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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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|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 routing
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| 653 |
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|a genetic algorithm
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| 653 |
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|a large-scale optimization
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| 653 |
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|a one billion variables
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| 653 |
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|a concept drift
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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 compact optimization
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| 653 |
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|a population based algorithms
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| 653 |
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|a k-means centroid
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| 653 |
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|a swarm intelligence
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| 653 |
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|a Particle Swarm Optimization
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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 parameter tuning
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| 653 |
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|a normalization
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| 653 |
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|a Information technology industries / bicssc
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| 653 |
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|a meta-heuristic algorithms
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| 653 |
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|a edit distance
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| 653 |
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|a evolutionary algorithms
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| 653 |
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|a online clustering
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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 global/local optimization
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| 653 |
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|a hybrid algorithms
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| 653 |
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|a context-triggered piecewise hashing
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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 memetic computing
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| 653 |
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|a dynamic stream clustering
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| 653 |
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|a kinematic parameters
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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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| 024 |
8 |
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|a 10.3390/books978-3-03943-455-8
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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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| 856 |
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|u https://www.mdpi.com/books/pdfview/book/3131
|7 0
|x Verlag
|3 Volltext
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|a 000
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|a 576
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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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