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optimization problem solving with auctions in Cultural Algorithms

MEMETIC COMPUTING 2013年第2期5卷 83-94页

作者： Reynolds, Robert G. Kinnaird-Heether, Leonard Wayne State Univ Dept Comp Sci Detroit MI 48202 USA

In this paper we investigate the role that the competition among knowledge sources plays in the problem solving behavior of a population as the complexity of the optimization problem solving landscapes increases. We employ a type of game theoretic mechanism, auctions, in our study. Our main goal is to determine whether it matters if knowledge sources competed for individuals using auction mechanisms or weighted majority win mechanisms as the landscape complexity increased. The weighted majority win situation allows the Knowledge Sources to make predictions about future success whereas the Auction mechanism allows them to invest in their future by using tokens earned from recent performance. This latter approach allows contextual Knowledge Sources such as Situational Knowledge to play a larger role. Although the results are preliminary, it appears that the auction mechanism is more efficient when solving a problem in situations where contextual information is available. In that case, it is easier for the knowledge sources to make judicial bids or investments. However, once the landscapes become chaotic there is less contextual information available and correspondingly little advantage to the auction mechanism over the weighted majority win situation in terms of the number of generations needed to achieve a solution.

关键词： Cultural Algorithms Game theory Auction mechanisms Evolutionary computation problem complexity Social networks optimization problem solving

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Population Mechanics and Cultural Algorithms in the Development of a Cultural Engine

Population Mechanics and Cultural Algorithms in the Developm...

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Reynolds, Robert G. Kinnaird-Heether, Leonard Wayne State Univ Dept Comp Sci Detroit MI 48202 USA

ISBN: (纸本)9781538627266

Cultural Algorithms provide a meta-heuristic framework for the application of various solution mechanism represented by Knowledge Sources in the Belief Space to the evolution of complex social networks in the population space. One of the key components in the configuration of a Cultural Algorithm is the knowledge distribution mechanism, the means with which the influence of a solution approach is spread out among individuals in individual subnetworks. In this paper a new method of Knowledge distribution called the population mechanism is introduced and its performance compared in complex real-valued functional landscapes. While capable of generating solutions to the entire range of problem, from fixed to chaotic, they are best utilized for fixed problems containing of low to high variability but begin to lose their edge with increasing periodicity. It is proposed that the population mechanism when paired with other previous forms of knowledge distribution will be able to provide focus in specific areas of search and provide a framework with which to provide metrics to build a Cultural Engine.

关键词： Cultural Algorithms Metaheuristics population mechanism evolutionary computation problem complexity social networks optimization problem solving

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Population Mechanics and Cultural Algorithms in the Development of a Cultural Engine

Population Mechanics and Cultural Algorithms in the Developm...

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IEEE Symposium Series on Computational Intelligence

作者： Robert G. Reynolds Leonard Kinnaird-Heether Department of Computer Science Wayne State University Detroit MI

关键词： Cultural Algorithms Metaheuristics population mechanism Evolutionary computation problem complexity Social networks optimization problem solving

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