Evolving common LISP programs in a linear-genotype evolutionary computation system

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@InProceedings{Cullen:2009:GEC,

• author = "Jamie Cullen",
• title = "Evolving common LISP programs in a linear-genotype evolutionary computation system",
• booktitle = "GEC '09: Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation",
• year = "2009",
• editor = "Lihong Xu and Erik D. Goodman and Guoliang Chen and Darrell Whitley and Yongsheng Ding",
• bibsource = "DBLP, http://dblp.uni-trier.de",
• pages = "75--80",
• address = "Shanghai, China",
• organisation = "SigEvo",
• DOI = "doi:10.1145/1543834.1543846",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• month = jun # " 12-14",
• isbn13 = "978-1-60558-326-6",
• keywords = "genetic algorithms, genetic programming",
• abstract = "Evolutionary Meta Programming (EMP) is an approach to Evolutionary Computation, which allows freedom of programming language choice in the evolved programs, as well as the ready use of external tools and testbenches, with which to perform fitness evaluation. The current implementation of EMP uses a linear genotype in a manner similar to Grammatical Evolution (GE). In contrast, traditional Genetic Programming (GP) typically uses a subset of the LISP programming language to represent target programs in a tree-based structure. The ability of EMP to leverage external tools and arbitrary languages enables the rapid prototyping of possibly novel approaches to Evolutionary Computation. One such experiment is presented herein: The evolution of Common LISP language constructs using a linear genotype and associated grammar, and evaluation using a real external LISP interpreter. An exploratory study is performed with three classic problems: Symbolic Regression, Ant Trail, and Towers of Hanoi. Solutions to these problems were evolved in both Common LISP and ANSI C versions, and runtime and performance results collected. Present results are relatively unintuitive, when compared to conventional programming wisdom, with some problems apparently favoring a paradigm not traditionally suited to them in a non-evolutionary programming setting.",
• notes = "Also known as \cite{DBLP:conf/gecco/Cullen09} part of \cite{DBLP:conf/gec/2009}",

}

Genetic Programming entries for Jamie Cullen

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