Neural Networks, Genetic Algorithms, Probability Algorithms, and Simulated Annealing Algorithms

MATLAB's core algorithm suite includes Neural Networks, Genetic Algorithms, Probability Algorithms, and Simulated Annealing Algorithms, each extensively applied across diverse problem-solving domains. Neural Networks mimic the interconnected structure of biological neurons to handle complex pattern recognition and prediction tasks - typically implemented using MATLAB's Neural Network Toolbox with functions like feedforwardnet() for architecture creation and train() for model optimization. Genetic Algorithms simulate natural selection processes to explore optimal solutions, commonly utilizing MATLAB's Global Optimization Toolbox with ga() for population initialization, crossover, and mutation operations. Probability Algorithms employ statistical models and probabilistic methods for data modeling and analysis, often leveraging built-in functions like fitdist() for distribution fitting and random() for stochastic sampling. Simulated Annealing Algorithms optimize solutions by emulating metallurgical annealing principles, implemented through functions such as simulannealbnd() for temperature-controlled iterative improvement. These algorithm implementations provide robust methodologies and tools for scientific computing and engineering applications.