71 citations · 187 across the 20 of their papers we have counts for
20 papers
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…
Discovering Adaptable Symbolic Algorithms from Scratch
Stephen Kelly, Daniel S. Park, Xingyou Song +8
Autonomous robots deployed in the real world will need control policies that rapidly adapt to environmental changes. To this end, we propose AutoRobotics-Zero (ARZ), a method based…
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…
Using Evolutionary Algorithms to Design Antennas with Greater Sensitivity to Ultra High Energy Neutrinos
J. Rolla, A. Machtay, A. Patton +11
The Genetically Evolved NEutrino Telescopes for Improved Sensitivity, or GENETIS, project seeks to optimize detectors in physics for science outcomes in high dimensional parameter…
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…
Putting Natural Time into Science
Roger White, Wolfgang Banzhaf
This contribution argues that the notion of time used in the scientific modeling of reality deprives time of its real nature. Difficulties from logic paradoxes to mathematical inco…
NSGA-Net: Neural Architecture Search using Multi-Objective Genetic Algorithm
Zhichao Lu, Ian Whalen, Vishnu Boddeti +4
This paper introduces NSGA-Net -- an evolutionary approach for neural architecture search (NAS). NSGA-Net is designed with three goals in mind: (1) a procedure considering multiple…
ARJA: Automated Repair of Java Programs via Multi-Objective Genetic Programming
Yuan Yuan, Wolfgang Banzhaf
Recent empirical studies show that the performance of GenProg is not satisfactory, particularly for Java. In this paper, we propose ARJA, a new GP based repair approach for automat…
Drone Squadron Optimization: a Self-adaptive Algorithm for Global Numerical Optimization
Vinícius Veloso de Melo, Wolfgang Banzhaf
This paper proposes Drone Squadron Optimization, a new self-adaptive metaheuristic for global numerical optimization which is updated online by a hyper-heuristic. DSO is an artifac…
Evolving Genes to Balance a Pole
Miguel Nicolau, Marc Schoenauer, W. Banzhaf
We discuss how to use a Genetic Regulatory Network as an evolutionary representation to solve a typical GP reinforcement problem, the pole balancing. The network is a modified vers…
"Going back to our roots": second generation biocomputing
Jon Timmis, Martyn Amos, Wolfgang Banzhaf +1
Researchers in the field of biocomputing have, for many years, successfully "harvested and exploited" the natural world for inspiration in developing systems that are robust, adapt…