The Effect of Distinct Geometric Semantic Crossover Operators in Regression Problems

Albinati, Julio; Pappa, Gisele L.; Otero, Fernando E. B.; Oliveira, Luiz Otávio Vilas Bôas

doi:10.1007/978-3-319-16501-1_1

Cited by 17 publications

(17 citation statements)

References 12 publications

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“…The experimental test bed is composed of datasets selected from the UCI machine learning repository [18], GP benchmarks [19] and a previous work involving GSGP [20], as presented in the Table II. The categorical attributes from the Computer Hardware and the Forest Fires datasets were removed for compatibility purpose.…”

Section: Resultsmentioning

confidence: 99%

Revisiting the Sequential Symbolic Regression Genetic Programming

Oliveira

Otero

Miranda

et al. 2016

2016 5th Brazilian Conference on Intelligent Systems (BRACIS)

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Abstract-Sequential Symbolic Regression (SSR) is a technique that recursively induces functions over the error of the current solution, concatenating them in an attempt to reduce the error of the resulting model. As proof of concept, the method was previously evaluated in one-dimensional problems and compared with canonical Genetic Programming (GP) and Geometric Semantic Genetic Programming (GSGP). In this paper we revisit SSR exploring the method behaviour in higher dimensional, larger and more heterogeneous datasets. We discuss the difficulties arising from the application of the method to more complex problems, e.g., overfitting, along with suggestions to overcome them. An experimental analysis was conducted comparing SSR to GP and GSGP, showing SSR solutions are smaller than those generated by the GSGP with similar performance and more accurate than those generated by the canonical GP.

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Section: Resultsmentioning

confidence: 99%

Revisiting the Sequential Symbolic Regression Genetic Programming

Oliveira

Otero

Miranda

et al. 2016

2016 5th Brazilian Conference on Intelligent Systems (BRACIS)

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“…Rows with missing values are omitted. Most datasets are taken from the UCI Machine Learning repository 2 , with exception for Dow Chemical and Tower, which come from GP literature [33,34].…”

Section: Methodsmentioning

confidence: 99%

On explaining machine learning models by evolving crucial and compact features

Virgolin

Alderliesten

Bosman

2020

Swarm and Evolutionary Computation

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Feature construction can substantially improve the accuracy of Machine Learning (ML) algorithms. Genetic Programming (GP) has been proven to be effective at this task by evolving non-linear combinations of input features. GP additionally has the potential to improve ML explainability since explicit expressions are evolved. Yet, in most GP works the complexity of evolved features is not explicitly bound or minimized though this is arguably key for explainability. In this article, we assess to what extent GP still performs favorably at feature construction when constructing features that are (1) Of small-enough number, to enable visualization of the behavior of the ML model; (2) Of small-enough size, to enable interpretability of the features themselves; (3) Of sufficient informative power, to retain or even improve the performance of the ML algorithm. We consider a simple feature construction scheme using three different GP algorithms, as well as random search, to evolve features for four ML algorithms, including support vector machines and random forest. Our results on 20 datasets pertaining to classification and regression problems show that constructing only two compact features can be sufficient to rival the use of the entire original feature set. We further find that a modern GP algorithm, GP-GOMEA, performs best overall. These results, combined with examples that we provide of readable constructed features and of 2D visualizations of ML behavior, lead us to positively conclude that GP-based feature construction still works well when explicitly searching for compact features, making it extremely helpful to explain ML models.

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“…Despite the unimodal fitness landscape, the randomness present in these operators, available in the form of random real functions or constants, has been shown to be a better way to explore the space, in terms of generalisation, when compared to modifications of these operators where the randomness is replaced by decisions based on the training error [1,6,9]. Definition 3.…”

Section: Definitionmentioning

confidence: 99%

“…This fact justifies the use of other operators (crossover and mutation) in order to further explore the search space. Although the location of the optimal solution is known, making decisions based only on the distance to the solution (equivalent to the training error) may lead to overfitting, as presented in other works [1,6,9].…”

Section: The Geometric Dispersion Operatormentioning

confidence: 99%

A Dispersion Operator for Geometric Semantic Genetic Programming

Oliveira

Otero

Pappa

2016

Proceedings of the Genetic and Evolutionary Computation Conference 2016

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Recent advances in geometric semantic genetic programming (GSGP) have shown that the results obtained by these methods can outperform those obtained by classical genetic programming algorithms, in particular in the context of symbolic regression. However, there are still many open issues on how to improve their search mechanism. One of these issues is how to get around the fact that the GSGP crossover operator cannot generate solutions that are placed outside the convex hull formed by the individuals of the current population. Although the mutation operator alleviates this problem, we cannot guarantee it will find promising regions of the search space within feasible computational time. In this direction, this paper proposes a new geometric dispersion operator that uses multiplicative factors to move individuals to less dense areas of the search space around the target solution before applying semantic genetic operators. Experiments in sixteen datasets show that the results obtained by the proposed operator are statistically significantly better than those produced by GSGP and that the operator does indeed spread the solutions around the target solution.

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The Effect of Distinct Geometric Semantic Crossover Operators in Regression Problems

Cited by 17 publications

References 12 publications

Revisiting the Sequential Symbolic Regression Genetic Programming

Revisiting the Sequential Symbolic Regression Genetic Programming

On explaining machine learning models by evolving crucial and compact features

A Dispersion Operator for Geometric Semantic Genetic Programming

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