- author = "Patricia Ryser-Welch",
- title = "Evolving comprehensible and scalable solvers using {CGP} for solving some real-world inspired problems",
- school = "Electronic Engineering, University of York",
- year = "2017",
- address = "UK",
- month = jul,
- keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming",
-
URL = "
http://etheses.whiterose.ac.uk/id/eprint/19011",
-
URL = "
http://etheses.whiterose.ac.uk/19011/1/finalThesisv3.pdf",
-
URL = "https://www.researchgate.net/publication/321867825_Evolving_comprehensible_and_scalable_solvers_using_CGP_for_solving_some_real-world_inspired_problems",
-
URL = "https://www.researchgate.net/profile/Patricia_Ryser-Welch/publication/321867825_Evolving_comprehensible_and_scalable_solvers_using_CGP_for_solving_some_real-world_inspired_problems/links/5a366b1945851532e83025db/Evolving-comprehensible-and-scalable-solvers-using-CGP-for-solving-some-real-world-inspired-problems.pdf",
- size = "372 pages",
-
abstract = "My original contribution to knowledge is the
application of Cartesian Genetic Programming to design
some scalable and human-understandable metaheuristics
automatically; those find some suitable solutions for
real-world NP-hard and discrete problems. This
technique is thought to possess the ability to raise
the generality of a problem-solving process, allowing
some supervised machine learning tasks and being able
to evolve non-deterministic algorithms.
Two extensions of Cartesian Genetic Programming are presented. Iterative Cartesian Genetic Programming can encode loops and nested loop with their termination criteria, making susceptible to evolutionary modification the whole programming construct. This newly developed extension and its application to metaheuristics are demonstrated to discover effective solvers for NP-hard and discrete problems. This thesis also extends Cartesian Genetic Programming and Iterative Cartesian Genetic Programming to adapt a hyper-heuristic reproductive operator at the same time of exploring the automatic design space. It is demonstrated the exploration of an automated design space can be improved when specific types of active and non-active genes are mutated.
A series of rigorous empirical investigations demonstrate that lowering the comprehension barrier of automatically designed algorithms can help communicating and identifying an effective and ineffective pattern of primitives. The complete evolution of loops and nested loops without imposing a hard limit on the number of recursive calls is shown to broaden the automatic design space. Finally, it is argued the capability of a learning objective function to assess the scalable potential of a generated algorithm can be beneficial to a generative hyper-heuristic.",
- notes = "Supervisor Julian Miller",
