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This monograph presents examples of best practices when combining
bioinspired algorithms with parallel architectures. The book
includes recent work by leading researchers in the field and offers
a map with the main paths already explored and new ways towards the
future. Parallel Architectures and Bioinspired Algorithms will be
of value to both specialists in Bioinspired Algorithms, Parallel
and Distributed Computing, as well as computer science students
trying to understand the present and the future of Parallel
Architectures and Bioinspired Algorithms.
This monograph presents examples of best practices when combining
bioinspired algorithms with parallel architectures. The book
includes recent work by leading researchers in the field and offers
a map with the main paths already explored and new ways towards the
future. Parallel Architectures and Bioinspired Algorithms will be
of value to both specialists in Bioinspired Algorithms, Parallel
and Distributed Computing, as well as computer science students
trying to understand the present and the future of Parallel
Architectures and Bioinspired Algorithms.
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Applications of Evolutionary Computation - 23rd European Conference, EvoApplications 2020, Held as Part of EvoStar 2020, Seville, Spain, April 15-17, 2020, Proceedings (Paperback, 1st ed. 2020)
Pedro A. Castillo, Juan Luis Jimenez Laredo, Francisco Fernandez De Vega
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R1,692
Discovery Miles 16 920
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Ships in 10 - 15 working days
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This book constitutes the refereed proceedings of the 23rd European
Conference on Applications of Evolutionary Computation,
EvoApplications 2020, held as part of Evo*2020, in Seville, Spain,
in April 2020, co-located with the Evo*2020 events EuroGP,
EvoMUSART and EvoCOP. The 44 full papers presented in this book
were carefully reviewed and selected from 62 submissions. The
papers cover a wide spectrum of topics, ranging from applications
of bio-inspired techniques on social networks, evolutionary
computation in digital healthcare and personalized medicine,
soft-computing applied to games, applications of deep-bioinspired
algorithms, parallel and distributed systems, and evolutionary
machine learning.
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