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Evolutionary Computation & Swarm Intelligence

The vast majority of real-world problems can be expressed as an optimisation task by formulating an objective function, also known as cost or fitness function. The most logical methods to optimise such a function when (1) an analytical expression is not available, (2) mathematical hypotheses do not...

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Guardat en:
Format: Online
Idioma:anglès
Publicat: MDPI - Multidisciplinary Digital Publishing Institute 2021
Matèries:
dynamic stream clustering
online clustering
metaheuristics
optimisation
population based algorithms
density based clustering
k-means centroid
concept drift
concept evolution
imbalanced data
screening criteria
DE-MPFSC algorithm
Markov process
entanglement degree
data integration
PSO
robot
manipulator
analysis
kinematic parameters
identification
approximate matching
context-triggered piecewise hashing
edit distance
fuzzy hashing
LZJD
multi-thread programming
sdhash
signatures
similarity detection
ssdeep
maximum k-coverage
redundant representation
normalization
genetic algorithm
hybrid algorithms
memetic algorithms
particle swarm
multi-objective deterministic optimization, derivative-free
global/local optimization
simulation-based design optimization
wireless sensor networks
routing
Swarm Intelligence
Particle Swarm Optimization
Social Network Optimization
compact optimization
discrete optimization
large-scale optimization
one billion variables
evolutionary algorithms
estimation distribution algorithms
algorithmic design
metaheuristic optimisation
evolutionary computation
swarm intelligence
memetic computing
parameter tuning
fitness trend
Wilcoxon rank-sum
Holm–Bonferroni
benchmark suite
data sampling
feature selection
instance weighting
nature-inspired algorithms
meta-heuristic algorithms
thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries
Accés en línia:ONIX_20210501_9783039434541_1085
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