A Genetic Source-Code Program-Synthesizer for Real-Time Co-Evolution with Humans

Gene-Level Geometric-Push Program-Synthesis

Speakman, John ORCID: https://orcid.org/0000-0002-1318-9880 (2024) A Genetic Source-Code Program-Synthesizer for Real-Time Co-Evolution with Humans. Doctoral thesis, Falmouth University / University of the Arts London.

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Abstract / Summary

Can genetic operators be used to produce human-centric source-code documents for human-interactive and collaborative programming practice?
Addressing this question, a novel automated source-code generation system is created
which synthesizes, using genetic operators, object-oriented compilable files, which better
conforms to human coding conventions than current benchmark genetic algorithms. This
project:
• Provides a new form of AI for automatic source-code generation.
• Implements novel use cases for automatic source-code generation in practical
collaborative applications.
• Benchmarks the new algorithm against a standard program synthesis benchmark
suite.
• Assesses a genetic realignment algorithm in the context of crossover for elitest
fitness.
• Explores novel approaches to interaction methods with a genetic auto-coder for
multiple simultaneous users.
As of the beginning of this project, there were no existing implementations of source-code generators designed for human legibility while synthesizing for arbitrary language
environments: automated program synthesis algorithms derived from genetic operators
traditionally operate on specialist languages, with bytecode or machine code solutions or
suffered significantly with comprehension against human written code.
The novel algorithm introduced in this thesis interjects into current discourse with a
heuristic approach which can operate with relative language agnosticism, while retaining
common coding conventions, indentation, arbitrary code length, looping operators,
multiple function definitions and function calls.
Using this new algorithm, this thesis explores three diverse experimental phases to
analyse potential use-cases, from a qualitative perspective:
An automatic programmer for coding problems: as a tool to support human software
developers, for simple programming tasks.
A co-creative environment with Artificial Life: demonstrating the automatic
programmer’s ability to create evolvable behaviour controllers which adapt to survive
under various environmental pressures and to co-evolve with human interactors.
A public facing music generator: as a collaborative medium for live performance
environments, providing human-guided fitness training for live evolution of audio.
The thesis concludes that the algorithm is successful in automatic coding for implicit
fitness or regression environments, with several limitations relating to the fitness function
used and the size of the search space. These limitations are established and persist across
genetic methods for automatic coding. Regardless of the limitations, this approach demonstrates valuable use cases in artistic mediums.

Item Type:	Thesis (Doctoral)
Subjects:	Computing & Data Science Computing & Data Science > Game Design Research
Department:	Games Academy
Depositing User:	Nicola Bond
Date Deposited:	29 Oct 2025 12:55
Last Modified:	29 Oct 2025 12:55
URI:	https://repository.falmouth.ac.uk/id/eprint/6222

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