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221112s2022 sz a o 101 0 eng d |
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|b eng
|e rda
|e pn
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|d GW5XE
|d EBLCP
|d OCLCF
|d UKAHL
|d OCLCQ
|d INT
|d OCLCO
|d OCLCL
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|a 1350689905
|a 1374612714
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|a 9783031210945
|q (electronic bk.)
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|q (electronic bk.)
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|q (5)
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|a 830312109X
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7 |
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|a 10.1007/978-3-031-21094-5
|2 doi
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| 035 |
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|a (OCoLC)1350669354
|z (OCoLC)1350689905
|z (OCoLC)1374612714
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4 |
|a QA76.9.N37
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| 072 |
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|a UK
|2 bicssc
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|a COM043000
|2 bisacsh
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|a UK
|2 thema
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|a HCDD
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| 111 |
2 |
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|a BIOMA (Conference)
|n (10th :
|d 2022 :
|c Maribor, Slovenia)
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| 245 |
1 |
0 |
|a Bioinspired optimization methods and their applications :
|b 10th international conference, BIOMA 2022, Maribor, Slovenia, November 17-18, 2022 : proceedings /
|c Marjan Mernik, Tome Eftimov, Matej Črepinšek (eds.).
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| 246 |
3 |
0 |
|a BIOMA 2022
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| 264 |
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1 |
|a Cham :
|b Springer,
|c [2022]
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| 264 |
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4 |
|c ©2022
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| 300 |
|
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|a 1 online resource (x, 277 pages) :
|b illustrations (chiefly color).
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 347 |
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|a text file
|2 rdaft
|0 http://rdaregistry.info/termList/fileType/1002
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| 490 |
1 |
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|a Lecture notes in computer science ;
|v 13627
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| 500 |
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|a International conference proceedings.
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| 500 |
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|a Includes author index.
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| 520 |
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|a This book constitutes the refereed proceedings of the 10th International Conference on Bioinspired Optimization Methods and Their Applications, BIOMA 2022, held in Maribor, Slovenia, in November 2022. The 19 full papers presented in this book were carefully reviewed and selected from 23 submissions. The papers in this BIOMA proceedings specialized in bioinspired algorithms as a means for solving the optimization problems and came in two categories: theoretical studies and methodology advancements on the one hand, and algorithm adjustments and their applications on the other.
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| 588 |
0 |
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|a Online resource; title from PDF title page (SpringerLink, viewed November 23, 2022).
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| 505 |
0 |
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|a Intro -- Preface -- Organization -- Contents -- An Agent-Based Model to Investigate Different Behaviours in a Crowd Simulation -- 1 Introduction -- 2 The Mathematical Model -- 3 NetLogo Model -- 4 Experimental Results -- 5 Conclusions and Future Works -- References -- Accelerating Evolutionary Neural Architecture Search for Remaining Useful Life Prediction -- 1 Introduction -- 2 Background -- 3 Method -- 3.1 Multi-objective Optimization -- 3.2 Speeding up Evaluation -- 4 Experimental Setup -- 4.1 Computational Setup and Benchmark Dataset -- 4.2 Data Preparation and Training Details
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| 505 |
8 |
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|a 5 Results -- 6 Conclusions -- References -- ACOCaRS: Ant Colony Optimization Algorithm for Traveling Car Renter Problem -- 1 Introduction -- 2 Related Work -- 3 Problem Description -- 4 ACOCaRS Algorithm -- 5 Experiment -- 5.1 Testbed -- 5.2 Results -- 6 Discussion -- 7 Conclusion and Future Work -- References -- A New Type of Anomaly Detection Problem in Dynamic Graphs: An Ant Colony Optimization Approach -- 1 Introduction -- 2 Anomaly Detection Problem -- 3 Proposed Approach -- 4 Numerical Experiments -- 4.1 Benchmarks -- 4.2 Parameter Setting -- 4.3 Anomaly Detection in Real-World Networks
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| 505 |
8 |
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|a 5 Conclusion and Further Work -- References -- .28em plus .1em minus .1emCSS-A Cheap-Surrogate-Based Selection Operator for Multi-objective Optimization -- 1 Introduction -- 2 Background -- 2.1 Spherical Search -- 2.2 Cheap Surrogate Selection (CSS) -- 3 Proposed Method -- 3.1 General Framework of CSS-MOEA -- 3.2 The Detailed Process of CSS-MOEA -- 4 Experiment Results -- 5 Conclusion -- References -- Empirical Similarity Measure for Metaheuristics -- 1 Introduction -- 2 Related Works -- 3 Preliminaries -- 3.1 Metaheuristic Algorithms -- 3.2 Benchmark Functions -- 3.3 Parameter Tuning
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| 505 |
8 |
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|a 4 Proposed Comparison Method -- 4.1 Algorithm Instances -- 4.2 Algorithm Profiling -- 4.3 Measuring Similarity -- 5 Results -- 5.1 Comparing Instances of the Same Algorithm -- 5.2 Comparing Instances of the Same Tuning Function -- 5.3 Clustering the Algorithms' Instances Based on Similarity -- 5.4 Discussion -- 6 Conclusion -- References -- Evaluation of Parallel Hierarchical Differential Evolution for Min-Max Optimization Problems Using SciPy -- 1 Introduction -- 2 Definition of the Problem -- 3 Differential Evolution for MinMax Problems -- 3.1 Overview of Differential Evolution
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| 505 |
8 |
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|a 3.2 Hierarchical (Nested) Differential Evolution and Parallel Model -- 4 Experimental Setup and Results -- 4.1 Benchmark Test Functions -- 4.2 Parameter Settings -- 4.3 Results and Discussion -- 5 Conclusion and Future Work -- References -- Explaining Differential Evolution Performance Through Problem Landscape Characteristics -- 1 Introduction -- 2 Related Work -- 3 Experimental Setup -- 3.1 Benchmark Problem Portfolio -- 3.2 Landscape Data -- 3.3 Algorithm Portfolio -- 3.4 Performance Data -- 3.5 Regression Models -- 3.6 Leave-One Instance Out Validation -- 3.7 SHAP Explanations
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| 650 |
|
0 |
|a Natural computation
|v Congresses.
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| 650 |
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0 |
|a Mathematical optimization
|v Congresses.
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| 650 |
|
7 |
|a Mathematical optimization
|2 fast
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| 650 |
|
7 |
|a Natural computation
|2 fast
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| 655 |
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7 |
|a proceedings (reports)
|2 aat
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| 655 |
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7 |
|a Conference papers and proceedings
|2 fast
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| 655 |
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7 |
|a Conference papers and proceedings.
|2 lcgft
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| 655 |
|
7 |
|a Actes de congrès.
|2 rvmgf
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| 700 |
1 |
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|a Mernik, Marjan,
|d 1964-
|e editor.
|1 https://id.oclc.org/worldcat/entity/E39PBJwHfG3gxYyDBBD7Bwqmh3
|
| 700 |
1 |
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|a Eftimov, Tome,
|e editor.
|
| 700 |
1 |
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|a Črepinšek, Matej,
|e editor.
|
| 776 |
0 |
8 |
|c Original
|z 303121093X
|z 9783031210938
|w (OCoLC)1347430700
|
| 830 |
|
0 |
|a Lecture notes in computer science ;
|v 13627.
|
| 856 |
4 |
0 |
|u https://holycross.idm.oclc.org/login?auth=cas&url=https://link.springer.com/10.1007/978-3-031-21094-5
|y Click for online access
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| 903 |
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|a SPRING-COMP2022
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| 994 |
|
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|a 92
|b HCD
|