Differentiable Genetic Programming

Izzo, Dario; Biscani, Francesco; Mereta, Alessio

doi:10.1007/978-3-319-55696-3_3

Cited by 33 publications

(28 citation statements)

References 20 publications

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“…The fixed-length segments of this list represents a connection of a set of predictors to a function. Differentiable Cartesian Genetic Programming (Izzo et al, 2017) applies automatic differentiation to adjust the constant coefficients of the decoded expression. The expressions are evolved by a mutation-only evolutionary algorithm.…”

Section: Comparison With Other Approachesmentioning

confidence: 99%

Interaction–Transformation Evolutionary Algorithm for Symbolic Regression

França

Aldeia

2021

Evolutionary Computation

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Interaction-Transformation (IT) is a new representation for Symbolic Regression that reduces the space of solutions to a set of expressions that follow a specific structure. The potential of this representation was illustrated in prior work with the algorithm called SymTree. This algorithm starts with a simple linear model and incrementally introduces new transformed features until a stop criterion is met. While the results obtained by this algorithm were competitive with the literature, it had the drawback of not scaling well with the problem dimension. This paper introduces a mutation only Evolutionary Algorithm, called ITEA, capable of evolving a population of IT expressions. One advantage of this algorithm is that it enables the user to specify the maximum number of terms in an expression. In order to verify the competitiveness of this approach, ITEA is compared to linear, nonlinear and Symbolic Regression models from the literature. The results indicate that ITEA is capable of finding equal or better approximations than other Symbolic Regression models while being competitive to state-of-the-art non-linear models. Additionally, since this representation follows a specific structure, it is possible to extract the importance of each original feature of a data set as an analytical function, enabling us to automate the explanation of any prediction. In conclusion, ITEA is competitive when comparing to regression models with the additional benefit of automating the extraction of additional information of the generated models.

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Section: Comparison With Other Approachesmentioning

confidence: 99%

Interaction–Transformation Evolutionary Algorithm for Symbolic Regression

França

Aldeia

2021

Evolutionary Computation

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“…Genetic programming is normally considered as a derivative-free method, however in an impressive paper Izzo et al [33] show how it is possible to obtain a complete representation of the differential properties of a program encoded by a genetic programming expression. They applied this to CGP creating a new form of it called called differentiable CGP (dCGP).…”

Section: Differentiable Cgpmentioning

confidence: 99%

“…Differentiable CGP [33] is available in C++ and Python. 10 It includes examples and tutorials on a number of problem types.…”

Section: Softwarementioning

confidence: 99%

Cartesian genetic programming: its status and future

Miller

2019

Genet Program Evolvable Mach

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Cartesian genetic programming, a well-established method of genetic programming, is approximately 20 years old. It represents solutions to computational problems as graphs. Its genetic encoding includes explicitly redundant genes which are wellknown to assist in effective evolutionary search. In this article, we review and compare many of the important aspects of the method and findings discussed since its inception. In the process, we make many suggestions for further work which could improve the efficiency of the CGP for solving computational problems.

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“…One area that has not hitherto received much attention is the important topic of integration of GP into conventional optimization-based applications, such as control, that typically require the computation of derivatives for fast solution. Izzo et al [8] have recently listed a range of of diverse applications of GP that require derivatives for effective solution.…”

Section: Introductionmentioning

confidence: 99%