ArticleGenetics2019
Computational Complexity as an Ultimate Constraint on Evolution.
Article in Genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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23 citing papers in PubMed.
- Evolution-guided engineering of an ancient nitrogenase interface enhances enzyme activity and stability.bioRxiv : the preprint server for biology · 2026Article
- Stochastic Evolutionary Control in Heterogeneous Populations.bioRxiv : the preprint server for biology · 2026Article
- When prediction fails: a computational complexity view of stress.Frontiers in veterinary science · 2026Article
- Semirational Protein Engineering Yields Archaerhodopsin-3-Based Fluorescent Genetically Encoded Voltage Indicators with Enhanced Brightness and Red Shifted Absorption Bands.Chem & bio engineering · 2025Article
- What Makes a Good Theory? Interdisciplinary Perspectives.Computational brain & behavior · 2024Article
- From Empirical Problem-Solving to Theoretical Problem-Finding Perspectives on the Cognitive Sciences.Computational brain & behavior · 2024Article
- Open-endedness in synthetic biology: A route to continual innovation for biological design.Science advances · 2024Review
- Evolution of biological cooperation: an algorithmic approach.Scientific reports · 2024Article
- Epistasis and pleiotropy shape biophysical protein subspaces associated with drug resistance.Physical review. E · 2023Article
- Epistasis meets pleiotropy in shaping biophysical protein subspaces associated with antimicrobial resistance.bioRxiv : the preprint server for biology · 2023Article
- Novelty Search Promotes Antigenic Diversity in Microbial Pathogens.Pathogens (Basel, Switzerland) · 2023Article
- Getting higher on rugged landscapes: Inversion mutations open access to fitter adaptive peaks in NK fitness landscapes.PLoS computational biology · 2022Article
- Discovery of positive and purifying selection in metagenomic time series of hypermutator microbial populations.PLoS genetics · 2022Article
- The Contribution of Evolutionary Game Theory to Understanding and Treating Cancer.Dynamic games and applications · 2022Review
- Theory Before the Test: How to Build High-Verisimilitude Explanatory Theories in Psychological Science.Perspectives on psychological science : a journal of the Association for Psychological Science · 2021Article
- Novelty and imitation within the brain: a Darwinian neurodynamic approach to combinatorial problems.Scientific reports · 2021Article
- How Intractability Spans the Cognitive and Evolutionary Levels of Explanation.Topics in cognitive science · 2020Article
- Article
- Evolution Rapidly Optimizes Stability and Aggregation in Lattice Proteins Despite Pervasive Landscape Valleys and Mazes.Genetics · 2020Article
- Continual evolution through coupled fast and slow feedbacks.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
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1 author.
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Abstract
Experiments show that evolutionary fitness landscapes can have a rich combinatorial structure due to epistasis. For some landscapes, this structure can produce a computational constraint that prevents evolution from finding local fitness optima-thus overturning the traditional assumption that local fitness peaks can always be reached quickly if no other evolutionary forces challenge natural selection. Here, I introduce a distinction between easy landscapes of traditional theory where local fitness peaks can be found in a moderate number of steps, and hard landscapes where finding local optima requires an infeasible amount of time. Hard examples exist even among landscapes with no reciprocal sign epistasis; on these semismooth fitness landscapes, strong selection weak mutation dynamics cannot find the unique peak in polynomial time. More generally, on hard rugged fitness landscapes that include reciprocal sign epistasis, no evolutionary dynamics-even ones that do not follow adaptive paths-can find a local fitness optimum quickly. Moreover, on hard landscapes, the fitness advantage of nearby mutants cannot drop off exponentially fast but has to follow a power-law that long-term evolution experiments have associated with unbounded growth in fitness. Thus, the constraint of computational complexity enables open-ended evolution on finite landscapes. Knowing this constraint allows us to use the tools of theoretical computer science and combinatorial optimization to characterize the fitness landscapes that we expect to see in nature. I present candidates for hard landscapes at scales from single genes, to microbes, to complex organisms with costly learning (Baldwin effect) or maintained cooperation (Hankshaw effect). Just how ubiquitous hard landscapes (and the corresponding ultimate constraint on evolution) are in nature becomes an open empirical question.
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