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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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Bibliographic Details
Main Author: Caraffini, Fabio
Other Authors: Santucci, Valentino, Milani, Alfredo
Format: eBook
Language:English
Published: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2020
Subjects:
Fuzzy Hashing
Memetic Algorithms
Screening Criteria
Identification
Multi-objective Deterministic Optimization, Derivative-free
Maximum K-coverage
Concept Evolution
Routing
Genetic Algorithm
Large-scale Optimization
One Billion Variables
Concept Drift
Evolutionary Computation
Wilcoxon Rank-sum
Density Based Clustering
Robot
Signatures
Feature Selection
Particle Swarm
Lzjd
Swarm Intelligence
Pso
Approximate Matching
Compact Optimization
Population Based Algorithms
K-means Centroid
Particle Swarm Optimization
Entanglement Degree
De-mpfsc Algorithm
Imbalanced Data
Data Sampling
Holm-bonferroni
Algorithmic Design
Parameter Tuning
Normalization
Information Technology Industries / Bicssc
Meta-heuristic Algorithms
Edit Distance
Evolutionary Algorithms
Online Clustering
Wireless Sensor Networks
Optimisation
Global/local Optimization
Hybrid Algorithms
Context-triggered Piecewise Hashing
Nature-inspired Algorithms
Social Network Optimization
Fitness Trend
Analysis
Ssdeep
Benchmark Suite
Data Integration
Sdhash
Metaheuristic Optimisation
Redundant Representation
Metaheuristics
Discrete Optimization
Multi-thread Programming
Markov Process
Manipulator
Similarity Detection
Instance Weighting
Memetic Computing
Dynamic Stream Clustering
Kinematic Parameters
Simulation-based Design Optimization
Estimation Distribution Algorithms
Online Access:
Collection: Directory of Open Access Books - Collection details see MPG.ReNa
Description
Summary: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.
Item Description:Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
Physical Description:1 electronic resource (286 p.)
ISBN:9783039434558
books978-3-03943-455-8
9783039434541

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