71 citations · 187 across the 20 of their papers we have counts for
8 papers · 1 filter
Phenotype Search Trajectory Networks for Linear Genetic Programming
Ting Hu, Gabriela Ochoa, Wolfgang Banzhaf
Genotype-to-phenotype mappings translate genotypic variations such as mutations into phenotypic changes. Neutrality is the observation that some mutations do not lead to phenotypic…
An Artificial Chemistry Implementation of a Gene Regulatory Network
Iliya Miralavy, Wolfgang Banzhaf
Gene Regulatory Networks are networks of interactions in biological organisms responsible for determining the production levels of proteins and peptides. Proteins are workers of a…
Correlation versus RMSE Loss Functions in Symbolic Regression Tasks
Nathan Haut, Wolfgang Banzhaf, Bill Punch
The use of correlation as a fitness function is explored in symbolic regression tasks and the performance is compared against the typical RMSE fitness function. Using correlation w…
Genetic Improvement in the Shackleton Framework for Optimizing LLVM Pass Sequences
Shuyue Stella Li, Hannah Peeler, Andrew N. Sloss +2
Genetic improvement is a search technique that aims to improve a given acceptable solution to a problem. In this paper, we present the novel use of genetic improvement to find prob…
Iterative Genetic Improvement: Scaling Stochastic Program Synthesis
Yuan Yuan, Wolfgang Banzhaf
Program synthesis aims to {\it automatically} find programs from an underlying programming language that satisfy a given specification. While this has the potential to revolutioniz…
Using Genetic Programming to Predict and Optimize Protein Function
Iliya Miralavy, Alexander Bricco, Assaf Gilad +1
Protein engineers conventionally use tools such as Directed Evolution to find new proteins with better functionalities and traits. More recently, computational techniques and espec…
Active Learning Improves Performance on Symbolic RegressionTasks in StackGP
Nathan Haut, Wolfgang Banzhaf, Bill Punch
In this paper we introduce an active learning method for symbolic regression using StackGP. The approach begins with a small number of data points for StackGP to model. To improve…
Optimizing LLVM Pass Sequences with Shackleton: A Linear Genetic Programming Framework
Hannah Peeler, Shuyue Stella Li, Andrew N. Sloss +3
In this paper we introduce Shackleton as a generalized framework enabling the application of linear genetic programming -- a technique under the umbrella of evolutionary algorithms…