Grant: Discovery Grants Program - Individual

Title Emergence and Open-ended Evolution in Genetic Programming*The role of emergence in science and technology is just beginning to become highly visible. Emergent phenomena have long been constrained to the discussion of specialists in particular fields but with the introduction of large-scale simulation approaches to science and engineering, this is about to change in in that it will loose its ""exotic character"" and become visible in what it is: A widespread phenomenon with explanatory power in a vast array of disciplines. Novelty and innovation, on the other hand, have long been recognized as drivers of progress in science and technology. However, their modelling and formalization has been difficult, due to the essential rule time plays in any notion of novelty and the technical difficulties of capturing that concept in formal systems. *We shall use one of the methods at the cutting edge of Bio-inspired Computing, Genetic Programming, to study the phenomena of novelty creation and emergence in computational systems. In particular, we are interested in how to achieve an ongoing process of creativity, a continued generation of novelty, in such systems. We further shall attempt to formalize the emergence of new entities in systems, using Genetic Programming as a laboratory in which to set appropriate conditions. *By taking inspiration from the Darwinian process of evolution, Genetic Programming allows not only to solve individual problem instances, like in combinatorial optimization, but to use the recipe of Nature which led to adaptive organisms and the intelligence of higher-level organisms to generate problem-solving entities. With the continued validity of Moore's law, the next decade will demonstrate an explosion of human-competitive results from bio-inspired algorithms, and in particular from those gleaned from evolution. *Genetic Programming is part of the upswing in Artificial Intelligence, which is driven by the same forces (1) availability of large amounts of data in all areas of science and technology; (2) continued growth in the availability of processing cycles for calculation of ever more complex algorithms; (3) new paradigms like deep learning for neural networks that have revolutionized entire application areas.*We shall take Genetic Programming as our laboratory to explore novelty, innovation, emergence and creativity in an algorithmic system with applications to multiple domains: machine learning and artificial intelligence, big data analysis, computational science and mathematics. Besides methodological improvements to genetic programming algorithms, we shall examine their behaviour in two important application areas, the structuring of deep learning neural networks and the evolution of code patches for repairing bugs in computer code.