Evolutionary Computation & Swarm Intelligence

Edited by
November 2020
286 pages
  • ISBN978-3-03943-454-1 (Hardback)
  • ISBN978-3-03943-455-8 (PDF)

This book is a reprint of the Special Issue Evolutionary Computation & Swarm Intelligence that was published in

Computer Science & Mathematics
Physical Sciences
Public Health & Healthcare
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.
  • Hardback
License and Copyright
© 2021 by the authors; CC BY license
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

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