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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Other Authors: Caraffini, Fabio (Editor), Santucci, Valentino (Editor), Milani, Alfredo (Editor)
Format: Electronic Book Chapter
Language:English
Published: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2020
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DOAB: description of the publication
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245 1 0 |a Evolutionary Computation & Swarm Intelligence 
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520 |a 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. 
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546 |a English 
650 7 |a Information technology industries  |2 bicssc 
653 |a dynamic stream clustering 
653 |a online clustering 
653 |a metaheuristics 
653 |a optimisation 
653 |a population based algorithms 
653 |a density based clustering 
653 |a k-means centroid 
653 |a concept drift 
653 |a concept evolution 
653 |a imbalanced data 
653 |a screening criteria 
653 |a DE-MPFSC algorithm 
653 |a Markov process 
653 |a entanglement degree 
653 |a data integration 
653 |a PSO 
653 |a robot 
653 |a manipulator 
653 |a analysis 
653 |a kinematic parameters 
653 |a identification 
653 |a approximate matching 
653 |a context-triggered piecewise hashing 
653 |a edit distance 
653 |a fuzzy hashing 
653 |a LZJD 
653 |a multi-thread programming 
653 |a sdhash 
653 |a signatures 
653 |a similarity detection 
653 |a ssdeep 
653 |a maximum k-coverage 
653 |a redundant representation 
653 |a normalization 
653 |a genetic algorithm 
653 |a hybrid algorithms 
653 |a memetic algorithms 
653 |a particle swarm 
653 |a multi-objective deterministic optimization, derivative-free 
653 |a global/local optimization 
653 |a simulation-based design optimization 
653 |a wireless sensor networks 
653 |a routing 
653 |a Swarm Intelligence 
653 |a Particle Swarm Optimization 
653 |a Social Network Optimization 
653 |a compact optimization 
653 |a discrete optimization 
653 |a large-scale optimization 
653 |a one billion variables 
653 |a evolutionary algorithms 
653 |a estimation distribution algorithms 
653 |a algorithmic design 
653 |a metaheuristic optimisation 
653 |a evolutionary computation 
653 |a swarm intelligence 
653 |a memetic computing 
653 |a parameter tuning 
653 |a fitness trend 
653 |a Wilcoxon rank-sum 
653 |a Holm-Bonferroni 
653 |a benchmark suite 
653 |a data sampling 
653 |a feature selection 
653 |a instance weighting 
653 |a nature-inspired algorithms 
653 |a meta-heuristic algorithms 
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