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Genetic algorithm

A genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA) in computer science and operations research. Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems via biologically inspired operators such as…

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Overview

Methodology

The building block hypothesis

Limitations

Variants

Problem domains

History

Related techniques

Tutorials

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Map overview Semantic statistics

Genetic algorithm

Nodes141
Edges140
Triples174
Avg. degree1.99
Density0.014184
Components1

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Genetic algorithm

Top relations

related to history · 47
Genetic algorithm → Adaptation, Advanced Study, Alan Turing, Alex Fraser, Although Barricelli, Another, Artificial Systems, Australian, Bremermann's, Burnell, Computer, Crosby, Evolutionary, Fogel, Fraser, Fraser's, From, GAs, Genetic, Genetic Algorithms
related to Tutorials · 27
Genetic algorithm → An, Application Archived, Computer, Darrell Whitley Computer Science, Department Colorado State University, Essentials, Free, GA, GAs, Genetic Algorithm Tutorial, Genetic Algorithms, Genetic Algorithms Uses, Global Optimization Algorithms, Interactive, John Holland, Learn, Metaheuristics, Prisoner's DilemmaAn, Python, Python Tutorial
related to Limitations · 23
Genetic algorithm → Again, Alternative, Another, As, Diversity, Finding, For, GA, GAs, Gaussian, Genetic, Hence, However, In, It, No Free Lunch, Operating, Repeated, Several, That
related to Adaptive GAs · 14
Genetic algorithm → AGA, AGAs, Examples, GA, Genetic, In AGA, In CAGA, Instead, LIGA, Recent, Researchers, Successive, The, There
related to Chromosome representation · 12
Genetic algorithm → Crossover, Different, For, Gray, Hamming, In, John Henry Holland, Other, The, This, Typically, When
related to Commercial products · 11
Genetic algorithm → Axcelis, Evolver, GA, General Electric, In, Inc, John Markoff, MATLAB, Palisade, Since, The New York Times
related to Problem domains · 7
Genetic algorithm → As, GAs, Genetic, Markov, Mutation, Observe, Problems
related to Resources · 5
Genetic algorithm → Evolutionary Algorithms, Flavors, History, Overview, Provides
related to Optimization problems · 4
Genetic algorithm → Commonly, Each, In, The
related to Parallel implementations · 4
Genetic algorithm → Coarse-grained, Fine-grained, Other, Parallel

Important terminology Word statistics

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Important terminology

genetic algorithms population fitness algorithm problems mutation optimization solutions solution may crossover ga evolutionary problem selection evolution search function also

Entity relationships Subject–Predicate–Object triples

SubjectPredicateObjectConfidenceSrc
selectioninstance ofGenetic algorithms are commonly used to generate high-quality solutions to optimization and search problems via biologically inspired operators0.80text
crossoverinstance ofGenetic algorithms are commonly used to generate high-quality solutions to optimization and search problems via biologically inspired operators0.80text
and mutationinstance ofGenetic algorithms are commonly used to generate high-quality solutions to optimization and search problems via biologically inspired operators0.80text
the mutation probabilityinstance ofthat support the importance of mutation-based search.It is worth tuning parameters0.80text
crossover probabilityinstance ofthat support the importance of mutation-based search.It is worth tuning parameters0.80text
population size to find reasonable settings for the problem's complexity class being worked oninstance ofthat support the importance of mutation-based search.It is worth tuning parameters0.80text
structural optimization problemsinstance ofIn real world problems0.80text
a single function evaluation may require several hours to several days of complete simulationinstance ofIn real world problems0.80text
designing an engineinstance ofThis makes it extremely difficult to use the technique on problems0.80text
a house or a planeinstance ofThis makes it extremely difficult to use the technique on problems0.80text
combining information from multiple parents.Estimation of Distribution Algorithminstance ofand can include other variation operations0.80text
Genetic algorithmrelated to Adaptive GAsGenetic0.60section

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