Genetic Programming with Local Scoring
Neural and Evolutionary Computing
2023-01-26 v4
Abstract
We present new techniques for synthesizing programs through sequences of mutations. Among these are (1) a method of local scoring assigning a score to each expression in a program, allowing us to more precisely identify buggy code, (2) suppose-expressions which act as an intermediate step to evolving if-conditionals, and (3) cyclic evolution in which we evolve programs through phases of expansion and reduction. To demonstrate their merits, we provide a basic proof-of-concept implementation which we show evolves correct code for several functions manipulating integers and lists, including some that are intractable by means of existing Genetic Programming techniques.
Cite
@article{arxiv.2211.17234,
title = {Genetic Programming with Local Scoring},
author = {Max Vistrup},
journal= {arXiv preprint arXiv:2211.17234},
year = {2023}
}
Comments
Minor improvements