TY - BOOK ID - 137163877 TI - Evolutionary Computation & Swarm Intelligence AU - Caraffini, Fabio AU - Santucci, Valentino AU - Milani, Alfredo PY - 2020 PB - Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute DB - UniCat KW - dynamic stream clustering KW - online clustering KW - metaheuristics KW - optimisation KW - population based algorithms KW - density based clustering KW - k-means centroid KW - concept drift KW - concept evolution KW - imbalanced data KW - screening criteria KW - DE-MPFSC algorithm KW - Markov process KW - entanglement degree KW - data integration KW - PSO KW - robot KW - manipulator KW - analysis KW - kinematic parameters KW - identification KW - approximate matching KW - context-triggered piecewise hashing KW - edit distance KW - fuzzy hashing KW - LZJD KW - multi-thread programming KW - sdhash KW - signatures KW - similarity detection KW - ssdeep KW - maximum k-coverage KW - redundant representation KW - normalization KW - genetic algorithm KW - hybrid algorithms KW - memetic algorithms KW - particle swarm KW - multi-objective deterministic optimization, derivative-free KW - global/local optimization KW - simulation-based design optimization KW - wireless sensor networks KW - routing KW - Swarm Intelligence KW - Particle Swarm Optimization KW - Social Network Optimization KW - compact optimization KW - discrete optimization KW - large-scale optimization KW - one billion variables KW - evolutionary algorithms KW - estimation distribution algorithms KW - algorithmic design KW - metaheuristic optimisation KW - evolutionary computation KW - swarm intelligence KW - memetic computing KW - parameter tuning KW - fitness trend KW - Wilcoxon rank-sum KW - Holm–Bonferroni KW - benchmark suite KW - data sampling KW - feature selection KW - instance weighting KW - nature-inspired algorithms KW - meta-heuristic algorithms UR - https://www.unicat.be/uniCat?func=search&query=sysid:137163877 AB - 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. ER -