Evolutionary Automata and Deep Evolutionary Computation

• URL: http://arxiv.org/abs/2411.15008v1
• Date: Fri, 22 Nov 2024 15:31:50 GMT
• Title: Evolutionary Automata and Deep Evolutionary Computation
• Authors: Eugene Eberbach,
• Abstract summary: An evolutionary automaton is an automaton that evolves performing evolutionary computation perhaps using an infinite number of generations. This also gives the hint to the power of natural evolution that is self-evolving by interactive feedback with the environment.
• Score: 0.38073142980732994
• License:
• Abstract: Evolution by natural selection, which is one of the most compelling themes of modern science, brought forth evolutionary algorithms and evolutionary computation, applying mechanisms of evolution in nature to various problems solved by computers. In this paper we concentrate on evolutionary automata that constitute an analogous model of evolutionary computation compared to well-known evolutionary algorithms. Evolutionary automata provide a more complete dual model of evolutionary computation, similar like abstract automata (e.g., Turing machines) form a more formal and precise model compared to recursive algorithms and their subset - evolutionary algorithms. An evolutionary automaton is an automaton that evolves performing evolutionary computation perhaps using an infinite number of generations. This model allows for a direct modeling evolution of evolution, and leads to tremendous expressiveness of evolutionary automata and evolutionary computation. This also gives the hint to the power of natural evolution that is self-evolving by interactive feedback with the environment.

Related papers

• When large language models meet evolutionary algorithms [48.213640761641926]
  Pre-trained large language models (LLMs) have powerful capabilities for generating creative natural text. Evolutionary algorithms (EAs) can discover diverse solutions to complex real-world problems. Motivated by the common collective and directionality of text generation and evolution, this paper illustrates the parallels between LLMs and EAs.
  arXiv  Detail & Related papers  (2024-01-19T05:58:30Z)
• 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)
• Complex Evolutional Pattern Learning for Temporal Knowledge Graph Reasoning [60.94357727688448]
  TKG reasoning aims to predict potential facts in the future given the historical KG sequences. The evolutional patterns are complex in two aspects, length-diversity and time-variability. We propose a new model, called Complex Evolutional Network (CEN), which uses a length-aware Convolutional Neural Network (CNN) to handle evolutional patterns of different lengths.
  arXiv  Detail & Related papers  (2022-03-15T11:02:55Z)
• Evolving Evolutionary Algorithms with Patterns [0.0]
  The model is based on the Multi Expression Programming (MEP) technique. Several evolutionary algorithms for function optimization are evolved by using the considered model.
  arXiv  Detail & Related papers  (2021-10-10T16:26:20Z)
• 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)
• 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)
• Evolving the Behavior of Machines: From Micro to Macroevolution [4.061135251278186]
  Evolution has inspired computer scientists since the advent of computing. This has led to tools that can evolve complex neural networks for machines. Modern view of artificial evolution is moving the field away from microevolution to macroevolution.
  arXiv  Detail & Related papers  (2020-12-21T21:35:15Z)
• Mimicking Evolution with Reinforcement Learning [10.35437633064506]
  We argue that the path to developing artificial human-like-intelligence will pass through mimicking the evolutionary process in a nature-like simulation. This work proposes Evolution via Evolutionary Reward (EvER) that allows learning to single-handedly drive the search for policies with increasingly evolutionary fitness.
  arXiv  Detail & Related papers  (2020-03-31T18:16:53Z)

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.