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
Nelishia Pillay
University of Pretoria, South Africa | 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.Gabriel Kronberger
University of Applied Sciences Upper Austria | webpage
Gabriel Kronberger is a Professor at the University of Applied Sciences Upper Austria and has been working on tree-based genetic programming methods especially for symbolic regression since 20 years. His research interests are symbolic regression for physics-based machine learning and applications in science and engineering.