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Evolutionary Computation

Evolutionary computation offers powerful problem-solving methodologies inspired by models of natural genetics and evolutionary processes. Potential applications are wide-ranging and include problems related to combinatorial optimization, numerical optimization, multi-objective optimization, and othe...

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Gorde:
Formatua: Online
Hizkuntza:ingelesa
Argitaratua: MDPI - Multidisciplinary Digital Publishing Institute 2025
Gaiak:
combinatorial optimization
evolutionary algorithms
fitness distance correlation
fitness landscape analysis
genetic algorithms
mutation
permutation cycles
permutation distance
metaheuristics
differential evolution
stigmergy
CUDA
micro-hyropower plant
layout optimization
Hermite splines
genetic algorithm
feature selection
classification
evolutionary computing
overweight
obesity
target coverage problem
directional sensor networks
discrete particle swarm optimization
fully informed particle swarm
self-adaptive differential evolution
particle swarm optimization
intelligence optimization algorithm
linkage system
vehicle wipers
global optimization
orthogonal search
parameterless
TROS
QROS
cross-validation
genetic programming
interaction location
SuperCDMS
symbolic regression
evolutionary optimization
multitask optimization
interactive
surrogate model
personalized recommendation
optimization
UAVs
wireless networks
potential fields
electric vehicles
charging station allocation
facility-to-site allocation
grid discretization
ERP
services composition
QoS optimization
context-aware
IoT-Fog cloud service ontology
multi-agent strategies
improved NSGA-II algorithm
n/a
thema EDItEUR::M Medicine and Nursing::MJ Clinical and internal medicine::MJC Diseases and disorders::MJCL Oncology
Sarrera elektronikoa:ONIX_20250220_9783725821235_124
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Gaia:Evolutionary computation offers powerful problem-solving methodologies inspired by models of natural genetics and evolutionary processes. Potential applications are wide-ranging and include problems related to combinatorial optimization, numerical optimization, multi-objective optimization, and others, as well as specific applications of these problems in diverse domains, such as engineering, design, medicine, robotics, science, etc. Techniques from evolutionary computation often lend themselves well to parallel and distributed implementations and are often more effective in dealing with challenging problem characteristics such as non-linearity and high dimensionality than alternative approaches. This Special Issue brings together recent advances in the theory and application of evolutionary computation.

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