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By Herbert Dawid

This ebook considers the training habit of Genetic Algorithms in monetary structures with mutual interplay, like markets. Such platforms are characterised by means of a nation based health functionality and for the 1st time mathematical effects characterizing the long term final result of genetic studying in such structures are supplied. a number of insights in regards to the influence of using diversified genetic operators, coding mechanisms and parameter constellations are received. The usefulness of the derived effects is illustrated via a great number of simulations in evolutionary video games and financial types.

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Extra resources for Adaptive Learning by Genetic Algorithms: Analytical Results and Applications to Economical Models

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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). The same holds true if we consider the values of the even cells at even periods.

Ions, where the actually paid price is the mean value of both price expectations. After every period the weights of the networks are updated with the help of observed data. Every T periods the agents may choose a new strategy. The probability that a certain strategy is adopted is proportional to the wealth of the agent who used this strategy previously. Afterwards the strategy is mutated randomly. Simulations show that the dumb agents always disappear quickly. Initially there is a high percentage of smart agents, but as the market stabilizes also naive expectations are good and the naive agents who do not have to face costs for building their expectations may take over the population if the expectation costs of the smart agents are too high.

Similar arguments apply to the problem of the coding of the actions of the agents. There is a lot of evidence in the GA literature showing that different coding procedures may lead to different learning behavior. Again no economic argument can be given to decide the problem, and this must be seen as another drawback (note that we are now talking about the procedure that encodes a given number or strategy or automata in a binary string. The interpretation of this number is another problem and can be decided on grounds of economic arguments as shown above).

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