  Data generation and analysis is a fundamental aspect of many industries and
disciplines, from strategic decision making in business to research in the
physical and social sciences. However, data generated using software and
algorithms can be subject to biases and errors. These can be due to problems
with the original software, default settings that do not align with the
specific needs of the situation, or even deeper problems with the underlying
theories and models. This paper proposes an advanced search and optimization
framework aimed at generating and choosing optimal source code capable of
correcting errors and biases from previous versions to address typical problems
in software systems specializing in data analysis and generation, especially
those in the corporate and data science world. Applying this framework multiple
times on the same software system would incrementally improve the quality of
the output results. It uses Solomonoff Induction as a sound theoretical basis,
extending it with Kolmogorov Conditional Complexity, a novel adaptation, to
evaluate a set of candidate programs. We propose the use of generative models
for the creation of this set of programs, with special emphasis on the
capabilities of Large Language Models (LLMs) to generate high quality code.
