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.
