAuGE SystemThis paper explores the topics of gene dependency and degenerate code, and their combined effect in the GAuGE system, a recently introduced position-independent genetic algorithm. To do so, a simulation was used to calculate the average position specifications of all genotype individuals, and the effect of degnerate code on these averages. The results obtained so far suggest that the introduction of degenerate code loosens the dependency between the position coding genes in each individual.
91 - PublicationGenetic Operators and Sequencing in the GAuGE SystemThis paper investigates the effects of the mapping process employed by the GAuGE system on standard genetic operators. It is shown that the application of that mapping process transforms these operators into suitable sequencing searching tools. A practical application is analysed, and its results compared with a standard genetic algorithm, using the same operators. Results and analysis highlight the suitability of GAuGE and its operators, for this class of problems.
392 - PublicationHow Functional Dependency Adapts to Salience Hierarchy in the GAuGE SystemGAuGE is a position independent genetic algorithm that suffers from neither under nor over-specification, and uses a genotype to phenotype mapping process. By specifying both the position and the value of each gene, it has the potential to group important data together in the genotype string, to prevent it from being broken up and disrupted during the evolution process. To test this ability, GAuGE was applied to a set of problems with exponentially scaled salience. The results obtained demonstrate that GAuGE is indeed moving the more salient genes to the start of the genotype strings, creating robust individuals that are built in a progressive fashion from the left to the right side of the genotype.
405Scopus© Citations 6 - PublicationEfficient Crossover in the GAuGE SystemThis paper presents a series of context-preserving crossover operators for the GAuGE system. These operators have been designed to respect the representation of genotype strings in GAuGE, thereby making sensible changes at the genotypic level. Results on a set of problems suggest that some of these operators can improve the maintenance and propagation of building blocks in GAuGE, as well as its scalability, and could be of use to other systems using structural evolving genomes.
341Scopus© Citations 3 - PublicationCrossover, Population Dynamics and Convergence in the GAuGE SystemThis paper presents a study of the effectiveness of a recently presented crossover operator for the GAuGE system. This crossover, unlike the traditional crossover employed previously, preserves the association of positions and values which exists in GAuGE genotype strings, and as such is more adequate for problems where the meaning of an allele is dependent on its placement in the phenotype string. Results obtained show that the new operator improves the performance of the GAuGE system on simple binary problems, both when position-sensitive data is manipulated and not.
360Scopus© Citations 1 - PublicationOn the use of Gene Dependency to Avoid Deceptive TrapsTHis paper presents a new approach to the field of genetic algorithms, basedon the indroduction of dependency between genes, as inspired by Grammatical Evolution. A system based on that approach, LINKGUAGE, is presented, and results reported show how the dependency between genes creates a tight linkage, guiding the system to success on hard deceptive linkage problems.
131 - PublicationFunctional Dependency and Degeneracy: Detailed Analysis of the GAuGE SystemThis paper explores the mapping process of the GAuGE system, a recently introduced position-independent genetic algorithm, that encodes both the positions and the values of individuals at the genotypic level. A mathematical formalisation of its mapping process is presented, and is used to characterise the functional dependency feature of the system. An analysis of the effect of degeneracy in this functional dependency is then performed, and a mathematical theorem is given, showing that the introduction of degeneracy reduces the position specification bias of individuals. Experimental results are given, that backup these findings.
329Scopus© Citations 5 - PublicationZero is not a Four Letter Word: Studies in the Evolution of LanguageWe examine a model genetic system that has features of both genetic programming and genetic regulatory networks, to show how various forms of degeneracy in the genotype-phenotype map can induce complex and subtle behaviour in the dynamics that lead to enhanced evolutionary robustness and can be fruitfully described in terms of an elementary algorithmic 'language'.
381Scopus© Citations 1 - PublicationGrammar Defined Introns: An Investigation Into Grammars, Introns, and Bias in Grammatical EvolutionWe describe an investigation into the design of different grammars on Grammatical Evolution. As part of this investigation we introduce introns using the grammar as a mechanism by which they may be incorporated into Grammatical Evolution. We establish that a bias exists towards certain production rules for each non-terminal in the grammar, and propose alternative mechanisms by which this bias may be altered either through the use of introns, or by changing the degeneracy of the genetic code. The benefits of introns for Grammatical Evolution are demonstrated experimentally.
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