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Fabricio Olivetti de Franca

Fabricio Olivetti de Franca appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

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Machine LearningNeural and Evolutionary Computingphysics.soc-phSocial and Information NetworksArtificial IntelligenceInformation Retrieval

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Published work

9 published item(s)

preprint2026arXiv

A Comparative Study of Model Selection Criteria for Symbolic Regression

Effective model selection is critical in symbolic regression (SR) to identify mathematical expressions that balance accuracy and complexity, and have low expected error on unseen data. Many modern implementations of genetic programming (GP) for SR generate a set of Pareto optimal candidate solutions, but reliable automatic selection of solutions that generalize well remains an open issue. Current literature offers various information-theoretic and Bayesian approaches, yet comprehensive comparisons of their performance across different data regimes are limited. This study presents a systematic empirical comparison of widely used selection criteria: the Akaike information criterion (AIC), the corrected AIC (AICc), the Bayesian information criterion (BIC), minimum description length (MDL), as well as Efron's bootstrap estimate for the in-sample prediction error on seven synthetic datasets with Gaussian noise. We rank candidate expressions generated by perturbing ground-truth functions to assess generalization error and selection probability of the ground-truth expression. Our findings reveal that MDL consistently identifies models with the lowest test error and the shortest length across most datasets. While no single criterion dominates all results, MDL and BIC produced the highest probability of selecting the ground-truth expressions.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Prediction Intervals and Confidence Regions for Symbolic Regression Models based on Likelihood Profiles

Symbolic regression is a nonlinear regression method which is commonly performed by an evolutionary computation method such as genetic programming. Quantification of uncertainty of regression models is important for the interpretation of models and for decision making. The linear approximation and so-called likelihood profiles are well-known possibilities for the calculation of confidence and prediction intervals for nonlinear regression models. These simple and effective techniques have been completely ignored so far in the genetic programming literature. In this work we describe the calculation of likelihood profiles in details and also provide some illustrative examples with models created with three different symbolic regression algorithms on two different datasets. The examples highlight the importance of the likelihood profiles to understand the limitations of symbolic regression models and to help the user taking an informed post-prediction decision.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Transformation-Interaction-Rational Representation for Symbolic Regression

Symbolic Regression searches for a function form that approximates a dataset often using Genetic Programming. Since there is usually no restriction to what form the function can have, Genetic Programming may return a hard to understand model due to non-linear function chaining or long expressions. A novel representation called Interaction-Transformation was recently proposed to alleviate this problem. In this representation, the function form is restricted to an affine combination of terms generated as the application of a single univariate function to the interaction of selected variables. This representation obtained competing solutions on standard benchmarks. Despite the initial success, a broader set of benchmarking functions revealed the limitations of the constrained representation. In this paper we propose an extension to this representation, called Transformation-Interaction-Rational representation that defines a new function form as the rational of two Interaction-Transformation functions. Additionally, the target variable can also be transformed with an univariate function. The main goal is to improve the approximation power while still constraining the overall complexity of the expression. We tested this representation with a standard Genetic Programming with crossover and mutation. The results show a great improvement when compared to its predecessor and a state-of-the-art performance for a large benchmark.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

EvoMan: Game-playing Competition

This paper describes a competition proposal for evolving Intelligent Agents for the game-playing framework called EvoMan. The framework is based on the boss fights of the game called Mega Man II developed by Capcom. For this particular competition, the main goal is to beat all of the eight bosses using a generalist strategy. In other words, the competitors should train the agent to beat a set of the bosses and then the agent will be evaluated by its performance against all eight bosses. At the end of this paper, the competitors are provided with baseline results so that they can have an intuition on how good their results are.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Interaction-Transformation Evolutionary Algorithm for Symbolic Regression

The Interaction-Transformation (IT) is a new representation for Symbolic Regression that restricts the search space into simpler, but expressive, function forms. This representation has the advantage of creating a smoother search space unlike the space generated by Expression Trees, the common representation used in Genetic Programming. This paper introduces an Evolutionary Algorithm capable of evolving a population of IT expressions supported only by the mutation operator. The results show that this representation is capable of finding better approximations to real-world data sets when compared to traditional approaches and a state-of-the-art Genetic Programming algorithm.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

The message does not matter: the influence of the network on information diffusion

How an information spreads throughout a social network is a valuable knowledge sought by many groups such as marketing enterprises and political parties. If they can somehow predict the impact of a given message or manipulate it in order to amplify how long it will spread, it would give them a huge advantage over their competitors. Intuitively, it is expected that two factors contribute to make an information becoming viral: how influential the person who spreads is inside its network and the content of the message. The former should have a more important role, since people will not just blindly share any content, or will they? In this work it is found that the degree of a node alone is capable of accurately predicting how many followers of the seed user will spread the information through a simple linear regression. The analysis was performed with five different messages from Twitter network that was shared with different degrees along the users. The results show evidences that no matter the content, the number of affected neighbors is predictable. The role of the content of the messages of a user is likely to influence the network formation and the path the message will follow through the network.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

A Flexible Fitness Function for Community Detection in Complex Networks

Most community detection algorithms from the literature work as optimization tools that minimize a given \textit{fitness function}, while assuming that each node belongs to a single community. Since there is no hard concept of what a community is, most proposed fitness functions focus on a particular definition. As such, these functions do not always lead to partitions that correspond to those observed in practice. This paper proposes a new flexible fitness function that allows the identification of communities with distinct characteristics. Such flexibility was evaluated through the adoption of an immune-inspired optimization algorithm, named cob-aiNet[C], to identify both disjoint and overlapping communities in a set of benchmark networks. The results have shown that the obtained partitions are much closer to the ground-truth than those obtained by the optimization of the modularity function.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

Iterative Universal Hash Function Generator for Minhashing

Minhashing is a technique used to estimate the Jaccard Index between two sets by exploiting the probability of collision in a random permutation. In order to speed up the computation, a random permutation can be approximated by using an universal hash function such as the $h_{a,b}$ function proposed by Carter and Wegman. A better estimate of the Jaccard Index can be achieved by using many of these hash functions, created at random. In this paper a new iterative procedure to generate a set of $h_{a,b}$ functions is devised that eliminates the need for a list of random values and avoid the multiplication operation during the calculation. The properties of the generated hash functions remains that of an universal hash function family. This is possible due to the random nature of features occurrence on sparse datasets. Results show that the uniformity of hashing the features is maintaned while obtaining a speed up of up to $1.38$ compared to the traditional approach.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

Maximizing Diversity for Multimodal Optimization

Most multimodal optimization algorithms use the so called \textit{niching methods}~\cite{mahfoud1995niching} in order to promote diversity during optimization, while others, like \textit{Artificial Immune Systems}~\cite{de2010conceptual} try to find multiple solutions as its main objective. One of such algorithms, called \textit{dopt-aiNet}~\cite{de2005artificial}, introduced the Line Distance that measures the distance between two solutions regarding their basis of attraction. In this short abstract I propose the use of the Line Distance measure as the main objective-function in order to locate multiple optima at once in a population.

0 citations0 reviewsNo code linkedOpen access