Simulated Annealing vs Genetic Algorithms

What are the main differences between simulated annealing and genetic algorithms?

Simulated annealing is inspired by the cooling process of metals, while genetic algorithms mimic natural selection. Both are optimization techniques but differ in their approach.

Can you explain how simulated annealing works?

Sure! Simulated annealing starts with a random solution and iteratively makes small changes, accepting worse solutions with a probability that decreases over time, mimicking the cooling process.

What about genetic algorithms? How do they evolve solutions?

Genetic algorithms use a population of solutions that evolve over generations. They apply selection, crossover, and mutation to create new solutions, favoring those that perform better.

Which method is better for optimization problems?

It depends on the problem. Simulated annealing is often better for single-objective problems, while genetic algorithms excel in multi-objective scenarios.

Are there any real-world applications for these algorithms?

Yes, both are used in various fields like logistics, scheduling, and machine learning for optimizing complex problems.