What are genetic algorithms and how do they work in optimization?
Genetic algorithms are search heuristics inspired by natural selection. They are used to find optimal solutions by evolving a population of candidate solutions over generations.
Can you explain the steps involved in a genetic algorithm?
Sure! The main steps are: initialization, selection, crossover, mutation, and evaluation. These steps help evolve solutions towards optimality.
What types of problems can genetic algorithms solve?
Genetic algorithms can solve various optimization problems, including scheduling, routing, and resource allocation, among others.
Are there any limitations to using genetic algorithms?
Yes, some limitations include convergence to local optima, high computational cost, and the need for careful parameter tuning.
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