Genetic Algorithms for Inductive Program Synthesis

Bachelor thesis (2022)

Authors

M.R. Tromp Electrical Engineering, Mathematics and Computer Science

Contributors

S. Dumančić Algorithmics - EEMCS (mentor)
M.L. Molenaar Computer Graphics and Visualisation - EEMCS (graduation committee member)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
Copyright: © 2022 Marije Tromp
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Published Date

24-06-2022

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

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.