| 000 | 04921naaaa2201141uu 4500 | ||
|---|---|---|---|
| 001 | https://directory.doabooks.org/handle/20.500.12854/69339 | ||
| 005 | 20220220071407.0 | ||
| 020 | _abooks978-3-03943-455-8 | ||
| 020 | _a9783039434541 | ||
| 020 | _a9783039434558 | ||
| 024 | 7 |
_a10.3390/books978-3-03943-455-8 _cdoi |
|
| 041 | 0 | _aEnglish | |
| 042 | _adc | ||
| 072 | 7 |
_aKNTX _2bicssc |
|
| 100 | 1 |
_aCaraffini, Fabio _4edt |
|
| 700 | 1 |
_aSantucci, Valentino _4edt |
|
| 700 | 1 |
_aMilani, Alfredo _4edt |
|
| 700 | 1 |
_aCaraffini, Fabio _4oth |
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| 700 | 1 |
_aSantucci, Valentino _4oth |
|
| 700 | 1 |
_aMilani, Alfredo _4oth |
|
| 245 | 1 | 0 | _aEvolutionary Computation & Swarm Intelligence |
| 260 |
_aBasel, Switzerland _bMDPI - Multidisciplinary Digital Publishing Institute _c2020 |
||
| 300 | _a1 electronic resource (286 p.) | ||
| 506 | 0 |
_aOpen Access _2star _fUnrestricted online access |
|
| 520 | _aThe 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. | ||
| 540 |
_aCreative Commons _fhttps://creativecommons.org/licenses/by/4.0/ _2cc _4https://creativecommons.org/licenses/by/4.0/ |
||
| 546 | _aEnglish | ||
| 650 | 7 |
_aInformation technology industries _2bicssc |
|
| 653 | _adynamic stream clustering | ||
| 653 | _aonline clustering | ||
| 653 | _ametaheuristics | ||
| 653 | _aoptimisation | ||
| 653 | _apopulation based algorithms | ||
| 653 | _adensity based clustering | ||
| 653 | _ak-means centroid | ||
| 653 | _aconcept drift | ||
| 653 | _aconcept evolution | ||
| 653 | _aimbalanced data | ||
| 653 | _ascreening criteria | ||
| 653 | _aDE-MPFSC algorithm | ||
| 653 | _aMarkov process | ||
| 653 | _aentanglement degree | ||
| 653 | _adata integration | ||
| 653 | _aPSO | ||
| 653 | _arobot | ||
| 653 | _amanipulator | ||
| 653 | _aanalysis | ||
| 653 | _akinematic parameters | ||
| 653 | _aidentification | ||
| 653 | _aapproximate matching | ||
| 653 | _acontext-triggered piecewise hashing | ||
| 653 | _aedit distance | ||
| 653 | _afuzzy hashing | ||
| 653 | _aLZJD | ||
| 653 | _amulti-thread programming | ||
| 653 | _asdhash | ||
| 653 | _asignatures | ||
| 653 | _asimilarity detection | ||
| 653 | _assdeep | ||
| 653 | _amaximum k-coverage | ||
| 653 | _aredundant representation | ||
| 653 | _anormalization | ||
| 653 | _agenetic algorithm | ||
| 653 | _ahybrid algorithms | ||
| 653 | _amemetic algorithms | ||
| 653 | _aparticle swarm | ||
| 653 | _amulti-objective deterministic optimization, derivative-free | ||
| 653 | _aglobal/local optimization | ||
| 653 | _asimulation-based design optimization | ||
| 653 | _awireless sensor networks | ||
| 653 | _arouting | ||
| 653 | _aSwarm Intelligence | ||
| 653 | _aParticle Swarm Optimization | ||
| 653 | _aSocial Network Optimization | ||
| 653 | _acompact optimization | ||
| 653 | _adiscrete optimization | ||
| 653 | _alarge-scale optimization | ||
| 653 | _aone billion variables | ||
| 653 | _aevolutionary algorithms | ||
| 653 | _aestimation distribution algorithms | ||
| 653 | _aalgorithmic design | ||
| 653 | _ametaheuristic optimisation | ||
| 653 | _aevolutionary computation | ||
| 653 | _aswarm intelligence | ||
| 653 | _amemetic computing | ||
| 653 | _aparameter tuning | ||
| 653 | _afitness trend | ||
| 653 | _aWilcoxon rank-sum | ||
| 653 | _aHolm–Bonferroni | ||
| 653 | _abenchmark suite | ||
| 653 | _adata sampling | ||
| 653 | _afeature selection | ||
| 653 | _ainstance weighting | ||
| 653 | _anature-inspired algorithms | ||
| 653 | _ameta-heuristic algorithms | ||
| 856 | 4 | 0 |
_awww.oapen.org _uhttps://mdpi.com/books/pdfview/book/3131 _70 _zDOAB: download the publication |
| 856 | 4 | 0 |
_awww.oapen.org _uhttps://directory.doabooks.org/handle/20.500.12854/69339 _70 _zDOAB: description of the publication |
| 999 |
_c72668 _d72668 |
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