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arxiv: 2604.12884 · v1 · submitted 2026-04-14 · 💻 cs.NE · q-bio.PE

An abstract model of nonrandom, non-Lamarckian mutation in evolution using a multivariate estimation-of-distribution algorithm

Liudmyla Vasylenko , Adi Livnat This is my paper

Pith reviewed 2026-05-10 13:45 UTC · model grok-4.3

classification 💻 cs.NE q-bio.PE
keywords nonrandom mutationinteraction-based evolutionestimation of distribution algorithmnon-Lamarckian mutationevolutionary simulationgenomic informationparsimony and fit
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The pith

Nonrandom non-Lamarckian mutations arise as genomes accumulate internal information over generations

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents a simulation model using multivariate estimation-of-distribution algorithms to show that mutations can be nonrandom and non-Lamarckian. In this model, mutations are shaped by information that has accumulated in the genome across generations rather than being purely random or directed by the environment in a Lamarckian way. Selection, recombination, and mutation interact complementarily, with evolution driven by the interplay between parsimony and environmental fit. Random elements enable generalization through their integration with the rest of the process instead of directly encoding improvements. This approach connects to observations about increased variation under changed conditions and analogies with computational learning theory.

Core claim

Using a multivariate estimation-of-distribution algorithm as an abstract model, the paper demonstrates concrete nonrandom, non-Lamarckian mutation where selection, recombination, and mutation interact in a complementary fashion; evolution is driven by the interaction of parsimony and fit; and random bits enable generalization by connecting with the rest of the evolutionary process, indirectly capturing aspects of interaction-based evolution.

What carries the argument

The multivariate estimation-of-distribution algorithm, which serves as an abstract model for generating mutations based on probability distributions updated from selected high-fitness individuals.

If this is right

  • Selection, recombination, and nonrandom non-Lamarckian mutation interact complementarily to drive evolution.
  • Evolution is propelled by the interaction between parsimony and fit to the environment.
  • Random bits do not directly encode improvement but enable generalization through their connections in the process.
  • Changed conditions increase the rate of production of heritable variation.
  • Analogies to computational learning theory place the learned hypothesis at the population level.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • This framework suggests that evolutionary models should incorporate mechanisms for internal integration of information through heritable changes.
  • Similar information-accumulating processes might be testable in real biological systems by examining mutation patterns correlated with population fitness history.
  • Applications in evolutionary computation could improve by adopting mutation operators that build on accumulated distributional information.

Load-bearing premise

The multivariate estimation-of-distribution algorithm can faithfully model biological nonrandom non-Lamarckian mutation without simulation artifacts that fail to correspond to actual genomic processes.

What would settle it

An experiment comparing mutation spectra in evolving populations under this model versus purely random mutation to check whether the former produces patterns of variation matching observed biological responses to selection and environmental change.

Figures

Figures reproduced from arXiv: 2604.12884 by Adi Livnat, Liudmyla Vasylenko.

Figure 1
Figure 1. Figure 1: The origination of heritable change is influenced by fan-in of heritable information, [PITH_FULL_IMAGE:figures/full_fig_p008_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The fan-in of information in the generation of heritable change enables a network of [PITH_FULL_IMAGE:figures/full_fig_p009_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: A comparison between basic evolutionary algorithms (EAs) and the RBM-based [PITH_FULL_IMAGE:figures/full_fig_p015_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: a) w¯ in the RBM (blue), RM (red) and BRG (grey) models for three MAX-3-SAT instances of 150, 600 and 2400 variables (rows) and three maximum population sizes Nmax (columns). Generations are on the x-axis. The difference between the RBM and RM mean fitnesses increases with the population size as well as with the problem size. The BRG curve, which provides a baseline for comparison, starts higher than the o… view at source ↗
Figure 5
Figure 5. Figure 5: w¯ over the generations in the genome completion test for even-parity Boolean func￾tions with 2, 3, 4 and 5 variables, averaged over 100 independent runs. The known gene values are sampled from the population and the remaining gene value is completed by the RBM. The rows show results for functions of different numbers of variables, from n = 2 to n = 5. The columns show results for different population size… view at source ↗
Figure 6
Figure 6. Figure 6: w¯ over the generations for two MAX-3-SAT instances (75 and 1200 variables) av￾eraged over 100 independent runs for three different models: the full model (solid blue line), the weights-only model (dashed orange line) and the biases-only model (dashed green line). Results are shown for three different population sizes: N = 100 (left), N = 1000 (middle) and N = 10, 000 (right). The other RBM parameters are … view at source ↗
Figure 7
Figure 7. Figure 7: w¯ over time (left, blue) and the average expected heterozygosity, H¯ e over time (right, red) in the RBM model, averaged over 100 independent runs, for MAX-3-SAT instances with n = 20 variables. Let He = 1 − p 2 − q 2 , where p and q are frequencies of the two alleles, 0 and 1, in a given locus, with p + q = 1, and let H¯ e be the mean He across loci, serving here as a measure of genetic variation. The to… view at source ↗
Figure 8
Figure 8. Figure 8: Distributions of the fitness values in the population at different generations in the [PITH_FULL_IMAGE:figures/full_fig_p033_8.png] view at source ↗
read the original abstract

At the fundamental conceptual level, two alternatives have traditionally been considered for how mutations arise and how evolution happens: 1) random mutation and natural selection, and 2) Lamarckism. Recently, the theory of Interaction-based Evolution (IBE) has been proposed, according to which mutations are neither random nor Lamarckian, but are influenced by information accumulating internally in the genome over generations. Based on the estimation-of-distribution algorithms framework, we present a simulation model that demonstrates nonrandom, non-Lamarckian mutation concretely while capturing indirectly several aspects of IBE: selection, recombination, and nonrandom, non-Lamarckian mutation interact in a complementary fashion; evolution is driven by the interaction of parsimony and fit; and random bits do not directly encode improvement but enable generalization by the manner in which they connect with the rest of the evolutionary process. Connections are drawn to Darwin's observations that changed conditions increase the rate of production of heritable variation; to the causes of bell-shaped distributions of traits and how these distributions respond to selection; and to computational learning theory, where analogizing evolution to learning in accord with IBE casts individuals as examples and places the learned hypothesis at the population level. The model highlights the importance of incorporating internal integration of information through heritable change in both evolutionary theory and evolutionary computation.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper presents an abstract simulation model based on multivariate estimation-of-distribution algorithms (EDAs) to demonstrate nonrandom, non-Lamarckian mutation consistent with Interaction-based Evolution (IBE). It claims that selection, recombination, and distribution-based sampling interact complementarily to drive evolution via parsimony and fit, with random bits enabling generalization through population-level connections rather than direct encoding of improvements. The model is linked to Darwin's observations on heritable variation under changed conditions, the dynamics of bell-shaped trait distributions under selection, and an analogy to computational learning theory in which individuals act as examples and the population encodes the learned hypothesis.

Significance. If the simulation is implemented with sufficient detail to allow reproduction and validation, the work could provide a useful computational framework for exploring nonrandom mutation mechanisms in evolutionary computation and theoretical biology. It offers an illustrative bridge between EDA techniques and IBE concepts, highlighting internal information integration without requiring Lamarckian inheritance, and may suggest new directions for algorithms that learn mutation distributions from populations.

major comments (2)
  1. [Model description] Model description section: the manuscript states that the multivariate EDA framework is used to demonstrate nonrandom, non-Lamarckian mutation but supplies no equations for the joint distribution, no description of the parameter estimation procedure from the population, and no pseudocode or implementation details for the mutation sampling step. This is load-bearing for the central claim, as the nonrandom character cannot be assessed without knowing how the distribution is constructed and sampled.
  2. [Results and validation] Results and validation: the abstract asserts that the model 'demonstrates' the claimed properties (complementary interaction of selection/recombination/mutation, parsimony-fit drive, and generalization via random bits), yet no simulation outputs, figures, statistical summaries, or control experiments are provided to show these behaviors emerge. Without such evidence, it is impossible to determine whether the demonstration is substantive or circular by construction of the rules.
minor comments (2)
  1. [Abstract] The abstract is overly dense; separating the core model claims from the broader IBE and learning-theory connections would improve readability.
  2. [Discussion] The mapping from EDA components to IBE aspects (e.g., how 'internal genomic information accumulation' is represented) is stated at a high level but could be made more explicit even in an abstract model.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their careful reading and constructive comments, which highlight important areas for clarification and strengthening. We address each major comment below and will revise the manuscript to incorporate the requested details and evidence.

read point-by-point responses

  1. Referee: [Model description] Model description section: the manuscript states that the multivariate EDA framework is used to demonstrate nonrandom, non-Lamarckian mutation but supplies no equations for the joint distribution, no description of the parameter estimation procedure from the population, and no pseudocode or implementation details for the mutation sampling step. This is load-bearing for the central claim, as the nonrandom character cannot be assessed without knowing how the distribution is constructed and sampled.

    Authors: We agree that the current description is insufficient for independent assessment of the nonrandom mutation mechanism. The revised manuscript will add the explicit equations for the multivariate joint distribution (including how it factors over selected individuals), the maximum-likelihood parameter estimation procedure applied to the post-selection population, and pseudocode for the sampling step that generates offspring. These additions will make transparent how population-level statistics induce nonrandom, non-Lamarckian variation without direct encoding of improvements in individual genomes. revision: yes

  2. Referee: [Results and validation] Results and validation: the abstract asserts that the model 'demonstrates' the claimed properties (complementary interaction of selection/recombination/mutation, parsimony-fit drive, and generalization via random bits), yet no simulation outputs, figures, statistical summaries, or control experiments are provided to show these behaviors emerge. Without such evidence, it is impossible to determine whether the demonstration is substantive or circular by construction of the rules.

    Authors: The manuscript is framed as an abstract model whose primary contribution is conceptual illustration rather than exhaustive empirical benchmarking. Nevertheless, the referee is correct that the abstract's use of 'demonstrates' requires supporting output. In revision we will include representative simulation trajectories, figures of evolving trait distributions under the three operators, quantitative summaries of parsimony-fit dynamics, and control runs that replace the learned distribution with uniform random mutation. These will show that the reported interactions and generalization effects arise from the interplay rather than from rule construction alone. revision: yes

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper presents a simulation model based on the established multivariate estimation-of-distribution algorithm framework to illustrate nonrandom non-Lamarckian mutation consistent with Interaction-based Evolution. No equations, derivations, or load-bearing steps are provided that reduce by construction to fitted inputs, self-definitions, or self-citations. The model is offered as an abstract demonstration capturing interactions indirectly, with connections drawn to external observations and learning theory rather than internal logical closure. This is a standard illustrative framework without the specific patterns of circularity enumerated.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the untested premise that an EDA-based simulation can abstractly represent biological mutation processes; no free parameters, additional axioms, or invented entities are explicitly listed in the abstract.

axioms (1)
  • domain assumption Multivariate estimation-of-distribution algorithms can model nonrandom, non-Lamarckian mutation in a manner faithful to Interaction-based Evolution
    Invoked to justify using the EDA framework as a concrete demonstration of the theoretical claim.

pith-pipeline@v0.9.0 · 5549 in / 1395 out tokens · 54447 ms · 2026-05-10T13:45:21.672142+00:00 · methodology

discussion (0)

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