# Beyond Evolutionary Algorithms for Search-based Software Engineering

## TL;DR

This paper introduces a novel search method for search-based software engineering that uses a large initial population and recursive bi-clustering to achieve comparable results to evolutionary algorithms with significantly fewer evaluations.

## Contribution

The paper proposes a new approach that replaces traditional evolutionary algorithms with a large initial population and bi-clustering, reducing the number of evaluations needed.

## Key findings

- Achieves comparable results with fewer than 100 evaluations
- Outperforms standard evolutionary algorithms in efficiency
- Encourages exploring alternative methods before traditional EAs

## Abstract

Context: Evolutionary algorithms typically require a large number of evaluations (of solutions) to converge - which can be very slow and expensive to evaluate.Objective: To solve search-based software engineering (SE) problems, using fewer evaluations than evolutionary methods.Method: Instead of mutating a small population, we build a very large initial population which is then culled using a recursive bi-clustering chop approach. We evaluate this approach on multiple SE models, unconstrained as well as constrained, and compare its performance with standard evolutionary algorithms. Results: Using just a few evaluations (under 100), we can obtain comparable results to state-of-the-art evolutionary algorithms.Conclusion: Just because something works, and is widespread use, does not necessarily mean that there is no value in seeking methods to improve that method. Before undertaking search-based SE optimization tasks using traditional EAs, it is recommended to try other techniques, like those explored here, to obtain the same results with fewer evaluations.