AI Search & Optimization Algorithms
AI search and optimization algorithms are fundamental tools in artificial intelligence for solving complex problems efficiently. These algorithms are designed to navigate through large search spaces to find optimal solutions or make informed decisions. They range from uninformed search methods like depth-first search and breadth-first search to informed techniques such as A* search and genetic algorithms. Additionally, optimization algorithms like gradient descent and genetic programming help refine solutions to achieve desired outcomes. These algorithms play a crucial role in problem-solving, decision-making, and optimization tasks across various domains.
- Basics of AI Search Problem
- Types of AI Search Algorithms
- Uninformed Search Algorithms
- Informed Search Algorithms
- Adversarial Search Algorithms
- Uninformed Search Algorithms
- Informed Search Algorithms
- Beam Search
- Greedy Best First Search Algorithm
- A* Search Algorithms
- AO* Search algorithm
- Weighted A* Search Algorithms
- Iterative Deepening A* algorithm (IDA*)
- Memory-bounded search (or Memory Bounded Heuristic Search)
- Bidirectional heuristic search
- Local Search
- Adversarial Search Algorithms
- Basics of Game Theory
- MiniMax Search in Game Theory
- Depth Limited MiniMax
- Iterative Deepening Minimax
- Alpha-beta Pruning
- Monte Carlo Tree Search (MCTS)
- AlphaGo Algorithms
- Multi-Agent Search Algorithms
- Cooperative Search
- Distributed Search
- Competitive Search
- Dynamic Programming Algorithms
- Bellman-Ford Algorithm
- Floyd-Warshall Algorithm
- Viterbi Algorithm
- Dynamic Time Warping (DTW)
- Levenshtein Distance (Edit Distance) Algorithm
- Longest Common Subsequence (LCS) Algorithm
- Forward-Backward Algorithm
- Linear Programming
- Simplex Algorithm
- Interior Point Methods
- Dual Simplex Method
- Optimization Algorithms
- Gradient Descent
- Stochastic Gradient Descent (SGD)
- Newton’s Method
- Conjugate Gradient
- Genetic Programming
- Bayesian Optimization
- Constraint Satisfaction Problems (CSP)
- Arc Consistency
- Backtracking Search
- Forward Checking
- Constraint Propagation
- Hybrid Algorithms:
- Genetic Algorithm with Local Search
- Simulated Annealing with Genetic Algorithm
- Particle Swarm Optimization with Differential Evolution
Artificial Intelligence (AI) Algorithms
Artificial Intelligence (AI) is revolutionizing industries, transforming the way we interact with technology. With a growing interest in mastering AI, we’ve crafted a tutorial on AI algorithms, based on extensive research in the field. This tutorial covers core algorithms that serve as the backbone of artificially intelligent systems.
Contact Us