Grammatical evolution: Evolving programs for an arbitrary language

Ryan, Conor; Collins, John J.; O’Neill, Michael

doi:10.1007/bfb0055930

Cited by 549 publications

(383 citation statements)

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“…This paper details an investigation examining the possibility of evolving the grammar that Grammatical Evolution (GE) [1][2][3][4] uses to specify the construction of a syntactically correct solution. By evolving the grammar that GE uses to specify a solution, one can effectively permit the evolution of the genetic code.…”

Section: Introductionmentioning

confidence: 99%

Grammatical Evolution by Grammatical Evolution: The Evolution of Grammar and Genetic Code

O’Neill

Ryan

2004

Lecture Notes in Computer Science

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Abstract. This study examines the possibility of evolving the grammar that Grammatical Evolution uses to specify the construction of a syntactically correct solution. As the grammar dictates the space of symbols that can be used in a solution, its evolution represents the evolution of the genetic code itself. Results provide evidence to show that the coevolution of grammar and genetic code with a solution using grammatical evolution is a viable approach.

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Section: Introductionmentioning

confidence: 99%

Grammatical Evolution by Grammatical Evolution: The Evolution of Grammar and Genetic Code

O’Neill

Ryan

2004

Lecture Notes in Computer Science

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“…Grammatical Evolution(GE) [10] [6] is an Evolutionary Automatic Programming system that uses a variable length Genetic Algorithm to evolve programs in any language. The key to the system is the manner in which a Backus Naur Form (BNF) grammar is employed to specify the target language, and is used to map the linear genomes into syntactically correct programs.…”

Section: Introductionmentioning

confidence: 99%

On the Avoidance of Fruitless Wraps in Grammatical Evolution

Ryan

Keijzer

Nicolau

2003

Genetic and Evolutionary Computation — GECCO 2003

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Abstract. Grammatical Evolution (GE) is an evolutionary system that employs variable length linear chromosomes to represent computer programs. GE uses the individuals to produce derivation trees that adhere to a Backus Naur Form grammar, which are then mapped onto a program. One unusual characteristic of the system is the manner in which chromosomes can be "wrapped", that is, if an individual has used up all of its genes before a program is completely mapped, the chromosome is reread. While this doesn't guarantee that an individual will map, prior work suggested that wrapping is beneficial for the system, both in terms of increased success rates and a reduced number of invalid individuals. However, there has been no research into the number of times an individual should be wrapped before the system gives up, and an arbitrary upper limit is usually chosen. This paper discusses the different types of grammars that could be used with this system, and indicates the circumstances under which individuals will fail. It then presents a heuristic to minimize the number of wraps that have to be made before the system can determine that an individual will fail. It is shown that this can drastically reduce the amount of wrapping on a pathologically difficult problem, as well as on two classes of grammar often used by the system.

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“…In this paper, we use grammatical evolution [1] to evolve detection rules for dropping attacks on MANETs. In the dropping attack scenario malicious node(s) drop data packets not destined for themselves to disrupt the network connection.…”

Section: The Problemmentioning

confidence: 99%