A Structure Preserving Crossover In Grammatical Evolution

Abstract

Grammatical Evolution is an algorithm for evolving complete programs in an arbitrary language. By utilising a Backus Naur Form grammar the advantages of typing are achieved. A separation of genotype and phenotype allows the implementation of operators that manipulate (for instance by crossover and mutation) the genotype (in Grammatical Evolution -a sequence of bits) irrespective of the genotype to phenotype mapping (in Grammatical Evolution -an arbitrary grammar). This paper introduces a new type of crossover operator for Grammatical Evolution. The crossover operator uses information automatically extracted from the grammar to minimise any destructive impact from the crossover. The information, which is extracted at the same time as the genome is initially decoded, allows the swapping between entities of complete expansions of non-terminals in the grammar without disrupting useful blocks of code on either side of the two point crossover. In the domains tested, results confirm that the crossover is (i) more productive than hill-climbing; (ii) enables populations to continue to evolve over considerable numbers of generations without intron bloat; and (iii) allows populations (in the domains tested) to reach higher fitness levels, quicker.

Evolutionary design using grammatical evolution and shape grammars: designing a shelter

“…The flexibility of GE is such that even with the presence of the genotypephenotype map, traditional tree-based search operators such as crossover and mutation can be adopted in place of the genotype search operators, effectively transforming GE into a standard form of GP with the grammar used during the initialization of the population (Harper and Blair, 2005). It is also possible to use both genotypeand phenotype-focussed search operators, combining the benefits of each approach.…”

Multi-objective competitive coevolution for efficient GP classifier problem decomposition

“…There are obvious similarities between GE and GP; however, GE does not operate directly on the expression phenotypes themselves as in traditional GP; rather the programs are stored as a series of Backus-Naur form (BNF) grammar rule selectors, which are in turn indirectly represented by a fixed length binary string genotype. In this sense GE is similar to GA however, the model does benefit from the utilization of context aware variation operators that minimize the disruption of decoded genotypes [9]. Since the algorithm presented here is not specific to GE and because of the numerous similarities, the terms GE and GP are used interchangeably in this paper.…”

Acceleration of grammatical evolution using graphics processing units

“…GE is a population-based, iterative, stochastic algorithm that uses genetic operators known from other methods of the evolutionary computation [8]. It is formed by the following steps: selection ensures propagation of fitter individuals to the next generation, which allows convergence towards a solution crossover is responsible for mixing good features of individuals together [7] mutation includes small changes into genotype which has a positive effect for overcoming local extremes [2,3] genotype-phenotype mapping is a process of rewriting a genotype (binary string or array of integers) into a phenotype (evolved program) using the user-defined grammar evaluation is a problem-dependent process of getting the fitness value of individual's phenotype. In this paper, we focus on symbolic regression, which can be defined as a sum of local differences in a set of data points:…”