Genetic Algorithms for Inductive Program Synthesis

Genetic Algorithms for Inductive Program Synthesis

Tromp, Marije (TU Delft Electrical Engineering, Mathematics and Computer Science)

Dumančić, S. (mentor)
Molenaar, M.L. (graduation committee)

Delft University of Technology

Computer Science and Engineering

CSE3000 Research Project

2022-06-24

VanillaGP is an Inductive Program Synthesis algorithm that takes a Genetic Algorithm (GA) approach by using its 3 components: selection, mutation, and crossover. Many different alternatives exist for these components and although this is not the only application of a GAs on the Program Synthesis domain, it has not been extensively evaluated what the effects of using these different alternatives or combinations of them are. We explored this by evaluating the performance of multiple alternatives per component and comparing the results of combinations of these alternatives to VanillaGP. These evaluations were done on 3 IPS domains: robot, ASCII art, and strings. From these evaluations we conclude that Stochastic Universal Sampling combined with Queen Bee Crossover and an altered version of One Mutation Per Solution performs best on the string domain, and Down-Sampled Lexicase Selection combined with Three Parent Crossover and the same altered version of OMPS performs best on the other domains.

Genetic Algorithm
Inductive Program Synthesis
Program Synthesis

http://resolver.tudelft.nl/uuid:d5d6194c-27d8-4e25-a422-51e7a6bdec05

https://github.com/MarijeTromp/BEP_How_To_Train_Your_Dragon GitHub repository containing the received codebase and all implemented alternatives.

Student theses

bachelor thesis

© 2022 Marije Tromp