Genetic Algorithms: A Mature Bio-inspired Optimization Technique for Difficult Problems

DOI10.1007/978-3-030-26458-1_1zbMath1436.90165OpenAlexW2998856153MaRDI QIDQ3296227

Yiannis Kontos, K. L. Katsifarakis

Publication date: 7 July 2020

Published in: Nature-Inspired Methods for Metaheuristics Optimization (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-030-26458-1_1

zbMATH Keywords

optimizationaccuracygenetic algorithmsgenetic operatorsconstraint handlingterminology

Mathematics Subject Classification ID

Approximation methods and heuristics in mathematical programming (90C59)

Uses Software

• Genocop

Cites Work

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• Hybrid crossover operators for real-coded genetic algorithms: an experimental study
• Genetic algorithms: Principles and perpectives. A guide to GA theory
• Minimum penalty for constrained evolutionary optimization
• Global and local real-coded genetic algorithms based on parent-centric crossover operators
• Introduction to Evolutionary Algorithms
• Introduction to Genetic Algorithms

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