Current GP frameworks are highly effective on a range of real and simulated benchmarks. However, due to the high dimensionality of the genotypes for GP, the task of visualising the fitness landscape for GP search can be difficult. This paper describes a new framework: Floating Point Grammatical Evolution (FP-GE) which uses a single floating point genotype to encode an individual program. This encoding permits easier visualisation of the fitness landscape arbitrary problems by providing a way to map fitness against a single dimension. The new framework also makes it trivially easy to apply continuous search algorithms, such as Differential Evolution, to the search problem. In this work, the FP-GE framework is tested against several regression problems, visualising the search landscape for these and comparing different search meta-heuristics.
In more closely related work, Fonlupt @cite_1 encoded linear GP @cite_13 using a set of four floating point numbers. Their work differs from FP-GE both in the cardinality of its representation and the ability of FP-GE to work with arbitrary grammars. In contrast, to the four dimensions used in the above work, the single floating point genome used in FP-GE makes plotting the fitness landscape relatively straightforward.
@cite_13 @cite_1
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Linear Genetic Programming presents a variant of Genetic Programming that evolves imperative computer programs as linear sequences of instructions, in contrast to the more traditional functional expressions or syntax trees. Typical GP phenomena, such as non-effective code, neutral variations, and code growth are investigated from the perspective of linear GP. This book serves as a reference for researchers; it includes sufficient introductory material for students and newcomers to the field. The most widely used scheme in GP was proposed by Koza, where programs are represented as Lisp-like trees and evolved by a genetic algorithm. Many other paradigms were devised these last years to automatically evolve programs. For instance, linear genetic programming (LGP) [6] is based on an interesting feature: instead of creating program trees, LGP directly evolves programs represented as linear sequences of imperative computer instructions. LGP is successful enough to have given birth to a derived commercial product named discipulus. The representation (or genotype) of programs in LGP is a bounded-length list of integers. These integers are mapped into imperative instructions of a simple imperative language (a subset of C for instance).