Evolutionary Computation & Swarm Intelligence
Caraffini, Fabio
Evolutionary Computation & Swarm Intelligence - Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2020 - 1 electronic resource (286 p.)
Open Access
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.
Creative Commons
English
books978-3-03943-455-8 9783039434541 9783039434558
10.3390/books978-3-03943-455-8 doi
Information technology industries
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
Evolutionary Computation & Swarm Intelligence - Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2020 - 1 electronic resource (286 p.)
Open Access
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.
Creative Commons
English
books978-3-03943-455-8 9783039434541 9783039434558
10.3390/books978-3-03943-455-8 doi
Information technology industries
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