Generational Computation Reduction in Informal Counterexample-Driven Genetic Programming
This work addresses computational efficiency in software synthesis for researchers and practitioners, but it is incremental as it adapts existing CDGP ideas to a new context.
The paper tackled the problem of reducing computational cost in genetic programming by introducing 'informal CDGP', which applies counterexample-driven methods without formal specifications, using only user-provided training data. The results showed that this method finds solutions faster than standard GP, with one variant significantly increasing successful runs on about half of tested problems, and counterexample addition improving performance over static subsampling.
Counterexample-driven genetic programming (CDGP) uses specifications provided as formal constraints to generate the training cases used to evaluate evolving programs. It has also been extended to combine formal constraints and user-provided training data to solve symbolic regression problems. Here we show how the ideas underlying CDGP can also be applied using only user-provided training data, without formal specifications. We demonstrate the application of this method, called ``informal CDGP,'' to software synthesis problems. Our results show that informal CDGP finds solutions faster (i.e. with fewer program executions) than standard GP. Additionally, we propose two new variants to informal CDGP, and find that one produces significantly more successful runs on about half of the tested problems. Finally, we study whether the addition of counterexample training cases to the training set is useful by comparing informal CDGP to using a static subsample of the training set, and find that the addition of counterexamples significantly improves performance.