What are genetic algorithms?

What are genetic algorithms?

Genetic algorithms are optimization techniques inspired by natural selection. They use processes like selection, crossover, and mutation to evolve solutions to problems.

How do they work in practice?

In practice, genetic algorithms start with a population of potential solutions. They evaluate each solution's fitness, select the best ones, and combine them to create new solutions, repeating this process until an optimal solution is found.

What are some real-world applications?

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

Are there any limitations to using genetic algorithms?

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