- author = "Alcides Fonseca and Paulo Santos and Sara Silva",
- title = "The Usability Argument for Refinement Typed Genetic Programming",
- booktitle = "16th International Conference on Parallel Problem Solving from Nature, Part II",
- year = "2020",
- editor = "Thomas Baeck and Mike Preuss and Andre Deutz and Hao Wang2 and Carola Doerr and Michael Emmerich and Heike Trautmann",
- volume = "12270",
- series = "LNCS",
- pages = "18--32",
- address = "Leiden, Holland",
- month = "7-9 " # sep,
- publisher = "Springer",
- keywords = "genetic algorithms, genetic programming, SBSE, Refined types, Search-based software engineering",
- isbn13 = "978-3-030-58114-5",
-
DOI = "
doi:10.1007/978-3-030-58115-2_2",
-
abstract = "The performance of Evolutionary Algorithms is
frequently hindered by arbitrarily large search spaces.
In order to overcome this challenge, domain-specific
knowledge is often used to restrict the representation
or evaluation of candidate solutions to the problem at
hand. Due to the diversity of problems and the
unpredictable performance impact, the encoding of
domain-specific knowledge is a frequent problem in the
implementation of evolutionary algorithms.
We propose the use of Refinement Typed Genetic Programming, an enhanced hybrid of Strongly Typed Genetic Programming (STGP) and Grammar-Guided Genetic Programming (GGGP) that features an advanced type system with polymorphism and dependent and refined types.
We argue that this approach is more usable for describing common problems in machine learning, optimisation and program synthesis, due to the familiarity of the language (when compared to GGGP) and the use of a unifying language to express the representation, the phenotype translation, the evaluation function and the context in which programs are executed.",
- notes = "LASIGE Computer Science and Engineering Research Centre PPSN2020",
