On the Baldwin Effect under Coevolution
- URL: http://arxiv.org/abs/2004.14827v2
- Date: Tue, 26 May 2020 07:14:01 GMT
- Title: On the Baldwin Effect under Coevolution
- Authors: Larry Bull
- Abstract summary: This paper considers the interaction between learning and evolution in a coevolutionary scenario.
Using the NKCS model, it is shown how the amount of learning and the relative rate of evolution can alter behaviour.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The potentially beneficial interaction between learning and evolution, the
Baldwin effect, has long been established. This paper considers their
interaction within a coevolutionary scenario, ie, where the adaptations of one
species typically affects the fitness of others. Using the NKCS model, which
allows the systematic exploration of the effects of fitness landscape size,
ruggedness, and degree of coupling, it is shown how the amount of learning and
the relative rate of evolution can alter behaviour.
Related papers
- Cognitive Evolutionary Learning to Select Feature Interactions for Recommender Systems [59.117526206317116]
We show that CELL can adaptively evolve into different models for different tasks and data.
Experiments on four real-world datasets demonstrate that CELL significantly outperforms state-of-the-art baselines.
arXiv Detail & Related papers (2024-05-29T02:35:23Z) - 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) - 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) - Learning and evolution: factors influencing an effective combination [0.0]
The mutual relationship between evolution and learning is a controversial argument among the artificial intelligence and neuro-evolution communities.
The author investigates whether combining learning and evolution permits to find better solutions than those discovered by evolution alone.
arXiv Detail & Related papers (2023-06-20T09:03:52Z) - CausalDialogue: Modeling Utterance-level Causality in Conversations [83.03604651485327]
We have compiled and expanded upon a new dataset called CausalDialogue through crowd-sourcing.
This dataset includes multiple cause-effect pairs within a directed acyclic graph (DAG) structure.
We propose a causality-enhanced method called Exponential Average Treatment Effect (ExMATE) to enhance the impact of causality at the utterance level in training neural conversation models.
arXiv Detail & Related papers (2022-12-20T18:31:50Z) - 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) - Emergent Hand Morphology and Control from Optimizing Robust Grasps of
Diverse Objects [63.89096733478149]
We introduce a data-driven approach where effective hand designs naturally emerge for the purpose of grasping diverse objects.
We develop a novel Bayesian Optimization algorithm that efficiently co-designs the morphology and grasping skills jointly.
We demonstrate the effectiveness of our approach in discovering robust and cost-efficient hand morphologies for grasping novel objects.
arXiv Detail & Related papers (2020-12-22T17:52:29Z) - Are Artificial Dendrites useful in NeuroEvolution? [0.0]
This letter explores the effects of including a simple dendrite-inspired mechanism into neuroevolution.
The phenomenon of separate dendrite activation thresholds on connections is allowed to emerge under an evolutionary process.
arXiv Detail & Related papers (2020-10-02T10:53:46Z)
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.