AIMC Topic: Biological Evolution

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Direct Fit to Nature: An Evolutionary Perspective on Biological and Artificial Neural Networks.

Neuron
Evolution is a blind fitting process by which organisms become adapted to their environment. Does the brain use similar brute-force fitting processes to learn how to perceive and act upon the world? Recent advances in artificial neural networks have ...

Integration of Anatomy Ontologies and Evo-Devo Using Structured Markov Models Suggests a New Framework for Modeling Discrete Phenotypic Traits.

Systematic biology
Modeling discrete phenotypic traits for either ancestral character state reconstruction or morphology-based phylogenetic inference suffers from ambiguities of character coding, homology assessment, dependencies, and selection of adequate models. Thes...

Evolutionary aspects of reservoir computing.

Philosophical transactions of the Royal Society of London. Series B, Biological sciences
Reservoir computing (RC) is a powerful computational paradigm that allows high versatility with cheap learning. While other artificial intelligence approaches need exhaustive resources to specify their inner workings, RC is based on a reservoir with ...

Text-mined fossil biodiversity dynamics using machine learning.

Proceedings. Biological sciences
Documented occurrences of fossil taxa are the empirical foundation for understanding large-scale biodiversity changes and evolutionary dynamics in deep time. The fossil record contains vast amounts of understudied taxa. Yet the compilation of huge vo...

Why Open-Endedness Matters.

Artificial life
Rather than acting as a review or analysis of the field, this essay focuses squarely on the motivations for investigating open-endedness and the opportunities it opens up. It begins by contemplating the awesome accomplishments of evolution in nature ...

On the Potential for Open-Endedness in Neural Networks.

Artificial life
Natural evolution gives the impression of leading to an open-ended process of increasing diversity and complexity. If our goal is to produce such open-endedness artificially, this suggests an approach driven by evolutionary metaphor. On the other han...

Evolutionary Innovations and Where to Find Them: Routes to Open-Ended Evolution in Natural and Artificial Systems.

Artificial life
This article presents a high-level conceptual framework to help orient the discussion and implementation of open-endedness in evolutionary systems. Drawing upon earlier work by Banzhaf et al. (2016), three different kinds of open-endedness are identi...

The anatomy of phenotype ontologies: principles, properties and applications.

Briefings in bioinformatics
The past decade has seen an explosion in the collection of genotype data in domains as diverse as medicine, ecology, livestock and plant breeding. Along with this comes the challenge of dealing with the related phenotype data, which is not only large...

Impact of Clinical Parameters in the Intrahost Evolution of HIV-1 Subtype B in Pediatric Patients: A Machine Learning Approach.

Genome biology and evolution
Determining the factors modulating the genetic diversity of HIV-1 populations is essential to understand viral evolution. This study analyzes the relative importance of clinical factors in the intrahost HIV-1 subtype B (HIV-1B) evolution and in the f...