By Herbert Dawid
This ebook considers the educational habit of Genetic Algorithms in monetary structures with mutual interplay, like markets. Such structures are characterised by means of a kingdom established health functionality and for the 1st time mathematical effects characterizing the long term final result of genetic studying in such platforms are supplied. numerous insights about the effect of using diversified genetic operators, coding mechanisms and parameter constellations are won. The usefulness of the derived effects is illustrated via plenty of simulations in evolutionary video games and monetary versions.
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Extra info for Adaptive Learning by Genetic Algorithms: Analytical Results and Applications to Economical Models
It is however well known that different sets of parameter values may lead to quite different results, which implies that the setting of the parameters is of great importance. Usu- 36 2. Bounded Rationality and Artificial Intelligence ally those parameters are chosen which have been successfully used in static optimization problems, but this seems to be a weak argument, for we are not interested in optimizing a function but in simulating the behavior of a population of adaptive agents. A possible solution to this problem may be to compare computer simulations with human experiments in very simple models, and calibrate the parameters according to the experimental data.
Although, this slogan seems to be slightly tautological in the natural environment, where fitness is defined as the ability to survive, it makes good sense in the world of optimization problems, where the fitness of a string is given as the value of the function to be optimized at the argument encoded by the string. GAs proved to be quite successful in finding good solutions to such complex problems as the travelling salesman problem, the knapsack problem, large scheduling problems, graph partitioning problems, but also for engineering problems like the design of bridge structures or the optimal use of power plants.
Thus adjacent firms set their prices every other period. The decision rule of the firms are all the same and very simple. They consider only the set prices of their two direct neighbors and set a high price whenever both adjacent firms sell at the same price and at the low price if the adjacent firms sell at different prices. The authors argue that this kind of behavior may be implied by diseconomies of scale and the given structure of consumers. If we consider only the odd firms at the odd periods this model describes exactly an elementary cellular automaton with the modulo 2 rule (rule 90).
Adaptive Learning by Genetic Algorithms: Analytical Results and Applications to Economical Models by Herbert Dawid