Analysing the Genotype-Phenotype Map in Grammatical Evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@PhdThesis{fagan:PhDThesis:2014,

• author = "David Fagan",
• title = "Analysing the Genotype-Phenotype Map in Grammatical Evolution",
• school = "University College Dublin",
• year = "2013",
• address = "Ireland",
• month = "30 " # oct,
• keywords = "genetic algorithms, genetic programming, Grammatical Evolution, piGE",
• URL = "http://ncra.ucd.ie/papers/DavidFaganPhDThesis2014.pdf",
• size = "207 pages",
• abstract = "The Genotype-Phenotype Map (GPM) is an important aspect of the representation in Evolutionary Computing (EC). The GPM decouples the search space of the EC algorithm into a many-to-one mapping, allowing an abstraction of the search and solution spaces, which can bring a number of benefits to search. Grammatical Evolution (GE) is a grammar based form of Genetic Programming (GP) that incorporates a GPM at its core, which is loosely inspired by nature.

  This thesis investigates whether different approaches to the GPM can have a positive effect on GE's performance. By examining a range of GPMs that use differing expansion order principles it was found the one approach, Position Independent Grammatical Evolution (piGE) presented a viable alternative to the canonical GE GPM.

  piGE, while showing good performance, uses a variable expansion order controlled by evolution. This variable ordering increases the size of the search space that must be navigated by piGE during evolution. It is found that piGE gains a significant increase in connectivity by using an evolvable order, while also providing piGE with additional neutrality.

  Knowing what orders piGE uses during evolution may provide insight into new GPM approaches. With this in mind a set of measures are devised, that allow for the monitoring of piGE's population during an evolutionary run. What is found is that piGE doesn't converge to a single order but rather a distribution of GPM orders.

  The addition of the evolvable order in piGE provides an added degree of freedom in the mapping that is not exploited by standard genetic operations. A mutation operation is presented that will allow the algorithm to focus mutation on certain aspects of the piGE chromosome. It is found that with this ability the performance of piGE is increased.",

• notes = "Supervisor: Michael O'Neill",

}

Genetic Programming entries for David Fagan

Citations