Results 111 to 120 of about 2,128 (206)

Novel Uncertainty-Aware Deep Neuroevolution Algorithm to Quantify Tidal Forecasting

, 2022
Novel Uncertainty-Aware Deep Neuroevolution Algorithm to Quantify Tidal ...
F Wang (7715261), S Ahmadian (13361544), Abbas Khosravi (13078944), SMJ Jalali (14450514), MK Noman (15832853), SMS Islam (7723154), JPS Catalao (14455401)   +6 more
core  

Estimation of Anthocyanins in Heterogeneous and Homogeneous Bean Landraces Using Probabilistic Colorimetric Representation with a Neuroevolutionary Approach

Mathematical and Computational Applications
The concentration of anthocyanins in common beans indicates their nutritional value. Understanding this concentration makes it possible to identify the functional compounds present.
José-Luis Morales-Reyes, Elia-Nora Aquino-Bolaños, Héctor-Gabriel Acosta-Mesa, Aldo Márquez-Grajales   +3 more
doaj   +1 more source

Neuroevolution with Perceptron Turing Machines

CoRR, 2019
Patent ...
openaire   +2 more sources

Decision making in robot soccer using neuroevolution

, 2013
Während eines Fußballspiels befinden sich die Spieler einer Mannschaft in einer Reihe unterschiedlicher Spielsituationen. Jede dieser Spielsituationen erfordert die Ausführung einer Aktion, die der Spieler autonom bestimmen muss.
Oberhauser, Arthur
core   +3 more sources

Comparing the Performance of Off-Policy Deep Reinforcement Learning and Neuroevolution in Stealth Game Problems

IEEE Access
Deep Learning is a rapidly evolving field with the potential to revolutionize the video game industry. However, its application remains limited, particularly in unexplored genres such as stealth games.
Pedro F. Silva, Jacinto Estima, Ines Nobre, Edirlei Soares De Lima   +3 more
doaj   +1 more source

Many-Objective Neuroevolution for Testing Games [PDF]

Generating tests for games is challenging due to the high degree of randomisation inherent to games and hard-to-reach program states that require sophisticated gameplay.
Schmelz, Katrin, Fraser, Gordon, Feldmeier, Patric   +2 more
core   +3 more sources

Practical Approach to Studying Evolutionary Methods for Setting Weight Coefficients of Artificial Neural Networks

Цифровая трансформация
The article describes the problems of developing neurocontrollers for controlling dynamic objects, including the complexity of forming training data sets. It is indicated that one of the known methods for training an artificial neural network controlling
D. O. Petrov
doaj   +1 more source

Improved Search in Neuroevolution Using a Neural Architecture Classifier With the CNN Architecture Encoding as Feature Vector

IEEE Access
Designing Convolutional Neural Networks (CNNs) for a specific task requires not only Deep Learning expertise but also knowledge of the problem. The goal of Neuroevolution is to find CNN architectures automatically through evolution.
Jhon I. Pilataxi, Jorge E. Zambrano, Claudio A. Perez, Kevin W. Bowyer   +3 more
doaj   +1 more source

Neuroevolution-Based Network Architecture Evolution in Semiconductor Manufacturing. [PDF]

ACS Omega, 2023
Feng YW, Jiang BR, Lin AS.
europepmc   +1 more source

Designing Deep Neural Networks through Neuroevolution

This study investigated the designing of deep neural networks using novel neuroevolution models. Advanced algorithms were applied to three real-world problems including energy and medicine applications.
Seyed Mohammad Jafar Jalali (13436616)
core