Now showing 1 - 10 of 12
  • Publication
    On the use of Gene Dependency to Avoid Deceptive Traps
    (AAAI, 2002-07-13) ;
    THis 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.
      54
  • Publication
    A GAuGE Approach to Learning DFA from Noisy Samples
    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.
      55
  • Publication
    Genetic Operators and Sequencing in the GAuGE System
    (IEEE, 2006-07-21) ;
    This 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.
      252
  • Publication
    Investigating Degenerate Code and Gene Dependency in the GAuGE System
    This 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.
      54
  • Publication
    Grammar Defined Introns: An Investigation Into Grammars, Introns, and Bias in Grammatical Evolution
    (Morgan Kauffman, 2001-07-11) ; ;
    We 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.
      83
  • Publication
    Genetic Algorithms Using Grammatical Evolution
    This paper describes the GAUGE system, Genetic Algorithms Using Grammatical Evolution. GAUGE is a position independent Genetic Algorithm that uses Grammatical Evolution with an attribute grammar to dictate what position a gene codes for. GAUGE suffers from neither under-specification nor over-specification, is guaranteed to produce syntactically correct individuals, and does not require any repair after the application of genetic operators. GAUGE is applied to the standard onemax problem, with results showing that its genotype to phenotype mapping and position independence nature do not affect its performance as a normal genetic algorithm. A new problem is also presented, a deceptive version of the Mastermind game, and we show that GAUGE possesses the position independence characteristics it claims, and outperforms several genetic algorithms, including the competent genetic algorithm messyGA.
      303Scopus© Citations 30
  • Publication
    Solving Sudoku with the GAuGE System
    (Springer, 2006-04-12) ;
    This paper presents an evolutionary approach to solving Sudoku puzzles. Sudoku is an interesting problem because it is a challenging logical puzzle that has previously only been solved by computers using various brute force methods, but it is also an abstract form of a timetabling problem, and is scalably difficult. A different take on the problem, motivated by the desire to be able to generalise it, is presented. The GAuGE system was applied to the problem, and the results obtained show that its mapping process is well suited for this class of problems.
      421Scopus© Citations 11
  • Publication
    Zero is not a Four Letter Word: Studies in the Evolution of Language
    We 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'.
      251Scopus© Citations 1
  • Publication
    How Functional Dependency Adapts to Salience Hierarchy in the GAuGE System
    (Springer, 2003-04-16) ;
    GAuGE 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.
      241Scopus© Citations 6
  • Publication
    Efficient Crossover in the GAuGE System
    (Springer, 2004-04-07) ;
    This 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.
      251Scopus© Citations 3