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ORCIDSwarm and Evolutionary Computation, Volume 32
Volume 32, February 2017
- Adam P. Piotrowski:
Review of Differential Evolution population size. 1-24 - Anguluri Rajasekhar, Nandar Lynn, Swagatam Das
, Ponnuthurai N. Suganthan:
Computing with the collective intelligence of honey bees - A survey. 25-48 - Emrah Hancer, Dervis Karaboga:
A comprehensive survey of traditional, merge-split and evolutionary approaches proposed for determination of cluster number. 49-67 - Akhilesh Gotmare, Sankha Subhra Bhattacharjee, Rohan Patidar, Nithin V. George:
Swarm and evolutionary computing algorithms for system identification and filter design: A comprehensive review. 68-84 - Ruchika Malhotra, Megha Khanna, Rajeev R. Raje:
On the application of search-based techniques for software engineering predictive modeling: A systematic review and future directions. 85-109 - Mauro Castelli, Luca Manzoni, Sara Silva, Leonardo Vanneschi, Ales Popovic:
The influence of population size in geometric semantic GP. 110-120 - Jin Deng, Ling Wang:
A competitive memetic algorithm for multi-objective distributed permutation flow shop scheduling problem. 121-131 - Riham Moharam, Ehab Morsy:
Genetic algorithms to balanced tree structures in graphs. 132-139 - Hassan Ismkhan:
Effective heuristics for ant colony optimization to handle large-scale problems. 140-149 - Salima Nebti, Abdellah Boukerram:
Swarm intelligence inspired classifiers for facial recognition. 150-166 - Rajkamal Shukla, Dinesh Singh:
Experimentation investigation of abrasive water jet machining parameters using Taguchi and Evolutionary optimization techniques. 167-183 - Somnath Ganguly, Tarkeshwar Mahto, V. Mukherjee:
Integrated frequency and power control of an isolated hybrid power system considering scaling factor based fuzzy classical controller. 184-201 - Yogendra Arya, Narendra Kumar:
BFOA-scaled fractional order fuzzy PID controller applied to AGC of multi-area multi-source electric power generating systems. 202-218 - George J. Besseris:
Screening dense and noisy DOX-datasets with NN-blending and "dizzy" swarm intelligence: Profiling a water quality process. 219-233
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