Within the large field of Search-Based Software Engineering (SBSE), Genetic Improvement (GI) aims to improve the properties of software, often at the level of source-code but increasingly at other levels. This means that it operates directly on Java or C source code, and it typically starts with real-world software. This makes GI attractive for industrial applications, e.g. in contrast to Program Synthesis that aims to computationally create applications from scratch. We will demonstrate how GI methodologies can be applied to improve functional properties, such as fixing bugs and increasing accuracy, and also to optimise the physical properties of code, such as power consumption, size of code, bandwidth, and execution time. We will explore where generative AI fits in and how GI and LLMs complement each other. We will, furthermore, show and discuss the importance of involving the end users in the research and development of GI tools. This applies especially to industry software developers and how the tools deliver or suggest their improvements.

The aim of the tutorial is to:
• examine the motives for evolving source code directly, rather than a language built from a function set and terminal set which has to be interpreted after a program has been evolved
• understand different approaches to implementing GI including operating directly on text files, and operating on abstract syntax trees
• appreciate the new research questions that can be addressed while operating on actual source code
• understand some of the issues regarding measuring non-functional properties such as execution time and power consumption
• examine some early examples of GI as well as current state-of-the-art examples, industrial examples, and a few HUMIES winners.
• summarise recent synergies between LLMs and GI.
• understanding links between GI and other techniques such as hyper-heuristics, automatic parameter tuning, and deep parameter tuning
• highlight some of the multi-objective research where programs have been evolved that lie on the Pareto front with axes representing different non-functional properties
• discuss the issue of search space sparseness and flatness in GI, and more effective targeting of mutations to improve efficiency
• discuss the human-computer interaction between software developers and tools that provide automatic-programming-type services.
• give an introduction to GI in No Time - an open source simple micro-framework for GI (https://github.com/gintool/gin) including a live demonstration.