AIMC Topic: Biological Evolution

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Reprint of: Analysis of an evolutionary algorithm for complex fuzzy cognitive map learning based on graph theory metrics and output concepts.

Bio Systems Nov 23, 2019
The fuzzy cognitive map (FCM) is an effective tool for modeling dynamic decision support systems. It describes the analyzed phenomenon in the form of key concepts and the causal connections between them. The main aspects of the building of the FCM mo...
Models, Theoretical Fuzzy Logic Biological Evolution Algorithms Cognition
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Intelligent architectures for robotics: The merging of cognition and emotion.

Physics of life reviews Oct 22, 2019
What is the place of emotion in intelligent robots? In the past two decades, researchers have advocated for the inclusion of some emotion-related components in the general information processing architecture of autonomous agents, say, for better comm...
Biological Evolution Brain Executive Function Cognition Emotions Humans Robotics
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Combining evolution and self-organization to find natural Boolean representations in unconventional computational media.

Bio Systems Jul 29, 2019
Designing novel unconventional computing systems often requires the selection of the computational structure as well as choosing the right symbol encoding. Several approaches apply heuristic search and evolutionary algorithms to find both computation...
Neural Networks, Computer Algorithms Computational Biology Logic Models, Theoretical Computer Simulation Biological Evolution Feedback
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Simple Hyper-Heuristics Control the Neighbourhood Size of Randomised Local Search Optimally for LeadingOnes.

Evolutionary computation May 23, 2019
Selection hyper-heuristics (HHs) are randomised search methodologies which choose and execute heuristics during the optimisation process from a set of low-level heuristics. A machine learning mechanism is generally used to decide which low-level heur...
Computer Heuristics Algorithms Computer Simulation Search Engine Biological Evolution Machine Learning
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A neural network-evolutionary computational framework for remaining useful life estimation of mechanical systems.

Neural networks : the official journal of the International Neural Network Society May 4, 2019
This paper presents a framework for estimating the remaining useful life (RUL) of mechanical systems. The framework consists of a multi-layer perceptron and an evolutionary algorithm for optimizing the data-related parameters. The framework makes use...
Algorithms Databases, Factual Neural Networks, Computer Biological Evolution Neurons Linear Models
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Interactive biomedical ontology matching.

PloS one Apr 17, 2019
Due to continuous evolution of biomedical data, biomedical ontologies are becoming larger and more complex, which leads to the existence of many overlapping information. To support semantic inter-operability between ontology-based biomedical systems,...
Semantics Biological Ontologies Phenotype Health Information Interoperability Humans Quality Control Biological Evolution Algorithms
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Evolution of Deep Convolutional Neural Networks Using Cartesian Genetic Programming.

Evolutionary computation Mar 22, 2019
The convolutional neural network (CNN), one of the deep learning models, has demonstrated outstanding performance in a variety of computer vision tasks. However, as the network architectures become deeper and more complex, designing CNN architectures...
Biological Evolution Deep Learning Datasets as Topic Neural Networks, Computer Algorithms
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Analysis of an evolutionary algorithm for complex fuzzy cognitive map learning based on graph theory metrics and output concepts.

Bio Systems Feb 24, 2019
The fuzzy cognitive map (FCM) is an effective tool for modeling dynamic decision support systems. It describes the analyzed phenomenon in the form of key concepts and the causal connections between them. The main aspects of the building of the FCM mo...
Algorithms Learning Cognition Fuzzy Logic Humans Computer Graphics Biological Evolution
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Guiding Neuroevolution with Structural Objectives.

Evolutionary computation Feb 15, 2019
The structure and performance of neural networks are intimately connected, and by use of evolutionary algorithms, neural network structures optimally adapted to a given task can be explored. Guiding such neuroevolution with additional objectives rela...
Humans Algorithms Neural Networks, Computer Computer Simulation Biological Evolution
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Multi-task learning improves ancestral state reconstruction.

Theoretical population biology Jan 11, 2019
We consider the ancestral state reconstruction problem where we need to infer phenotypes of ancestors using observations from present-day species. For this problem, we propose a multi-task learning method that uses regularized maximum likelihood to e...
Biological Evolution Likelihood Functions Stochastic Processes Phenotype Computer Simulation Humans Machine Learning Phylogeny Models, Biological Mammals Learning Animals
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