Search Results for author: Wolfgang Banzhaf

Found 23 papers, 7 papers with code

On The Nature Of The Phenotype In Tree Genetic Programming

no code implementations12 Feb 2024 Wolfgang Banzhaf, Illya Bakurov

In this contribution, we discuss the basic concepts of genotypes and phenotypes in tree-based GP (TGP), and then analyze their behavior using five benchmark datasets.

Active Learning in Genetic Programming: Guiding Efficient Data Collection for Symbolic Regression

1 code implementation31 Jul 2023 Nathan Haut, Wolfgang Banzhaf, Bill Punch

This paper examines various methods of computing uncertainty and diversity for active learning in genetic programming.

Active Learning regression +1

Discovering Adaptable Symbolic Algorithms from Scratch

no code implementations31 Jul 2023 Stephen Kelly, Daniel S. Park, Xingyou Song, Mitchell McIntire, Pranav Nashikkar, Ritam Guha, Wolfgang Banzhaf, Kalyanmoy Deb, Vishnu Naresh Boddeti, Jie Tan, Esteban Real

We evolve modular policies that tune their model parameters and alter their inference algorithm on-the-fly to adapt to sudden environmental changes.

AutoML

Phenotype Search Trajectory Networks for Linear Genetic Programming

no code implementations15 Nov 2022 Ting Hu, Gabriela Ochoa, Wolfgang Banzhaf

Genotype-to-phenotype mappings translate genotypic variations such as mutations into phenotypic changes.

An Artificial Chemistry Implementation of a Gene Regulatory Network

no code implementations9 Sep 2022 Iliya Miralavy, Wolfgang Banzhaf

Gene Regulatory Networks are networks of interactions in biological organisms responsible for determining the production levels of proteins and peptides.

Correlation versus RMSE Loss Functions in Symbolic Regression Tasks

no code implementations31 May 2022 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.

regression Symbolic Regression

Genetic Improvement in the Shackleton Framework for Optimizing LLVM Pass Sequences

no code implementations28 Apr 2022 Shuyue Stella Li, Hannah Peeler, Andrew N. Sloss, Kenneth N. Reid, Wolfgang Banzhaf

In this paper, we present the novel use of genetic improvement to find problem-specific optimized LLVM pass sequences.

Code Generation

Iterative Genetic Improvement: Scaling Stochastic Program Synthesis

no code implementations26 Feb 2022 Yuan Yuan, Wolfgang Banzhaf

In cases where large programs are required for a solution, it is generally believed that {\it stochastic} search has advantages over other classes of search techniques.

Program Synthesis

Using Genetic Programming to Predict and Optimize Protein Function

no code implementations8 Feb 2022 Iliya Miralavy, Alexander Bricco, Assaf Gilad, Wolfgang Banzhaf

In this paper, we propose POET, a computational Genetic Programming tool based on evolutionary computation methods to enhance screening and mutagenesis in Directed Evolution and help protein engineers to find proteins that have better functionality.

Optimizing LLVM Pass Sequences with Shackleton: A Linear Genetic Programming Framework

1 code implementation31 Jan 2022 Hannah Peeler, Shuyue Stella Li, Andrew N. Sloss, Kenneth N. Reid, Yuan Yuan, Wolfgang Banzhaf

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 -- to a variety of use cases.

Evolutionary Algorithms

Evolving Hierarchical Memory-Prediction Machines in Multi-Task Reinforcement Learning

no code implementations23 Jun 2021 Stephen Kelly, Tatiana Voegerl, Wolfgang Banzhaf, Cedric Gondro

We use genetic programming to evolve highly-generalized agents capable of operating in six unique environments from the control literature, including OpenAI's entire Classic Control suite.

reinforcement-learning Reinforcement Learning (RL)

The Factory Must Grow: Automation in Factorio

1 code implementation9 Feb 2021 Kenneth N. Reid, Iliya Miralavy, Stephen Kelly, Wolfgang Banzhaf, Cedric Gondro

Efficient optimization of resources is paramount to success in many problems faced today.

Scheduling

The Effects of Taxes on Wealth Inequality in Artificial Chemistry Models of Economic Activity

no code implementations3 Jul 2020 Wolfgang Banzhaf

We consider a number of Artificial Chemistry models for economic activity and what consequences they have for the formation of economic inequality.

Evolution of Cooperative Hunting in Artificial Multi-layered Societies

no code implementations23 May 2020 Honglin Bao, Wolfgang Banzhaf

In this model, the standard hunting game of stag is modified into a new situation with social hierarchy and penalty.

It is Time for New Perspectives on How to Fight Bloat in GP

no code implementations1 May 2020 Francisco Fernández de Vega, Gustavo Olague, Francisco Chávez, Daniel Lanza, Wolfgang Banzhaf, Erik Goodman

This new perspective allows us to understand that new methods for bloat control can be derived, and the first of such a method is described and tested.

Distributed Computing Evolutionary Algorithms

Multi-Objective Evolutionary Design of Deep Convolutional Neural Networks for Image Classification

1 code implementation3 Dec 2019 Zhichao Lu, Ian Whalen, Yashesh Dhebar, Kalyanmoy Deb, Erik Goodman, Wolfgang Banzhaf, Vishnu Naresh Boddeti

While existing approaches have achieved competitive performance in image classification, they are not well suited to problems where the computational budget is limited for two reasons: (1) the obtained architectures are either solely optimized for classification performance, or only for one deployment scenario; (2) the search process requires vast computational resources in most approaches.

Classification Computational Efficiency +4

Batch Tournament Selection for Genetic Programming

no code implementations18 Apr 2019 Vinicius V. Melo, Danilo Vasconcellos Vargas, Wolfgang Banzhaf

Lexicase selection achieves very good solution quality by introducing ordered test cases.

Drone Squadron Optimization: a Self-adaptive Algorithm for Global Numerical Optimization

no code implementations14 Mar 2017 Vinícius Veloso de Melo, Wolfgang Banzhaf

This procedure is evolved by the Command Center during the global optimization process in order to adapt DSO to the search landscape.

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