Genetic Algorithms: Difference between revisions
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* [[Exploration vs exploitation]] | * [[Exploration vs exploitation]] | ||
* [[Fitness / fitness function / fitness landscape]] | * [[Fitness / fitness function / fitness landscape]] | ||
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* [[Hill climbing]] | * [[Hill climbing]] | ||
* [[Initialization parameters]] | * [[Initialization parameters]] | ||
Revision as of 09:46, 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
- Heuristics
- 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