Genetic Algorithms vs. Monte Carlo

AI Thread Summary
The discussion focuses on identifying simple problems or projects that can be addressed using both genetic algorithms and Monte Carlo methods, including simulated annealing and Metropolis-Hastings. Participants suggest that circuit optimization problems are suitable for both approaches. Additionally, the Rosenbrock function is mentioned as a specific example, raising the question of whether either method can effectively find its global minimum. The conversation emphasizes the versatility of these algorithms in solving optimization problems.
maverick_starstrider
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Hi, other than the Traveling Salesman Problems can anyone help me think of relatively simple problems/projects that are solvable through BOTH genetic algorithm techniques AND monte-carlo methods (such as simulated annealing and metropolis-hastings). Any help is greatly appreciated.
 
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maverick_starstrider said:
Hi, other than the Traveling Salesman Problems can anyone help me think of relatively simple problems/projects that are solvable through BOTH genetic algorithm techniques AND monte-carlo methods (such as simulated annealing and metropolis-hastings). Any help is greatly appreciated.

You certainly can use both on circuit optimization problems.
 
Find the global minimum of Rosenbrock's function,

f(x,y) = (1-x)^2 + 100\left(y-x^2\right)^2
 
Can either method find a global minimum?
 
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