71 citations · 187 across the 20 of their papers we have counts for
10 papers · 1 filter
Active Learning in Genetic Programming: Guiding Efficient Data Collection for Symbolic Regression
Nathan Haut, Wolfgang Banzhaf, Bill Punch
This paper examines various methods of computing uncertainty and diversity for active learning in genetic programming. We found that the model population in genetic programming can…
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…
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…
Evolving Hierarchical Memory-Prediction Machines in Multi-Task Reinforcement Learning
Stephen Kelly, Tatiana Voegerl, Wolfgang Banzhaf +1
A fundamental aspect of behaviour is the ability to encode salient features of experience in memory and use these memories, in combination with current sensory information, to pred…
Batch Tournament Selection for Genetic Programming
Vinicius V. Melo, Danilo Vasconcellos Vargas, Wolfgang Banzhaf
Lexicase selection achieves very good solution quality by introducing ordered test cases. However, the computational complexity of lexicase selection can prohibit its use in many a…
Faster Genetic Programming GPquick via multicore and Advanced Vector Extensions
W. B. Langdon, W. Banzhaf
We evolve floating point Sextic polynomial populations of genetic programming binary trees for up to a million generations. Programs with almost four hundred million instructions a…