Introduction to the Genetic Algorithm
Learn the fundamentals of genetic algorithms and how they optimize hyperparameters in machine learning models by mimicking natural selection. This lesson explains the five key phases: initialization, fitness assignment, selection, crossover, and termination. Discover how genetic algorithms provide a global search capability and handle high-dimensional parameter spaces to improve model performance over traditional methods.
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What is the genetic algorithm?
The genetic algorithm is a heuristic search algorithm that is inspired by Charles Darwnin’s theory of natural evolution. This algorithm reflects the process of natural selection, where the fittest individuals are selected for reproduction to produce the offspring of the next generation. The same idea is also implemented for hyperparameter optimization to find the values of different hyperparameters that can produce the best-performing ML model.
Genetic algorithms are widely used for solving optimization problems such as:
Finding the optimal configuration of parameters or variables in engineering design
Optimizing financial portfolios
Aligning sequences, predicting protein structures, and selecting optimal genes in bioinformatics ...