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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| 格式: | Online |
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| 語言: | 英语 |
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MDPI - Multidisciplinary Digital Publishing Institute
2021
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| 在線閱讀: | ONIX_20210501_9783039434541_1085 |
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| _version_ | 1869516143147876352 |
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| collection | Directory of Open Access Books |
| description | 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. |
| format | Online |
| id | doab-20.500.12854ir-69339 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-693392024-03-30T12:51:02Z Evolutionary Computation & Swarm Intelligence Caraffini, Fabio Santucci, Valentino Milani, Alfredo 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 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. 2021-05-01T15:47:05Z 2021-05-01T15:47:05Z 2020 book ONIX_20210501_9783039434541_1085 9783039434541 9783039434558 https://directory.doabooks.org/handle/20.500.12854/69339 eng application/octet-stream Attribution 4.0 International https://mdpi.com/books/pdfview/book/3131 https://mdpi.com/books/pdfview/book/3131 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-03943-455-8 10.3390/books978-3-03943-455-8 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783039434541 9783039434558 286 Basel, Switzerland open access |
| spellingShingle | 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 Evolutionary Computation & Swarm Intelligence |
| title | Evolutionary Computation & Swarm Intelligence |
| title_full | Evolutionary Computation & Swarm Intelligence |
| title_fullStr | Evolutionary Computation & Swarm Intelligence |
| title_full_unstemmed | Evolutionary Computation & Swarm Intelligence |
| title_short | Evolutionary Computation & Swarm Intelligence |
| title_sort | evolutionary computation swarm intelligence |
| topic | 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 |
| topic_facet | 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 |
| url | ONIX_20210501_9783039434541_1085 |