Epigenetic opportunities for Evolutionary Computation
- URL: http://arxiv.org/abs/2108.04546v1
- Date: Tue, 10 Aug 2021 09:44:53 GMT
- Title: Epigenetic opportunities for Evolutionary Computation
- Authors: Sizhe Yuen, Thomas H.G. Ezard, Adam J. Sobey
- Abstract summary: 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.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
However, recent developments have focused on computational or mathematical
adaptions, leaving their biological roots behind. This has left much of the
modern evolutionary literature relatively unexplored.
To understand which evolutionary mechanisms have been considered, and which
have been overlooked, this paper breaks down successful bio-inspired algorithms
under a contemporary biological framework based on the Extended Evolutionary
Synthesis, an extension of the classical, genetics focussed, Modern Synthesis.
The analysis shows that Darwinism and the Modern Synthesis have been
incorporated into Evolutionary Computation but that the Extended Evolutionary
Synthesis has been broadly ignored beyond:cultural inheritance, incorporated in
the sub-set of Swarm Intelligence algorithms, evolvability, through CMA-ES, and
multilevel selection, through Multi-Level Selection Genetic Algorithm.
The framework shows a missing gap in epigenetic inheritance for Evolutionary
Computation, despite being a key building block in modern interpretations of
how evolution occurs. Epigenetic inheritance can explain fast adaptation,
without changes in an individual's genotype, by allowing biological organisms
to self-adapt quickly to environmental cues, which, increases the speed of
convergence while maintaining stability in changing environments. This leaves a
diverse range of biologically inspired mechanisms as low hanging fruit that
should be explored further within Evolutionary Computation.
Related papers
- Meta-Learning an Evolvable Developmental Encoding [7.479827648985631]
Generative models have shown promise in being learnable representations for black-box optimisation.
Here we present a system that can meta-learn such representation by optimising for a representation's ability to generate quality-diversity.
In more detail, we show our meta-learning approach can find one Neural Cellular Automata, in which cells can attend to different parts of a "DNA" string genome during development.
arXiv Detail & Related papers (2024-06-13T11:52:06Z) - 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) - A Neuro-mimetic Realization of the Common Model of Cognition via Hebbian
Learning and Free Energy Minimization [55.11642177631929]
Large neural generative models are capable of synthesizing semantically rich passages of text or producing complex images.
We discuss the COGnitive Neural GENerative system, such an architecture that casts the Common Model of Cognition.
arXiv Detail & Related papers (2023-10-14T23:28:48Z) - Role of Morphogenetic Competency on Evolution [0.0]
In Evolutionary Computation, the inverse relationship (impact of intelligence on evolution) is approached from the perspective of organism level behaviour.
We focus on the intelligence of a minimal model of a system navigating anatomical morphospace.
We evolve populations of artificial embryos using a standard genetic algorithm in silico.
arXiv Detail & Related papers (2023-10-13T11:58:18Z) - 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) - Towards Large-Scale Simulations of Open-Ended Evolution in Continuous
Cellular Automata [0.0]
We build large-scale evolutionary simulations using parallel computing framework JAX.
We report a number of system design choices, including implicit implementation of genetic operators.
We propose several factors that may further facilitate open-ended evolution.
arXiv Detail & Related papers (2023-04-12T06:40:11Z) - Epigenetics Algorithms: Self-Reinforcement-Attention mechanism to
regulate chromosomes expression [0.0]
This paper proposes a new epigenetics algorithm that mimics the epigenetics phenomenon known as methylation.
The novelty of our epigenetics algorithms lies primarily in taking advantage of attention mechanisms and deep learning, which fits well with the genes/silencing concept.
arXiv Detail & Related papers (2023-03-15T21:33:21Z) - 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) - The Introspective Agent: Interdependence of Strategy, Physiology, and
Sensing for Embodied Agents [51.94554095091305]
We argue for an introspective agent, which considers its own abilities in the context of its environment.
Just as in nature, we hope to reframe strategy as one tool, among many, to succeed in an environment.
arXiv Detail & Related papers (2022-01-02T20:14:01Z) - 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) - Embodied Intelligence via Learning and Evolution [92.26791530545479]
We show that environmental complexity fosters the evolution of morphological intelligence.
We also show that evolution rapidly selects morphologies that learn faster.
Our experiments suggest a mechanistic basis for both the Baldwin effect and the emergence of morphological intelligence.
arXiv Detail & Related papers (2021-02-03T18:58:31Z)
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.