Results 181 to 190 of about 7,821 (233)

The effect of different initial solutions on the metaheuristic algorithms for the single allocation p-hub center and routing problem. [PDF]

PeerJ Comput Sci
Kassoumeh AK, Kartal Z, Arslan A.
europepmc   +1 more source

Dual site external validation of artificial intelligence-enabled treatment monitoring for neovascular age-related macular degeneration in England. [PDF]

Eye (Lond)
Hogg HDJ, Talks SJ, Engelmann J, Teare MD, Pogose M, Patel PJ, Balaskas K, Maniatopoulos G, Keane PA.   +8 more
europepmc   +1 more source

Modelling cross-diffusion in MHD Williamson nanofluid flow over a nonlinear stretching surface via Morlet wavelet neural networks. [PDF]

Sci Rep
Arif K, Saeed ST, Aslam MN, Younis J, Riaz A, Saleem S, Saleem S.   +6 more
europepmc   +1 more source

Optimization of range based self-localization problem in wireless sensor networks using improved cuckoo search algorithm. [PDF]

Sci Rep
Aouthu S, Dhanalakshmi N, Seshagiri T, Dilli R.   +3 more
europepmc   +1 more source

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Hyper-heuristics

2018
This chapter presents a literature review of the main advances in the field of hyper-heuristics, since the publication of a survey paper in 2013. The chapter demonstrates the most recent advances in hyper-heuristic foundations, methodologies, theory, and application areas.
Epitropakis, M.G., Burke, E.K.
openaire   +1 more source

Template method hyper-heuristics

Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation, 2014
The optimization literature is awash with metaphorically-inspired metaheuristics and their subsequent variants and hybridizations. This results in a plethora of methods, with descriptions that are often polluted with the language of the metaphor which inspired them [8].
John R. Woodward, Jerry Swan
openaire   +1 more source

Hyper-heuristics: Autonomous Problem Solvers

2021
Algorithm design is a general task for any problem-solving scenario. For Search and Optimization, this task becomes rather challenging due to the immense algorithm design space. Those existing design options are usually traversed to devise algorithms by the human algorithm development experts together with the specialists on the target problem domains.
openaire   +1 more source

Evolutionary Cross-Domain Hyper-Heuristics

Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation, 2015
Designing effective algorithms to solve computational problems is difficult and time-consuming. The standard methodology for designing such algorithms is “top-down”. This process breaks down large problems into more understood components and eventually identifies problem-specific operators that algorithms need to use to solve the given problem.
Patricia Ryser-Welch, Julian F. Miller, Shariar Asta   +2 more
openaire   +1 more source