We describe a Genetic Algorithm called Grammatical Evolution (GE) that can evolve complete programs in an arbitrary language using a variable length linear genome. The binary genome determines which production rules in a Backus Naur Form grammar deenition are used in a genotype to phenotype mapping process to a program. Expressions and programs of arbitrary complexity may be evolved using this ...

Grammatical Evolution is related to the idea of genetic programming in that the objective is to find an executable program or function. GE offers a solution by evolving solutions according to a user specified grammar (BackusNaur Form). In this paper GE is used to construct a classifier for some well known datasets. and curve fitting problems without the need to assume the equation shape.

Evolving classification models for prediction of patient recruitment in multicentre clinical trials using grammatical evolution Gilyana Borlikova, Michael Phillips, Louis Smith, Michael O'Neill Successful and timely completion of prospective clinical trials depends on patient recruitment as patients are critical to delivery of the prospective trial data. There exists a pressing need to develop ...

We propose a Grammatical Evolution approach to the automatic design of Ant Colony Optimization algorithms. The grammar adopted by this framework has the ability to guide the learning of novel architectures, by rearranging components regularly found on human designed variants. Results obtained with several TSP instances show that the evolved algorithmic strategies are effective, exhibit a good g...

This study attempts to decompose the behaviour of mutation in Grammatical Evolution (GE). Standard GE mutation can be divided into two types of events, those that are structural in nature and those that are nodal. A structural event can alter the length of the phenotype whereas a nodal event simply alters the value at any terminal (leaf or internal node) of a derivation tree. We analyse the beh...

This study examines the utility of employing digit concatenation, as distinct from the traditional expression based approach, for the purpose of evolving constants in Grammatical Evolution. Digit concatenation involves creating constants (either whole or real numbers) by concatenating digits to form a single value. The two methods are compared using three different problems, which are finding a...

We present an alternative mapping function called the Bucket Rule, for Grammatical Evolution, that improves upon the standard modulo rule. Grammatical Evolution is applied to a set of standard Genetic Algorithm problem domains using two alternative grammars. Applying GE to GA problems allows us to focus on a simple grammar whose effects are easily analysable. It will be shown that by using the ...