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GP - Genetic Programming

Description

Genetic Programming is an evolutionary computation technique that automatically generates solutions/programs to solve a given problem. In GP, various representations have been used, such as tree structures, linear sequences of code, graphs and grammars. Provided that a suitable fitness function is devised, computer programs solving the given problem emerge, without the need for humans to explicitly program the computer. The GP track invites original contributions on all aspects of evolutionary generation of computer programs or other executable structures for specific tasks.

Scope

Advances in genetic programming include but are not limited to:

  • Analysis: Information Theory, Complexity, Run-time, Visualization, Fitness Landscape, Generalisation, Domain adaptation
  • Synthesis: Programs, Algorithms, Circuits, Systems
  • Applications: Classification, Clustering, Control, Data mining, Big-Data analytics, Regression, Semi-supervised Learning, Policy search, Prediction, Continuous and Combinatorial Optimisation, Streaming Data, Design, Inductive Programming, Computer Vision, Feature Engineering and Feature Selection, Natural Language Processing
  • Environments: Static, Dynamic, Interactive, Uncertain
  • Operators: Replacement, Selection, Crossover, Mutation, Variation
  • Performance: Surrogate functions, Multi-Objective, Coevolutionary, Human Competitive, Parameter Tuning
  • Populations: Demes, Diversity, Niches
  • Programs: Decomposition, Modularity, Semantics, Simplification, Software Improvement, Bug Repair, Software/Program Testing
  • Programming Languages: Imperative, Declarative, Object-oriented, Functional
  • Representations: Cartesian, Grammatical, Graphs, Linear, Rules, Trees, Geometric and Semantic
  • Systems: Autonomous, Complex, Developmental, Gene Regulation, Parallel, Self-Organizing, Software

Track Chairs

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Nelishia Pillay

University of Pretoria | webpage

Nelishia Pillay is a Professor at the University of Pretoria, South Africa. She holds the Multichoice Joint-Chair in Machine Learning and SARChI Chair in Artificial Intelligence for Sustainable Development. Her research areas include hyper-heuristics, automated design of machine learning and search techniques, evolutionary transfer learning, combinatorial optimization, genetic programming, genetic algorithms and deep learning for and more generally machine learning and optimization for sustainable development. These are the focus areas of the NICOG (Nature-Inspired Computing Optimization) research group which she has established.

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Gabriel Kronberger

University of Applied Sciences Upper Austria | webpage

Gabriel Kronberger is professor at the University of Applied Sciences Upper Austria and has been working on algorithms for symbolic regression since more than 15 years. From 2018 until 2022 he led the Josef Ressel Center for Symbolic Regression. In 2024, he published a book on "Symbolic Regression" together with Burlacu, Kommenda, Winkler, and Affenzeller. His current research interests are symbolic regression for physics-based machine learning and applications in science and engineering. Gabriel has (co-)authored more than 100 publications (SCOPUS) and has been a member of the Program Committee for the GECCO Genetic Programming track since 2016. More information: (https://symreg.at)