58 citations · 112 across the 17 of their papers we have counts for
7 papers · 1 filter
Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions
Gabriel Kronberger, Fabricio Olivetti de Franca, Deaglan J. Bartlett +2
Symbolic regression with genetic programming (GPSR) may suffer from overfitting and structural bloat, especially when noise is present. In this paper we evaluate description length…
The Inefficiency of Genetic Programming for Symbolic Regression
Gabriel Kronberger, Fabricio Olivetti de Franca, Harry Desmond +2
We analyse the search behaviour of genetic programming for symbolic regression in practically relevant but limited settings, allowing exhaustive enumeration of all solutions. This…
Origami: (un)folding the abstraction of recursion schemes for program synthesis
Matheus Campos Fernandes, Fabricio Olivetti de Franca, Emilio Francesquini
Program synthesis with Genetic Programming searches for a correct program that satisfies the input specification, which is usually provided as input-output examples. One particular…
HOTGP -- Higher-Order Typed Genetic Programming
Matheus Campos Fernandes, Fabrício Olivetti de França, Emilio Francesquini
Program synthesis is the process of generating a computer program following a set of specifications, which can be a high-level description of the problem and/or a set of input-outp…
An electronic-game framework for evaluating coevolutionary algorithms
Karine da Silva Miras de Araújo, Fabrício Olivetti de França
One of the common artificial intelligence applications in electronic games consists of making an artificial agent learn how to execute some determined task successfully in a game e…
A Flexible Fitness Function for Community Detection in Complex Networks
Fabricio Olivetti de Franca, Guilherme Palermo Coelho
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 singl…
Maximizing Diversity for Multimodal Optimization
Fabricio Olivetti de Franca
Most multimodal optimization algorithms use the so called \textit{niching methods}~\cite{mahfoud1995niching} in order to promote diversity during optimization, while others, like \…