Evolving Complexity is Hard

• URL: http://arxiv.org/abs/2209.13013v1
• Date: Fri, 16 Sep 2022 19:13:02 GMT
• Title: Evolving Complexity is Hard
• Authors: Alden H. Wright and Cheyenne L. Laue
• Abstract summary: Genotype-phenotype maps are fundamental to evolution and enable evolution by following phenotype-preserving walks in genotype space. Here we use a digital logic gate circuit G-P map where genotypes are represented by circuits and phenotypes by the functions that the circuits compute. We show that the logic gate circuit shares many universal properties of biologically derived G-P maps, with the exception of the relationship between one method of computing phenotypic evolvability, robustness, and complexity.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Understanding the evolution of complexity is an important topic in a wide variety of academic fields. Implications of better understanding complexity include increased knowledge of major evolutionary transitions and the properties of living and technological systems. Genotype-phenotype (G-P) maps are fundamental to evolution, and biologically-oriented G-P maps have been shown to have interesting and often-universal properties that enable evolution by following phenotype-preserving walks in genotype space. Here we use a digital logic gate circuit G-P map where genotypes are represented by circuits and phenotypes by the functions that the circuits compute. We compare two mathematical definitions of circuit and phenotype complexity and show how these definitions relate to other well-known properties of evolution such as redundancy, robustness, and evolvability. Using both Cartesian and Linear genetic programming implementations, we demonstrate that the logic gate circuit shares many universal properties of biologically derived G-P maps, with the exception of the relationship between one method of computing phenotypic evolvability, robustness, and complexity. Due to the inherent structure of the G-P map, including the predominance of rare phenotypes, large interconnected neutral networks, and the high mutational load of low robustness, complex phenotypes are difficult to discover using evolution. We suggest, based on this evidence, that evolving complexity is hard and we discuss computational strategies for genetic-programming-based evolution to successfully find genotypes that map to complex phenotypes in the search space.

Related papers

• Predicting Genetic Mutation from Whole Slide Images via Biomedical-Linguistic Knowledge Enhanced Multi-label Classification [119.13058298388101]
  We develop a Biological-knowledge enhanced PathGenomic multi-label Transformer to improve genetic mutation prediction performances. BPGT first establishes a novel gene encoder that constructs gene priors by two carefully designed modules. BPGT then designs a label decoder that finally performs genetic mutation prediction by two tailored modules.
  arXiv  Detail & Related papers  (2024-06-05T06:42:27Z)
• PhyloGFN: Phylogenetic inference with generative flow networks [57.104166650526416]
  We introduce the framework of generative flow networks (GFlowNets) to tackle two core problems in phylogenetics: parsimony-based and phylogenetic inference. Because GFlowNets are well-suited for sampling complex structures, they are a natural choice for exploring and sampling from the multimodal posterior distribution over tree topologies. We demonstrate that our amortized posterior sampler, PhyloGFN, produces diverse and high-quality evolutionary hypotheses on real benchmark datasets.
  arXiv  Detail & Related papers  (2023-10-12T23:46:08Z)
• Unsupervised ensemble-based phenotyping helps enhance the discoverability of genes related to heart morphology [57.25098075813054]
  We propose a new framework for gene discovery entitled Un Phenotype Ensembles. It builds a redundant yet highly expressive representation by pooling a set of phenotypes learned in an unsupervised manner. These phenotypes are then analyzed via (GWAS), retaining only highly confident and stable associations.
  arXiv  Detail & Related papers  (2023-01-07T18:36:44Z)
• Probabilistic Genotype-Phenotype Maps Reveal Mutational Robustness of RNA Folding, Spin Glasses, and Quantum Circuits [0.0]
  We introduce probabilistic genotype-phenotype maps, where each genotype maps to a vector of phenotype probabilities. We study three model systems to show that PrGP maps offer a generalized framework which can handle uncertainty emerging from various physical sources. We derive an analytical theory for the behavior of PrGP robustness, and we demonstrate that the theory is highly predictive of empirical robustness.
  arXiv  Detail & Related papers  (2023-01-04T23:09:38Z)
• The Effect of Epigenetic Blocking on Dynamic Multi-Objective Optimisation Problems [1.4502611532302039]
  Epigenetic mechanisms allow quick non- or partially-genetic adaptations to environmental changes. This paper asks if the advantages that epigenetic inheritance provide in the natural world are replicated in dynamic multi-objective problems.
  arXiv  Detail & Related papers  (2022-11-25T16:33:05Z)
• On the Complexity of Bayesian Generalization [141.21610899086392]
  We consider concept generalization at a large scale in the diverse and natural visual spectrum. We study two modes when the problem space scales up, and the $complexity$ of concepts becomes diverse.
  arXiv  Detail & Related papers  (2022-11-20T17:21:37Z)
• Phenotype Search Trajectory Networks for Linear Genetic Programming [8.079719491562305]
  Neutrality is the observation that some mutations do not lead to phenotypic changes. We study the search trajectories of a genetic programming system as graph-based models. We measure the characteristics of phenotypes including their genotypic abundance and Kolmogorov complexity.
  arXiv  Detail & Related papers  (2022-11-15T21:20:50Z)
• Heritability in Morphological Robot Evolution [2.7402733069181]
  We introduce the biological notion of heritability, which captures the amount of phenotypic variation caused by genotypic variation. In our analysis we measure the heritability on the first generation of robots evolved from two different encodings. We show how heritability can be a useful tool to better understand the relationship between genotypes and phenotypes.
  arXiv  Detail & Related papers  (2021-10-21T14:58:17Z)
• 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)
• Epigenetic evolution of deep convolutional models [81.21462458089142]
  We build upon a previously proposed neuroevolution framework to evolve deep convolutional models. We propose a convolutional layer layout which allows kernels of different shapes and sizes to coexist within the same layer. The proposed layout enables the size and shape of individual kernels within a convolutional layer to be evolved with a corresponding new mutation operator.
  arXiv  Detail & Related papers  (2021-04-12T12:45:16Z)
• Complexity-based speciation and genotype representation for neuroevolution [81.21462458089142]
  This paper introduces a speciation principle for neuroevolution where evolving networks are grouped into species based on the number of hidden neurons. The proposed speciation principle is employed in several techniques designed to promote and preserve diversity within species and in the ecosystem as a whole.
  arXiv  Detail & Related papers  (2020-10-11T06:26:56Z)

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.