Evolutionary Computation & Swarm Intelligence

• Autore: Caraffini Fabio
• Pubblicazione: Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020
• Descrizione fisica: 1 online resource (286 p.)
• Soggetto topico: Information technology industries
• Soggetto non controllato: algorithmic design; analysis; approximate matching; benchmark suite; compact optimization; concept drift; concept evolution; context-triggered piecewise hashing; data integration; data sampling; DE-MPFSC algorithm; density based clustering; discrete optimization; dynamic stream clustering; edit distance; entanglement degree; estimation distribution algorithms; evolutionary algorithms; evolutionary computation; feature selection; fitness trend; fuzzy hashing; genetic algorithm; global/local optimization; Holm-Bonferroni; hybrid algorithms; identification; imbalanced data; instance weighting; k-means centroid; kinematic parameters; large-scale optimization; LZJD; manipulator; Markov process; maximum k-coverage; memetic algorithms; memetic computing; meta-heuristic algorithms; metaheuristic optimisation; metaheuristics; multi-objective deterministic optimization, derivative-free; multi-thread programming; nature-inspired algorithms; normalization; one billion variables; online clustering; optimisation; parameter tuning; particle swarm; Particle Swarm Optimization; population based algorithms; PSO; redundant representation; robot; routing; screening criteria; sdhash; signatures; similarity detection; simulation-based design optimization; Social Network Optimization; ssdeep; swarm intelligence; Swarm Intelligence; Wilcoxon rank-sum; wireless sensor networks
• Persona (resp. second.): SantucciValentino; MilaniAlfredo; CaraffiniFabio
• Sommario/riassunto: 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.
• Titolo autorizzato: Evolutionary Computation & Swarm Intelligence
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910557283803321
• Lo trovi qui: Univ. Federico II