Grammatical evolution

O’Neill, Michael; Ryan, Conor

doi:10.1109/4235.942529

Cited by 706 publications

(356 citation statements)

References 16 publications

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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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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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“…Note that these parameters were proposed before 1970s, and the formulas are obtained manually. At that time, optimization methods in function space such as genetic programming (Koza, 1992), grammatical evolution (O'Neill and Ryan, 2001), and parse-matrix evolution (Luo and Zhang, 2012) have not yet been proposed. Nowadays, the genetic programming and its variants are ready to use.…”

Section: Discussionmentioning

confidence: 99%

Adaptive space transformation: An invariant based method for predicting aerodynamic coefficients of hypersonic vehicles

Luo

Zhang

et al. 2015

Engineering Applications of Artificial Intelligence

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“…Indeed, for some other representations, e.g. grammatical evolution with linear genomes (O'Neill and Ryan, 2001), the implementation of ULTRA should be particularly straightforward.…”

Section: Discussion and Future Workmentioning

confidence: 99%

Uniform Linear Transformation with Repair and Alternation in Genetic Programming

Spector

Helmuth

2014

Genetic and Evolutionary Computation

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Several genetic programming researchers have argued for the utility of genetic operators that act uniformly. By "act uniformly" we mean two specific things: that the probability of an inherited program component being modified during inheritance is independent of the size and shape of the parent programs beyond the component in question; and that pairs of parents are combined in ways that allow arbitrary combinations of components from each parent to appear in the child. Uniform operators described in previous work have had limited utility, however, because of a mismatch between the relevant notions of uniformity and the hierarchical structure and variable sizes of many genetic programming representations. In this chapter we describe a new genetic operator, ULTRA, which incorporates aspects of both mutation and crossover and acts approximately uniformly across programs of variable sizes and structures. ULTRA treats hierarchical programs as linear sequences and includes a repair step to ensure that syntax constraints are satisfied after variation. We show that on the drug bioavailability and Pagie-1 benchmark problems ULTRA produces significant improvements both in problem-solving power and in program size relative to standard operators. Experiments with factorial regression and with the boolean 6-multiplexer problem demonstrate that ULTRA can manipulate programs that make use of hierarchical structure, but also that it is not always beneficial. The demonstrations evolve programs in the Push programming language, which makes repair particularly simple, but versions of the technique should be applicable in other genetic programming systems as well.

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Grammatical evolution

Cited by 706 publications

References 16 publications

On the Avoidance of Fruitless Wraps in Grammatical Evolution

On the Avoidance of Fruitless Wraps in Grammatical Evolution

Adaptive space transformation: An invariant based method for predicting aerodynamic coefficients of hypersonic vehicles

Uniform Linear Transformation with Repair and Alternation in Genetic Programming

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