Benchmarking state-of-the-art symbolic regression algorithms

Žegklitz, Jan; Pošík, Petr

doi:10.1007/s10710-020-09387-0

Cited by 28 publications

(22 citation statements)

References 22 publications

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“…Let us focus on symbolic regression, proposed by Koza (1992). More recent examples can be found, for example, in the paper by Awange and Paláncz (2016); Gajdoš and Zelinka (2014), or Žegklitz and Pošík (2020). If one sets as the structure elements the mathematical expressions, producing new objects (children) consists of exchanging the parts of the initial objects between each other (cross-over).…”

Section: Genetic Algorithmsmentioning

confidence: 99%

A Review of the Applications of Genetic Algorithms to Forecasting Prices of Commodities

Drachal

Pawłowski

2021

Economies

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This paper is focused on the concise review of the specific applications of genetic algorithms in forecasting commodity prices. Genetic algorithms seem relevant in this field for many reasons. For instance, they lack the necessity to assume a certain statistical distribution, and they are efficient in dealing with non-stationary data. Indeed, the latter case is very frequent while forecasting the commodity prices of, for example, crude oil. Moreover, growing interest in their application has been observed recently. In parallel, researchers are also interested in constructing hybrid genetic algorithms (i.e., joining them with other econometric methods). Such an approach helps to reduce each of the individual method flaws and yields promising results. In this article, three groups of commodities are discussed: energy commodities, metals, and agricultural products. The advantages and disadvantages of genetic algorithms and their hybrids are presented, and further conclusions concerning their possible improvements and other future applications are discussed. This article fills a significant literature gap, focusing on particular financial and economic applications. In particular, it combines three important—yet not often jointly discussed—topics: genetic algorithms, their hybrids with other tools, and commodity price forecasting issues.

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Section: Genetic Algorithmsmentioning

confidence: 99%

A Review of the Applications of Genetic Algorithms to Forecasting Prices of Commodities

Drachal

Pawłowski

2021

Economies

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“…The benchmarks selected for the comparison were taken from [22]. These were selected since comprehend both synthetic and real world data, hence they cover for i in number of niches do 3:…”

Section: Benchmarksmentioning

confidence: 99%

“…end for 7: end while a sufficient variety of problems. More on the benchmarks can be found in [22]. In this work the same number of samples and sampling technique to produce them as in [22] were used, except for the benchmarks korns11, where 5000 samples were used instead of 10000 to reduce the computational time, both on training and testing samples; and for the benchmark S2 where the same number of training samples used for the x variable were also used for the y one.…”

Section: Benchmarksmentioning

confidence: 99%

Inclusive Genetic Programming

Marchetti

Minisci

2021

Lecture Notes in Computer Science

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The promotion and maintenance of the population diversity in a Genetic Programming (GP) algorithm was proved to be an important part of the evolutionary process. Such diversity maintenance improves the exploration capabilities of the GP algorithm, which as a consequence improves the quality of the found solutions by avoiding local optima. This paper aims to further investigate and prove the efficacy of a GP heuristic proposed in a previous work: the Inclusive Genetic Programming (IGP). Such heuristic can be classified as a niching technique, which performs the evolutionary operations like crossover, mutation and selection by considering the individuals belonging to different niches in order to maintain and exploit a certain degree of diversity in the population, instead of evolving the niches separately to find different local optima. A comparison between a standard formulation of GP and the IGP is carried out on nine different benchmarks coming from synthetic and real world data. The obtained results highlight how the greater diversity in the population, measured in terms of entropy, leads to better results on both training and test data, showing that an improvement on the generalization capabilities is also achieved.

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“…Learning symbolic expressions have been extensively considered for regression tasks [29] in which an interpretable model is fitted to data. Nevertheless, discovering symbolic expressions can be extended to solve general optimisation problems.…”

Section: Related Workmentioning

confidence: 99%