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210512 ||| eng |
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|a 9783039434558
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|a books978-3-03943-455-8
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|a 9783039434541
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| 100 |
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|a Caraffini, Fabio
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| 245 |
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|a Evolutionary Computation & Swarm Intelligence
|h Elektronische Ressource
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| 260 |
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|a Basel, Switzerland
|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2020
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| 300 |
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|a 1 electronic resource (286 p.)
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| 653 |
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|a fuzzy hashing
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| 653 |
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|a memetic algorithms
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| 653 |
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|a screening criteria
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| 653 |
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|a identification
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| 653 |
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|a multi-objective deterministic optimization, derivative-free
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| 653 |
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|a maximum k-coverage
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| 653 |
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|a concept evolution
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| 653 |
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|a genetic algorithm
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| 653 |
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|a routing
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| 653 |
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|a large-scale optimization
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| 653 |
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|a concept drift
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| 653 |
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|a one billion variables
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| 653 |
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|a evolutionary computation
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| 653 |
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|a Wilcoxon rank-sum
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| 653 |
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|a density based clustering
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| 653 |
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|a robot
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| 653 |
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|a signatures
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| 653 |
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|a feature selection
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| 653 |
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|a particle swarm
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| 653 |
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|a LZJD
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| 653 |
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|a Swarm Intelligence
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| 653 |
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|a PSO
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| 653 |
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|a approximate matching
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| 653 |
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|a population based algorithms
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| 653 |
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|a compact optimization
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| 653 |
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|a k-means centroid
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| 653 |
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|a Particle Swarm Optimization
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| 653 |
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|a swarm intelligence
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| 653 |
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|a entanglement degree
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| 653 |
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|a DE-MPFSC algorithm
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| 653 |
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|a imbalanced data
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| 653 |
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|a data sampling
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| 653 |
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|a Holm-Bonferroni
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| 653 |
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|a algorithmic design
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| 653 |
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|a Information technology industries / bicssc
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| 653 |
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|a normalization
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| 653 |
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|a parameter tuning
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| 653 |
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|a edit distance
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| 653 |
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|a meta-heuristic algorithms
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| 653 |
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|a online clustering
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| 653 |
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|a evolutionary algorithms
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| 653 |
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|a wireless sensor networks
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| 653 |
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|a optimisation
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| 653 |
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|a context-triggered piecewise hashing
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| 653 |
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|a hybrid algorithms
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| 653 |
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|a global/local optimization
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| 653 |
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|a nature-inspired algorithms
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| 653 |
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|a Social Network Optimization
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| 653 |
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|a fitness trend
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| 653 |
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|a analysis
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| 653 |
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|a ssdeep
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| 653 |
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|a benchmark suite
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| 653 |
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|a data integration
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| 653 |
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|a sdhash
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| 653 |
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|a metaheuristic optimisation
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| 653 |
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|a redundant representation
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| 653 |
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|a metaheuristics
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| 653 |
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|a discrete optimization
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| 653 |
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|a multi-thread programming
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| 653 |
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|a Markov process
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| 653 |
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|a manipulator
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| 653 |
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|a similarity detection
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| 653 |
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|a instance weighting
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| 653 |
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|a kinematic parameters
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| 653 |
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|a dynamic stream clustering
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| 653 |
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|a memetic computing
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| 653 |
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|a simulation-based design optimization
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| 653 |
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|a estimation distribution algorithms
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| 700 |
1 |
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|a Santucci, Valentino
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| 700 |
1 |
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|a Milani, Alfredo
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| 700 |
1 |
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|a Caraffini, Fabio
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b DOAB
|a Directory of Open Access Books
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| 500 |
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|a Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
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| 028 |
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|a 10.3390/books978-3-03943-455-8
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| 856 |
4 |
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|u https://www.mdpi.com/books/pdfview/book/3131
|7 0
|x Verlag
|3 Volltext
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| 856 |
4 |
2 |
|u https://directory.doabooks.org/handle/20.500.12854/69339
|z DOAB: description of the publication
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|a 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 hold, and (3) the dimensionality of the problem or stringent real-time requirements make it infeasible to find an exact solution mathematically are from the field of Evolutionary Computation (EC) and Swarm Intelligence (SI). The latter are broad and still growing subjects in Computer Science in the study of metaheuristic approaches, i.e., those approaches which do not make any assumptions about the problem function, inspired from natural phenomena such as, in the first place, the evolution process and the collaborative behaviours of groups of animals and communities, respectively. This book contains recent advances in the EC and SI fields, covering most themes currently receiving a great deal of attention such as benchmarking and tunning of optimisation algorithms, their algorithm design process, and their application to solve challenging real-world problems to face large-scale domains.
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