List of artificial intelligence algorithms
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This is a list of artificial intelligence algorithms, including algorithms and algorithmic methods used in artificial intelligence (AI) for search, automated reasoning, knowledge representation and reasoning, planning, machine learning, deep learning, natural language processing, computer vision, and related areas.[1]
Search and optimization
[edit]- A* search algorithm
- Alpha–beta pruning
- Beam search
- Beam stack search
- Best-first search
- Breadth-first search
- Depth-first search
- Expectiminimax
- General Problem Solver
- Hill climbing
- Iterative deepening A*
- Iterative deepening depth-first search
- Minmax algorithm
- Monte Carlo tree search
- Simulated annealing
- SSS*
- Uniform-cost search
Evolutionary computation and bio-inspired methods
[edit]Automated reasoning and logic
[edit]Probabilistic reasoning and uncertain inference
[edit]Motion planning and decision-making
[edit]Machine learning and statistical classification
[edit]- AdaBoost
- Almeida–Pineda recurrent backpropagation
- ALOPEX
- Alternating decision tree
- Apriori algorithm
- Bayesian optimization
- Bootstrap aggregating
- BrownBoost
- C4.5 algorithm
- CN2 algorithm
- Constructing skill trees
- DBSCAN
- Decision tree learning
- Diffusion map
- Dominance-based rough set approach
- Dynamic time warping
- Eclat algorithm
- Error-driven learning
- Evolutionary multimodal optimization
- FastICA
- FP-growth algorithm
- GeneRec
- Genetic Algorithm for Rule Set Production
- Growing self-organizing map
- ID3 algorithm
- IDistance
- k-means++
- k-means clustering
- k-medoids
- k-nearest neighbors algorithm
- Kernel principal component analysis
- Learning vector quantization
- Leabra
- Linde–Buzo–Gray algorithm
- Lloyd's algorithm
- Local outlier factor
- Logic learning machine
- LogitBoost
- LPBoost
- Manifold alignment
- Markov chain Monte Carlo (MCMC)
- Minimum redundancy feature selection
- Naive Bayes classifier
- Non-negative matrix factorization
- OPTICS
- Prefrontal cortex basal ganglia working memory
- PVLV
- Quadratic unconstrained binary optimization
- Quickprop
- Random forest
- Randomized weighted majority algorithm
- Relevance vector machine
- Repeated incremental pruning to produce error reduction (RIPPER)
- Rprop
- Self-organizing map
- Skill chaining
- Sparse PCA
- Stochastic gradient descent
- Structured kNN
- Support vector machine
- T-distributed stochastic neighbor embedding
- Weighted majority algorithm (machine learning)
- Winnow algorithm[2]
Neural networks and deep learning
[edit]Reinforcement learning
[edit]Natural language processing
[edit]Computer vision and perception
[edit]Algorithmic game play
[edit]See also
[edit]
Wikibooks has a book on the topic of: Algorithms
- Glossary of artificial intelligence
- List of algorithms
- List of artificial intelligence journals
- List of quantum algorithms
- Outline of algorithms
- Outline of artificial intelligence
- Lists of open-source artificial intelligence software
- TurboQuant – online vector quantization algorithm for compressing high-dimensional vectors in large language model inference
- AlphaDev, AlphaEvolve, AlphaTensor — AI systems by Google DeepMind for discovering and optimizing algorithms
References
[edit]- ↑ "Artificial Intelligence (AI) Algorithms". GeeksforGeeks. July 23, 2025. Retrieved April 19, 2026.
- ↑ "Machine Learning Algorithms". GeeksforGeeks. January 20, 2026. Retrieved April 19, 2026.
- ↑ Quesada, Alberto (October 28, 2019). "5 algorithms to train a neural network". Neural Designer Blog. Artelnics. Retrieved April 20, 2026.
- ↑ Silver, David; et al. (January 2016). "Mastering the game of Go with deep neural networks and tree search". Nature. 529 (7587): 484–489. doi:10.1038/nature16961.
- ↑ Silver, David; et al. (October 2017). "Mastering the game of Go without human knowledge". Nature. 550: 354–359. doi:10.1038/nature24270.
- ↑ Silver, David; et al. (December 2018). "A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play". Science. 362 (6419): 1140–1144. doi:10.1126/science.aar6404.
- ↑ Schrittwieser, Julian; et al. (December 2020). "Mastering Atari, Go, chess and shogi by planning with a learned model". Nature. 588: 604–609. arXiv:1911.08265. doi:10.1038/s41586-020-03051-4.
- ↑ Tesauro, Gerald (March 1995). "Temporal difference learning and TD-Gammon". Communications of the ACM. 38 (3): 58–68. doi:10.1145/203330.203343.
