A Fresh Look at Lamarckian Evolution and the Baldwin Effect
- URL: http://arxiv.org/abs/2605.28703v1
- Date: Wed, 27 May 2026 16:30:39 GMT
- Title: A Fresh Look at Lamarckian Evolution and the Baldwin Effect
- Abstract summary: Baldwinian and Lamarckian evolution have existed for a long time in evolutionary algorithms (EAs) without ever dominating the academic literature or practical applications.<n>We use modern empirical and theoretical methods to revisit Lamarckian and Baldwinian evolution and rigorously compare them with the generic Darwinian evolution.<n>Our results show that Baldwinian and Lamarckian evolution consistently outperform Darwinian evolution.
- Score: 22.895536023786974
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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 Deceptive Leading Block benchmark to arbitrary block length and use tools from modern theoretical runtime analysis to prove upper and lower bounds on the expected runtime. For block lengths greater than two, Baldwinian evolution is asymptotically faster than Lamarckian which is asymptotically faster than Darwinian evolution. When accounting for the cost of the local search procedure in fitness evaluations, the ordering depends on the implementation with Baldwinian evolution staying fastest from small block lengths onwards, explaining its strong empirical performance.
Related papers
- Teaching LLMs to Self-Evolve: Cultivating Core Meta-Skills with Reinforcement Learning [51.17262419591891]
Test-time scaling through iterative self-evolution with environment feedback, as demonstrated by AlphaEvolve, shows remarkable performance gains.<n>We present MetaEvolve, a framework designed to develop these meta-skills via a data synthesis pipeline, evolution-aware reinforcement learning, and inference-time evolutionary search.
arXiv Detail & Related papers (2026-07-24T04:35:29Z) - Direct From Darwin: Deriving Advanced Optimizers From Evolutionary First Principles [0.0]
This paper derives a suite of advanced gradient-based optimization algorithms directly from evolutionary first principles.<n>We prove that, in an asexual context, Fisher's and Wright's historically opposed views of evolution are actually formally equivalent.<n>Even the state-of-the-art Adam can be brought into evolutionary compliance through a minor surgery.
arXiv Detail & Related papers (2026-05-06T17:33:58Z) - CausalEvolve: Towards Open-Ended Discovery with Causal Scratchpad [72.197582948888]
We develop CausalEvolve, equipped with a causal scratchpad that leverages Large Language Models (LLMs) to identify and reason about guiding factors for evolution.<n>CaulEvolve first identifies outcome-level factors that offer complementary inspirations in improving the target objective.<n>During the evolution, CausalEvolve also inspects surprise patterns during the evolution and abductive reasoning to hypothesize new factors, which in turn offer novel directions.
arXiv Detail & Related papers (2026-03-15T19:53:31Z) - Towards Better Evolution Modeling for Temporal Knowledge Graphs [0.0]
Temporal knowledge graphs (TKGs) preserve evolving human knowledge.<n>Recent research has focused on designing models to learn the evolutionary nature of TKGs to predict future facts.<n>We find that existing benchmarks inadvertently introduce a shortcut.
arXiv Detail & Related papers (2026-02-09T07:37:40Z) - Geometric Aspects of Entanglement Generating Hamiltonian Evolutions [0.02455468619225742]
We study stationary Hamiltonian evolutions transitioning from separable to maximally entangled two-qubit quantum states.<n>From a geometric perspective, each evolution is characterized by means of geodesic efficiency, speed efficiency, and curvature coefficient.<n>Our investigation suggests that this phenomenon arises because suboptimal trajectories between states are characterized by longer path lengths.
arXiv Detail & Related papers (2026-01-15T18:31:02Z) - CoT-Evo: Evolutionary Distillation of Chain-of-Thought for Scientific Reasoning [63.44477226386808]
Chain-of-thought (CoT) distillation from advanced large language models (LLMs) has proven effective in general reasoning tasks.<n>But it struggles in scientific domains where even advanced models often produce incorrect or superficial reasoning.<n>We propose CoT-Evo, an evolutionary CoT distillation framework to overcome this problem.
arXiv Detail & Related papers (2025-10-15T05:29:56Z) - Simulating Evolvability as a Learning Algorithm: Empirical Investigations on Distribution Sensitivity, Robustness, and Constraint Tradeoffs [0.0]
Theory of evolvability formalizes evolution as a constrained learning algorithm operating without labeled examples or structural knowledge.<n>We implement a genetic algorithm that faithfully simulates Valiant's model and conduct experiments across six Boolean function classes.<n>Our findings reveal sharp performance drops at intermediate dimensions and expose the essential role of neutral mutations in escaping fitness plateaus.
arXiv Detail & Related papers (2025-07-24T04:32:31Z) - DarwinLM: Evolutionary Structured Pruning of Large Language Models [49.55509443720372]
Large Language Models (LLMs) have achieved significant success across various NLP tasks.<n>Structured pruning offers an effective solution by compressing models and directly providing end-to-end speed improvements.<n>We propose DarwinLM, a method for training-aware structured pruning.
arXiv Detail & Related papers (2025-02-11T18:59:35Z) - DARLEI: Deep Accelerated Reinforcement Learning with Evolutionary
Intelligence [77.78795329701367]
We present DARLEI, a framework that combines evolutionary algorithms with parallelized reinforcement learning.
We characterize DARLEI's performance under various conditions, revealing factors impacting diversity of evolved morphologies.
We hope to extend DARLEI in future work to include interactions between diverse morphologies in richer environments.
arXiv Detail & Related papers (2023-12-08T16:51:10Z) - Phylogeny-informed fitness estimation [58.720142291102135]
We propose phylogeny-informed fitness estimation, which exploits a population's phylogeny to estimate fitness evaluations.
Our results indicate that phylogeny-informed fitness estimation can mitigate the drawbacks of down-sampled lexicase.
This work serves as an initial step toward improving evolutionary algorithms by exploiting runtime phylogenetic analysis.
arXiv Detail & Related papers (2023-06-06T19:05:01Z) - Epigenetic opportunities for Evolutionary Computation [0.0]
Evolutionary Computation is a group of biologically inspired algorithms used to solve complex optimisation problems.
It can be split into Evolutionary Algorithms, which take inspiration from genetic inheritance, and Swarm Intelligence algorithms, that take inspiration from cultural inheritance.
This paper breaks down successful bio-inspired algorithms under a contemporary biological framework based on the Extended Evolutionary Synthesis.
arXiv Detail & Related papers (2021-08-10T09:44:53Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.