Genetic algorithms: Difference between revisions
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A genetic algorithm is a meta-heuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms<ref name=WPEA>Wikipedia has details [[Wikipedia:Evolutionary algorithm|about evolutionary algorithms]].</ref>. Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators such as mutation, crossover and selection. Some examples of their applications include optimizing decision trees for better performance, solving sudoku puzzles, hyperparameter optimization, and causal inference. | A genetic algorithm is a meta-heuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms<ref name=WPEA>Wikipedia has details [[Wikipedia:Evolutionary algorithm|about evolutionary algorithms]].</ref>. Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators such as mutation, crossover and selection. Some examples of their applications include optimizing decision trees for better performance, solving sudoku puzzles, hyperparameter optimization, and causal inference. | ||
Wikipedia has a lot more information on genetic algorithms.<ref name=WPGA>Wikipedia has much more detail [[Wikipedia:Genetic algorithm|on genetic algorithms]].</ref> | |||
==Attribution== | ==Attribution== | ||
Latest revision as of 10:40, 14 September 2024
In computer science and operations research, genetic algorithms or GAs view learning as a competition among a population of evolving candidate problem solutions. A 'fitness' function estimates each solution for deciding whether it will contribute to the next generation of solutions or not. After that, as in gene transfer in sexual reproduction, the algorithm creates a new population of candidate solutions.
A genetic algorithm is a meta-heuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms[1]. Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators such as mutation, crossover and selection. Some examples of their applications include optimizing decision trees for better performance, solving sudoku puzzles, hyperparameter optimization, and causal inference.
Wikipedia has a lot more information on genetic algorithms.[2]
Attribution
- Some content on this page may previously have appeared on Wikipedia.
Footnotes
- ↑ Wikipedia has details about evolutionary algorithms.
- ↑ Wikipedia has much more detail on genetic algorithms.