# A Fresh Look at Lamarckian Evolution and the Baldwin Effect

arXiv: https://arxiv.org/abs/2605.28703 · HTML: https://mlsanity.com/paper/2605.28703/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 33 references that are in the map.
- First posted on arXiv 27 May 2026.

## Builds on (most cited first, 13 of 13)

- [Probabilistic Tools for the Analysis of Randomized Optimization Heuristics](https://mlsanity.com/paper/1801.06733/) (2018)
- [The Runtime of the Compact Genetic Algorithm on Jump Functions](https://mlsanity.com/paper/1908.06527/) (2019)
- [Exactly Solving the Maximum Weight Independent Set Problem on Large Real-World Graphs](https://mlsanity.com/paper/1810.10834/) (2018)
- [On the Limitations of the Univariate Marginal Distribution Algorithm to Deception and Where Bivariate EDAs might help](https://mlsanity.com/paper/1907.12438/) (2019)
- [GraphBench: Next-generation graph learning benchmarking](https://mlsanity.com/paper/2512.04475/) (2025)
- [Sharp Bounds for Genetic Drift in Estimation of Distribution Algorithms](https://mlsanity.com/paper/1910.14389/) (2019)
- [The Univariate Marginal Distribution Algorithm Copes Well With Deception and Epistasis](https://mlsanity.com/paper/2007.08277/) (2020)
- [Significance-based Estimation-of-Distribution Algorithms](https://mlsanity.com/paper/1807.03495/) (2018)
- [Lower Bounds from Fitness Levels Made Easy](https://mlsanity.com/paper/2104.03372/) (2021)
- [Choosing the Right Algorithm With Hints From Complexity Theory](https://mlsanity.com/paper/2109.06584/) (2021)
- [A Tight Runtime Analysis for the cGA on Jump Functions---EDAs Can Cross Fitness Valleys at No Extra Cost](https://mlsanity.com/paper/1903.10983/) (2019)
- [Lamarckian Inheritance Improves Robot Evolution in Dynamic Environments](https://mlsanity.com/paper/2403.19545/) (2024)
- [The Runtime of Random Local Search on the Generalized Needle Problem](https://mlsanity.com/paper/2403.08153/) (2024)

## Abstract (arXiv)

Baldwinian and Lamarckian evolution have existed for a long time in evolutionary algorithms (EAs) without ever dominating the academic literature or practical applications. In this work, we use modern empirical and theoretical methods to revisit Lamarckian and Baldwinian evolution and rigorously compare them with the generic Darwinian evolution. On the empirical side, we run a comprehensive suite of experiments on graphs from six different datasets from the recent GraphBench benchmark on Maximum Independent Set and Maximum Cut problems. Our results show that Baldwinian and Lamarckian evolution consistently outperform Darwinian evolution, confirming the great potential of local search augmented evolutionary algorithms. Notably, in the great majority of cases, all EAs outperform recent deep learning baselines and approach the performance of highly specialised heuristic and exact solvers. We furthermore report a high-performing set of generalist parameters for all studied evolution types that we hope will be of use to practitioners in future. On the theoretical side, we extend the existing DeceptiveLeadingBlocks benchmark to arbitrary block length k. For all constant k, we then prove asymptotically tight runtime bounds for the (1+1) EA in the three evolution types on this benchmark. For Baldwinian evolution, these are independent of k, whereas for the other two evolution types, the runtimes steeply increase with growing value of k.