A Parallel Approach to Grammatical Evolution in Python

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@Misc{Stallard:BSc,

• author = "Michael Stallard",
• title = "A Parallel Approach to Grammatical Evolution in Python",
• year = "2006",
• keywords = "genetic algorithms, genetic programming, Grammatical Evolution",
• URL = "http://www.cs.bath.ac.uk/~mdv/courses/CM30082/projects.bho/2005-6/stallard-mj-dissertation-2005-6.pdf",
• size = "158 pages",
• abstract = "Grammatical Evolution is the creation of computer programs using Evolutionary Computation over the search space of a language grammar. Previous systems have been written in C, C++ or Java, however due to the compiled nature of the languages used in the implementations there is the disadvantage of a clear separation between the Grammatical Evolution system and the generated solutions. Furthermore, these systems lack clarity and readability in their code and design. An implementation of Grammatical Evolution in the Python programming language would be able to take advantage of Python's inherently easy to understand syntax and Python ability execute to itself, producing a neat integration of program production and interpretation. However, Python has the disadvantage that its execution speed is relatively slow. This document details the specification, design, and implementation for a Python Grammatical framework and also documents the experimental analysis of applying parallel computing techniques in order to attempt to alleviate performance issues as a result of using Python. It concludes that a Python Grammatical Evolution framework is a valuable tool, and that parallel computing techniques bring performance gains in terms of both the execution speed and the success rate of a Grammatical Evolution system.",
• notes = "Cited by \cite{Chennupati:2014:NaBIC}. Undergraduate BSc. Bath University, UK",

}

Genetic Programming entries for Michael Stallard

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