What are genetic algorithms and how are they used in machine learning?

What are genetic algorithms and how are they used in machine learning?

Genetic algorithms are optimization techniques inspired by natural selection. They are used in machine learning to find optimal solutions by evolving a population of candidate solutions over generations.

Can you explain the process of how genetic algorithms work?

Sure! Genetic algorithms typically involve selection, crossover, and mutation. First, a population of solutions is evaluated. The best solutions are selected to create offspring through crossover, and then mutations are introduced to maintain diversity.

What are some real-world applications of genetic algorithms?

Genetic algorithms are used in various fields, including finance for portfolio optimization, engineering for design optimization, and even in game development for creating adaptive AI.

Are there any limitations to using genetic algorithms?

Yes, genetic algorithms can be computationally expensive and may converge to local optima rather than the global optimum. They also require careful tuning of parameters.