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Organismal Agency and Rapid Adaptation: The Phenopoiesis Algorithm for Phenotype-First Evolution

Nam H. Le

TL;DR

The paper addresses the limits of gene-centric evolution by operationalizing organismal agency through the Phenopoiesis Algorithm, enabling organisms to inherit learned phenotypic patterns via an epigenome in addition to their genome. This dual inheritance supports compositional reuse of solutions, online exploration within lifetimes, and measurable agency through pattern reuse, yielding substantially faster adaptation (up to 3.4×) and robust multi-task performance. In grid-based experiments with rapidly changing targets, phenotype-first agents outperform gene-centric and Baldwin-criteria models, with faster recovery and reduced catastrophic forgetting. The work demonstrates that organismal agency is an actionable mechanism with tangible adaptive value and suggests a path toward more sample-efficient and modular evolutionary computation, as well as broader implications for biology and cognition.

Abstract

Evolutionary success depends on the capacity to adapt: organisms must respond to environmental challenges through both genetic innovation and lifetime learning. The gene-centric paradigm attributes evolutionary causality exclusively to genes, while Denis Noble's phenotype-first framework argues that organisms are active agents capable of interpreting genetic resources, learning from experience, and shaping their own development. However, this framework has remained philosophically intuitive but algorithmically opaque. We show for the first time that organismal agency can be implemented as a concrete computational process through heritable phenotypic patterns. We introduce the Phenopoiesis Algorithm, where organisms inherit not just genes but also successful phenotypic patterns discovered during lifetime learning. Through experiments in changing environments, these pattern-inheriting organisms achieve 3.4 times faster adaptation compared to gene-centric models. Critically, these gains require cross-generational inheritance of learned patterns rather than within-lifetime learning alone. We conclude that organismal agency is not a philosophical abstraction but an algorithmic mechanism with measurable adaptive value. The mechanism works through compositional reuse: organisms discover how to compose primitive elements into solutions, encode those compositional recipes, and transmit them to offspring. Evolution operates across multiple timescales -- fast, reversible phenotypic inheritance and slow, permanent genetic inheritance -- providing adaptive flexibility that single-channel mechanisms cannot achieve.

Organismal Agency and Rapid Adaptation: The Phenopoiesis Algorithm for Phenotype-First Evolution

TL;DR

The paper addresses the limits of gene-centric evolution by operationalizing organismal agency through the Phenopoiesis Algorithm, enabling organisms to inherit learned phenotypic patterns via an epigenome in addition to their genome. This dual inheritance supports compositional reuse of solutions, online exploration within lifetimes, and measurable agency through pattern reuse, yielding substantially faster adaptation (up to 3.4×) and robust multi-task performance. In grid-based experiments with rapidly changing targets, phenotype-first agents outperform gene-centric and Baldwin-criteria models, with faster recovery and reduced catastrophic forgetting. The work demonstrates that organismal agency is an actionable mechanism with tangible adaptive value and suggests a path toward more sample-efficient and modular evolutionary computation, as well as broader implications for biology and cognition.

Abstract

Evolutionary success depends on the capacity to adapt: organisms must respond to environmental challenges through both genetic innovation and lifetime learning. The gene-centric paradigm attributes evolutionary causality exclusively to genes, while Denis Noble's phenotype-first framework argues that organisms are active agents capable of interpreting genetic resources, learning from experience, and shaping their own development. However, this framework has remained philosophically intuitive but algorithmically opaque. We show for the first time that organismal agency can be implemented as a concrete computational process through heritable phenotypic patterns. We introduce the Phenopoiesis Algorithm, where organisms inherit not just genes but also successful phenotypic patterns discovered during lifetime learning. Through experiments in changing environments, these pattern-inheriting organisms achieve 3.4 times faster adaptation compared to gene-centric models. Critically, these gains require cross-generational inheritance of learned patterns rather than within-lifetime learning alone. We conclude that organismal agency is not a philosophical abstraction but an algorithmic mechanism with measurable adaptive value. The mechanism works through compositional reuse: organisms discover how to compose primitive elements into solutions, encode those compositional recipes, and transmit them to offspring. Evolution operates across multiple timescales -- fast, reversible phenotypic inheritance and slow, permanent genetic inheritance -- providing adaptive flexibility that single-channel mechanisms cannot achieve.
Paper Structure (32 sections, 1 equation, 2 figures, 6 tables, 3 algorithms)

This paper contains 32 sections, 1 equation, 2 figures, 6 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. Background and Related Work
  3. Gene-Centric Paradigm and the Modern Synthesis
  4. The Baldwin Effect: Learning and Inheritance
  5. Noble's Challenge: Why Phenotype-First Remained Unimplemented
  6. Operationalizing Phenotype-First Evolution
  7. Why This Engineering Approach: Bridging Philosophy and Computation
  8. Five Engineering Solutions to Five Philosophical Problems
  9. Solution 1: Epigenome as Data Structure for Learned Solutions
  10. Solution 2: Bidirectional Write-Back Mechanism
  11. Solution 3: Compositional Encoding and Primitives Library
  12. Solution 4: Online Explore-Exploit Decision Within Lifetime
  13. Solution 5: Observable Measurement of Agency Through Pattern Reuse
  14. The Phenopoiesis Algorithm: Implementation
  15. Computational Model: Problem Definition and Implementation
  16. ...and 17 more sections

Figures (2)

  • Figure 1: Phenopoiesis Algorithm Complete Flow: Part 1 shows individual ontogenetic development with structure, initialization of genome/epigenome, exploration-exploitation pattern discovery, and bidirectional write-back to databases. Part 2 shows inter-generational selection and dual inheritance of both genetic and epigenomic material.
  • Figure 2: Target Shapes. L, T, Plus, Cross, Square on $10 \times 10$ grids.