Genetic Algorithms: Difference between revisions
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Revision as of 10:34, 9 December 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.
The basic pattern of genetic algorithms
- A random set of solutions would be generated on the sample documents
- And tested against the human labelling
- Best fit solutions retained
- New generation created by mutating/crossing
- Algorithm repeated
- Until a good fit obtained
Some videos
Helpful resources
- This article is a good place to get started
- a good game: https://andymakes.itch.io/combat-genetics
- a better game to help you understand genetic algorithms: https://david-birge-cotte.itch.io/evolution-sandbox
Terms associated with 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