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Found 13 papers, 6 papers with code
The Inefficiency of Genetic Programming for Symbolic Regression -- Extended Version
This enables us to quantify the success probability of finding the best possible expressions, and to compare the search efficiency of genetic programming to random search in the space of semantically unique expressions.
Interpretability in Symbolic Regression: a benchmark of Explanatory Methods using the Feynman data set
This paper proposes a benchmark scheme to evaluate explanatory methods to explain regression models, mainly symbolic regression models.
Inexact Simplification of Symbolic Regression Expressions with Locality-sensitive Hashing
Symbolic regression (SR) searches for parametric models that accurately fit a dataset, prioritizing simplicity and interpretability.
Minimum variance threshold for epsilon-lexicase selection
Parent selection plays an important role in evolutionary algorithms, and many strategies exist to select the parent pool before breeding the next generation.
Origami: (un)folding the abstraction of recursion schemes for program synthesis
Program synthesis with Genetic Programming searches for a correct program that satisfies the input specification, which is usually provided as input-output examples.
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.
Transformation-Interaction-Rational Representation for Symbolic Regression
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.
EvoMan: Game-playing Competition
For this particular competition, the main goal is to beat all of the eight bosses using a generalist strategy.
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.
A Greedy Search Tree Heuristic for Symbolic Regression
This paper introduces a new data structure, called Interaction-Transformation (IT), that constrains the search space in order to exclude a region of larger and more complicated expressions.
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.
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.
Iterative Universal Hash Function Generator for Minhashing
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.
