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Dynamic Programming: A comprehensive review of Algorithms, Applications and Advances
2018
Zenodo
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The integration of DP with device learning opens new avenues for research and application.Dynamic Programming (DP) stands as a foundational optimization approach with vast programs across numerous fields ...
This evaluate gives a comprehensive exploration of DP, encompassing its ancient evolution, fundamental ideas, algorithmic strategies, and significant applications. ...
Historical Evolution: The roots of dynamic programming can be traced lower back to Bellman's work on the principle of optimality, which laid the inspiration for this optimization approach. ...
doi:10.5281/zenodo.10691736
fatcat:op2aqrijkfflthfeehv6mjjcgq
Research on the Mechanical Automation Technology based on Evolutionary Algorithms and Artificial Intelligence Theory
2016
DEStech Transactions on Social Science Education and Human Science
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Computer aided design refers to the graphics device by using the computer aided design for mechanical design work. ...
Assuming that general co-evolution algorithm can be represented as a matrix game, as the optimization solution of the game, eventually reach equilibrium of that co-evolution algorithm. ...
doi:10.12783/dtssehs/isetem2016/4380
fatcat:igv7mqjcm5alrazqapqonaaena
Response: Do we believe in fairy tales?
2017
International Journal of First Aid Education
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And I must agree that there is no time, nor reason to let the evolution decide how we deliver First Aid, how we learn First Aid or how we teach First Aid. ...
The white paper asks for a revolution instead of evolution (Pellegrino et al., 2017) . ...
doi:10.21038/ijfa.2017.0003
fatcat:6yhnhaz2cvartff44m3ci6y2je
Accurate computational evolution of proteins and its dependence on deep learning
[article]
2021
arXiv
pre-print
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of directed evolution. ...
Although directed evolution methods significantly aid the construction of a functionally improved molecule, their credibility depends on the successful excavation of the functionally similar sequence space ...
Conclusion: Computational learning based enzymatic evolution is a multi-objective optimization problem. ...
arXiv:2106.01951v1
fatcat:wicc7bs7mbanlpuagdyrsj2yky
An effective hybrid cuckoo search and genetic algorithm for constrained engineering design optimization
2013
Engineering optimization (Print)
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A Differential Evolution based algorithm for single container loading problem ( ...
Q-learning policies for a single agent foraging tasks (2010) ISMA'10 -7th International Symposium on Mechatronics and its Applications, art. no. 5478440, . ...
Q-learning policies for multi-agent foraging task (2010) Communications in Computer and Information Science, 103 CCIS, pp. 194-201. Mohan, Y., Ponnambalam, S.G. ...
doi:10.1080/0305215x.2013.836640
fatcat:aarjyfi5rrbold2yocuv4xbswa
Scheme of Big-Data Supported Interactive Evolutionary Computation
2017
DEStech Transactions on Computer Science and Engineering
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For this purpose, transfer learning is adopted. ...
transferred to accelerate the optimization. ...
For the users who participated in the optimization, their preference as the evolution history information is stored in the big data repository. ...
doi:10.12783/dtcse/itme2017/7958
fatcat:65uhohaimrfcdkqjng52mmp4iq
AI in Finance: Challenges, Techniques and Opportunities
[article]
2021
arXiv
pre-print
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We then structure and illustrate the data-driven analytics and learning of financial businesses and data. ...
The comparison, criticism and discussion of classic vs. modern AI techniques for finance are followed. ...
AIDS for broader economic-financial areas. ...
arXiv:2107.09051v1
fatcat:g62cz4dqt5dcrbckn4lbveat3u
Beyond Explainability: Leveraging Interpretability for Improved Adversarial Learning
[article]
2019
arXiv
pre-print
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In particular, this study puts forward a novel strategy for leveraging gradient-based interpretability in the realm of adversarial examples, where we use insights gained to aid adversarial learning. ...
In this study, we propose the leveraging of interpretability for tasks beyond purely the purpose of explainability. ...
The authors would also like to thank Nvidia for donating GPUs used in the research. ...
arXiv:1904.09633v1
fatcat:4qbjha3amzexponzujifvll7g4
An Alternate Iterative Differential Evolution Algorithm for Parameter Identification of Chaotic Systems
2015
Discrete Dynamics in Nature and Society
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Parameter estimation of chaotic systems plays a key role for control and synchronization of chaotic systems. ...
At first, the parameter estimation of chaotic systems is mathematically formulated as a global continuous optimization problem. ...
Then, an alternate iterative differential algorithm, AIDE for short, is presented to solve the global optimization problem. First, a new mutation strategy DE/pbest/1 is proposed in AIDE. ...
doi:10.1155/2015/740721
fatcat:u6alvdoor5bdnpigmmksyz6yca
The Digital (R)Evolution of Cardiac Surgery and Cardiology
2022
e-Journal of Cardiovascular Medicine
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Chang (MD, MBA, MPH, MS, chief intelligence and innovation officer of the Children's Hospital of Orange County, Chair of the American Board of AI in Medicine) for his excellent talk about "Artificial Intelligence ...
The limiting factor at present is the lack of usable data for machine learning which has yet to be generated. ...
"Collective intelligence" and "swarm learning" will become the ultimate Heart Team where artificial experts and networked human specialist groups will discuss the patient and decide about the optimal treatment ...
doi:10.32596/ejcm.galenos.2022.2022-01-04
fatcat:g75hijf2qnh7lowrqpwkisb2kq
Dynamic Programming: A comprehensive review of Algorithms, Applications and Advances
2024
Zenodo
Preserved Fulltext
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The integration of DP with device learning opens new avenues for research and application. ...
This evaluate gives a comprehensive exploration of DP, encompassing its ancient evolution, fundamental ideas, algorithmic strategies, and significant applications. ...
Historical Evolution: The roots of dynamic programming can be traced lower back to Bellman's work on the principle of optimality, which laid the inspiration for this optimization approach. ...
doi:10.5281/zenodo.10805215
fatcat:agsnpvor5nbm3clteumrlt52fu
Evolution in Cognition 2016 Chairs' Welcome
2016
Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion - GECCO '16 Companion
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It is in this context that the optimizing and creative abilities of EAs could become an ideal framework that complement, aid in understanding, and facilitate the implementation of cognitive processes. ...
The combination of these two abilities makes EAs a tool of choice for the resolution of complex problems. ...
He has also worked extensively on the combination of evolution and learning [13] . ...
doi:10.1145/2908961.2931670
dblp:conf/gecco/DoncieuxADV16
fatcat:qq7nmla3hzhchad66nvguzbaim
Precision Agriculture technologies
2024
Zenodo
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drones for real-time records acquisition, strong facts analytics for actionable insights, and variable charge era for precise enter application. ...
Through the usage of GPS, far off sensing, statistics analytics, and automation, these technologies enable farmers to optimize useful resource allocation, beautify crop yields, and sell sustainable agricultural ...
It has empowered farmers with tools and insights to make informed choices, optimize aid allocation, and strive for sustainable and green agricultural manufacturing. ...
doi:10.5281/zenodo.10782318
fatcat:emcy4lhmazhqnomvnc2bpv6hd4
Table of Contents
2018
IEEE Transactions on Signal Processing
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Nie 2374 Global Energy Efficiency Optimization for Wireless-Powered Massive MIMO Aided Multiway AF Relay Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...
Dai 2358 Grid Evolution Method for DOA Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Q. Wang, Z. Zhao, Z. Chen, and Z. ...
doi:10.1109/tsp.2018.2821743
fatcat:3dvvitna2nftdiafsk6tcv3l2y
Deep Learning Frameworks In High Performance Computing Environments
2018
Zenodo
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This aids the enterprises to run the existing frameworks that are already fully optimized instead of investing time and resources into deep learning and machine learning tools and avoid to start tuning ...
It is important for the enterprises, deep learning and machine-learning practitioners to leverage the optimized and fully tuned platforms based on the hardware criteria and the requirements for scientific ...
doi:10.5281/zenodo.1186971
fatcat:eggksvlahzfd3inqj3zwats3yi
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