Genetic Algorithms: Difference between revisions
Mr. MacKenty (talk | contribs) No edit summary |
Mr. MacKenty (talk | contribs) No edit summary |
||
| Line 3: | Line 3: | ||
In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). 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<ref>https://en.wikipedia.org/wiki/Genetic_algorith</ref> | In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). 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<ref>https://en.wikipedia.org/wiki/Genetic_algorith</ref> | ||
Please first review the characteristics of [[Algorithms|algorithms]]. | * Please first review the characteristics of [[Algorithms|algorithms]]. | ||
* Please then review the characteristics of [[Heuristics|heuristics]]. | |||
== Start here to understand genetic algorithms == | == Start here to understand genetic algorithms == | ||
Revision as of 08:44, 29 November 2021
Advanced programming[1]
In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). 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[2]
- Please first review the characteristics of algorithms.
- Please then review the characteristics of heuristics.
Start here to understand genetic algorithms
- Brute force approach
- Combinatorial optimization
- Computational intractability
- Convergence
- Crossover / crossover operator
- Elitism
- Exploration vs exploitation
- Fitness / fitness function / fitness landscape
- Heuristic
- Hill climbing
- Initialization parameters
- Local extrema
- Mating pool
- Mutation / mutation rate
- Novelty search
- Offspring
- Optimization
- Population
- Premature convergence
- Problem space
- Ranking
- Roulette wheel selection
- Selection strategy
- Simulated annealing
- Stochastic universal sampling
- Termination condition
- Tour
- Tournament selection
- Truncation selection