Better GP benchmarks: community survey results and proposals

White, David R.; McDermott, James; Castelli, Mauro; Manzoni, Luca; Goldman, Brian W.; Kronberger, Gabriel; Jaśkowski, Wojciech; O’Reilly, Una-May; Luke, Sean

doi:10.1007/s10710-012-9177-2

Cited by 198 publications

(140 citation statements)

References 45 publications

(43 reference statements)

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“…The method also needs to be validated in more complex symbolic regression problems, such as those suggested as GP benchmarks (White et al, 2013). Finally, other methods for combining different solutions are worth further investigation.…”

Section: Discussionmentioning

confidence: 99%

Sequential Symbolic Regression with Genetic Programming

Oliveira

Otero

Pappa

et al. 2015

Genetic and Evolutionary Computation

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This chapter describes the Sequential Symbolic Regression (SSR) method, a new strategy for function approximation in symbolic regression. The SSR method is inspired by the sequential covering strategy from machine learning, but instead of sequentially reducing the size of the problem being solved, it sequentially transforms the original problem into potentially simpler problems. This transformation is performed according to the semantic distances between the desired and obtained outputs and a geometric semantic operator. The rationale behind SSR is that, after generating a suboptimal function f via symbolic regression, the output errors can be approximated by another function in a subsequent iteration. The method was tested in eight polynomial functions, and compared with canonical genetic programming (GP) and geometric semantic genetic programming (SGP). Results showed that SSR significantly outperforms SGP and presents no statistical difference to GP. More importantly, they show the potential of the proposed strategy: an effective way of applying geometric semantic operators to combine different (partial) solutions, avoiding the exponential growth problem arising from the use of these operators.

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Section: Discussionmentioning

confidence: 99%

Sequential Symbolic Regression with Genetic Programming

Oliveira

Otero

Pappa

et al. 2015

Genetic and Evolutionary Computation

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“…In order to test these approaches, both GE and CFG-GP were applied to three recommended symbolic regression benchmarks [26]:…”

Section: Methodsmentioning

confidence: 99%

Managing Repetition in Grammar-Based Genetic Programming

Nicolau

Fenton

2016

Proceedings of the Genetic and Evolutionary Computation Conference 2016

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Grammar-based Genetic Programming systems are capable of generating identical phenotypic solutions, either by creating repeated genotypic representations, or from distinct genotypes, through their many-to-one mapping process. Furthermore, their initialisation process can generate a high number of duplicate individuals, while traditional variation and replacement operators can permit multiple individuals to percolate through generations unchanged. This can lead to a high number of phenotypically identical individuals within a population. This study investigates the frequency and effect of such duplicate individuals on a suite of benchmark problems. Both Grammatical Evolution and the CFG-GP systems are examined. Experimental evidence suggests that these useless evaluations can be instead be used either to speed-up the evolutionary process, or to delay convergence.

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“…This problem has also been used for benchmarking (Harper, 2012), and has been recommended as a replacement for "toy" problems such as symbolic regression of the quartic polynomial (McDermott et al, 2012;White et al, 2013).…”

Section: Methodsmentioning

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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Better GP benchmarks: community survey results and proposals

Cited by 198 publications

References 45 publications

Sequential Symbolic Regression with Genetic Programming

Sequential Symbolic Regression with Genetic Programming

Managing Repetition in Grammar-Based Genetic Programming

Uniform Linear Transformation with Repair and Alternation in Genetic Programming

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