scholarly journals The Camouflage Machine: Optimizing protective coloration using deep learning with genetic algorithms

Evolution ◽  
2021 ◽  
Author(s):  
John G. Fennell ◽  
Laszlo Talas ◽  
Roland J. Baddeley ◽  
Innes C. Cuthill ◽  
Nicholas E. Scott‐Samuel
Keyword(s):  
Genetic Algorithms ◽  
Deep Learning ◽  
Protective Coloration

Recognizing arabic handwritten characters using deep learning and genetic algorithms

2021 ◽  
Author(s):  
Hossam Magdy Balaha ◽  
Hesham Arafat Ali ◽  
Esraa Khaled Youssef ◽  
Asmaa Elsayed Elsayed ◽  
Reem Adel Samak ◽  
...  
Keyword(s):  
Genetic Algorithms ◽  
Deep Learning

scholarly journals A novel hybrid scheme using genetic algorithms and deep learning for the reconstruction of portuguese tile panels

2019 ◽  
Author(s):  
Daniel Rika ◽  
Dror Sholomon ◽  
Eli (Omid) David ◽  
Nathan S. Netanyahu
Keyword(s):  
Genetic Algorithms ◽  
Deep Learning ◽  
Hybrid Scheme

scholarly journals An Experiment on the Use of Genetic Algorithms for Topology Selection in Deep Learning

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Fernando Mattioli ◽  
Daniel Caetano ◽  
Alexandre Cardoso ◽  
Eduardo Naves ◽  
Edgard Lamounier
Keyword(s):  
Genetic Algorithms ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Complex Task ◽  
Trial And Error ◽  
Complex Solution ◽  
Computation Algorithms ◽  
Computational Resources ◽  
Topology Selection

The choice of a good topology for a deep neural network is a complex task, essential for any deep learning project. This task normally demands knowledge from previous experience, as the higher amount of required computational resources makes trial and error approaches prohibitive. Evolutionary computation algorithms have shown success in many domains, by guiding the exploration of complex solution spaces in the direction of the best solutions, with minimal human intervention. In this sense, this work presents the use of genetic algorithms in deep neural networks topology selection. The evaluated algorithms were able to find competitive topologies while spending less computational resources when compared to state-of-the-art methods.

scholarly journals Observer variation-aware medical image segmentation by combining deep learning and surrogate-assisted genetic algorithms

2020 ◽  
Author(s):  
Arkadiy Dushatskiy ◽  
Adriënne M. Mendrik ◽  
Peter A. N. Bosman ◽  
Tanja Alderliesten
Keyword(s):  
Genetic Algorithms ◽  
Image Segmentation ◽  
Deep Learning ◽  
Medical Image ◽  
Observer Variation ◽  
Medical Image Segmentation

scholarly journals The Camouflage Machine: Optimising protective colouration using deep learning with genetic algorithms

2020 ◽  
Author(s):  
J. G. Fennell ◽  
L. Talas ◽  
R. J. Baddeley ◽  
I. C. Cuthill ◽  
N. E. Scott-Samuel
Keyword(s):  
Genetic Algorithms ◽  
Deep Learning ◽  
Reliable Method ◽  
Visual Detection ◽  
Natural Scenes ◽  
Visual Systems ◽  
Human Conflict ◽  
Human Participants ◽  
Essential Problem ◽  
Do So

AbstractThe essential problem in visual detection is separating an object from its background. Whether in nature or human conflict, camouflage aims to make the problem harder, while conspicuous signals (e.g. for warning or mate attraction) require the opposite. Our goal is to provide a reliable method for identifying the hardest and easiest to find patterns, for any given environment. The problem is challenging because the parameter space provided by varying natural scenes and potential patterns is vast. Here we successfully solve the problem using deep learning with genetic algorithms and illustrate our solution by identifying appropriate patterns in two environments. To show the generality of our approach, we do so for both trichromatic and dichromatic visual systems. Patterns were validated using human participants; those identified as the best camouflage were significantly harder to find than a widely adopted military camouflage pattern, while those identified as most conspicuous were significantly easier than other patterns. Our method, dubbed the ‘Camouflage Machine’, will be a useful tool for those interested in identifying the most effective patterns in a given context.

scholarly journals A Novel Action Recognition Framework Based on Deep-Learning and Genetic Algorithms

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 100631-100644 ◽  
Author(s):  
Abdullah Asim Yilmaz ◽  
Mehmet Serdar Guzel ◽  
Erkan Bostanci ◽  
Iman Askerzade
Keyword(s):  
Genetic Algorithms ◽  
Deep Learning ◽  
Action Recognition

scholarly journals REDUCING ERROR RATE OF DEEP LEARNING USING AUTO ENCODER AND GENETIC ALGORITHMS

2016 ◽  
Vol 16 (4) ◽  
pp. 41-53
Author(s):  
F. Habeeb ◽  
Sherihan Abuelenin ◽  
Samir Elmougy
Keyword(s):  
Genetic Algorithms ◽  
Deep Learning ◽  
Error Rate
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