Adaptive Learning by Genetic Algorithms: Analytical Results by Herbert Dawid

By Herbert Dawid

This ebook considers the educational habit of Genetic Algorithms in financial platforms with mutual interplay, like markets. Such platforms are characterised by means of a nation established health functionality and for the 1st time mathematical effects characterizing the long term consequence of genetic studying in such structures are supplied. numerous insights about the impression of using diversified genetic operators, coding mechanisms and parameter constellations are received. The usefulness of the derived effects is illustrated by means of a great number of simulations in evolutionary video games and fiscal versions.

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He tournament. Axelrod observed that there were eight special strategies in the second tournament, that could be well used to calculate the payoff of a given strategy in the whole contest. The average 28 2. Bounded Rationality and Artificial Intelligence C C C C C C D C C 0 0 0 fictitious'" history , C C C C D C C C D C D D C D D C D D D D 1 0 0 0 D C D D D D D l~to,y '" action Fig. 8. An example of the Axelrod like encoding of a strategy in the IPD. The action part of the string contains 64 bits payoff against these eight strategies was in many cases very similar to the average payoff against the whole population.

This model will be presented in some detail in chapter 6 and we will extend her results which indicate that the GA learns the stationary equilibria quite easily in cases where no other equilibria exist, by demonstrating that GAs are also able to learn cyclical or stochastic equilibria if the appropriate setup is used. 5 Potentiality and Problems of CI Techniques in Economics The examples of the application of artificially intelligent agents in economic systems as given above are of course only a small part of the rapidly growing literature devoted to this area.

Mutation probabilities are in general of order 10- 3 • Whereas the selection operator reduces the diversity in the population, the mutation operator increases it again. The higher the mutation probability, the smaller is the danger of premature convergence. A high mutation probability will however transform a GA to some kind of random search algorithm, which is of course not the intention of this algorithm. 4 Other Operators Besides the genetic operators presented above, there exist a number of different operators, which are used in different applications.

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