Evolutionary algorithms typically follow very rigid generational cycles. These cycles are simple to implement, analyze, and use, but they pose severe constraints on design space. Consequently, typical implementations can be differentiated only across a limited range of parameters and paradigms. While parameter tuning and human expertise can help determine which algorithmic configuration may be best on a given problem class, it is likely that there exist even better optimizers outside of this traditional design space. Previous work has shown that directed graphs can be used to automatically design evolutionary cycles which outperform traditional configurations on specific benchmarks. In this work, we present a novel representation and associated variation operator for evolving such graphs inspired by techniques used for neuro-evolution, demonstrate that this can significantly improve performance and result in more complex structures, and show evidence suggesting that, for some problems, traditional cycles are not even locally optimal. We further validate the effectiveness of our algorithm against benchmarks from the IOHprofiler suite, achieving competitive results.
Braden N. Tisdale, D. Tauritz, A. Pope· Annual Conference on Genetic...· 0 citations
The vital functions of life are driven by proteins — long folded strings of amino acids. Millions of proteins exist in nature, all optimized for a certain set of operating conditions. One particularly important trait of a protein is thermostability, or the tendency to maintain form and function at high temperatures. Protein thermostability is important for both biological organisms and industrial enzymatic processes that utilize them. This work proposes a novel hybrid EC approach, employing the EAs Simulating Molecular Evolution (EASME) software, for evolving thermostable variants of proteins with minimal computational overhead and customized problem-specific fitness functions. A variation of NSGA-II with a novel recombination/mutation interplay was designed to tackle the specific challenges of working with proteins. The results of this work, which computationally improved the thermostability of a protein more effectively than a state of the art machine learning model from 2025, demonstrate that EC may have been historically undervalued in the realm of protein design. The pipeline designed for this work could be applied to numerous other proteins, easily expanded to optimize other traits of those proteins, or employed on another problem entirely where mutations have a notable fitness cost.
J. S. Browning, D. Tauritz, John Beckmann· Annual Conference on Genetic...· 0 citations