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Content available remote On Patterns in Economic Data and Monetary Councils Decisions
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EN
In the paper an attempt to identify decision rules which emulate policy decisions of monetary councils (councils) in Poland, United States and Japan is presented. Policy decisions are defined as decisions concerning target interest rate changes. Generated decision rules emulate therefore monetary authorities' reaction function or policy rules. Accuracy of these rules is measured by the ratio of instances (decisions) correctly classified to the number of all instances under consideration. Generated rules are meaningful. Most of them can be interpreted in terms of stabilizing policy, which stimulates output or decelerates inflation dependent on the evolution of economic situation in the domestic economy. This is consistent with the goal of monetary policy, which is to stabilize prices and (in case of USA) output fluctuations.
EN
We compare six metaheuristic optimization algorithms applied to solving the travelling salesman problem. We focus on three classical approaches: genetic algorithms, simulated annealing and tabu search, and compare them with three recently developed ones: quantum annealing, particle swarm optimization and harmony search. On top of that we compare all results with those obtained with a greedy 2-opt interchange algorithm. We are interested in short-term performance of the algorithms and use three criteria to evaluate them: solution quality, standard deviation of results and time needed to reach the optimum. Following the results from simulation experiments we conclude that simulated annealing and tabu search outperform newly developed approaches in short simulation runs with respect to all three criteria. Simulated annealing finds best solutions, yet tabu search has lower variance of results and converges faster.
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