K. Sastry, D. Goldberg and G. Kendall. (2006). Genetic algorithms. In: E.K. Burke and G. Kendall (eds.) (2005). Introductory Tutorials in Optimisation, Decision Support and Search Methodology. ISBN: 0387234608, Springer. Chapter 4, 97-125. Home/Search Document Not in Database Summary Related Articles Check |
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....of noise. Empirical research by Nissen and Propach [11] seems to support this idea. However, little is known as to where exactly the benefits of distributed populations in noisy environments stem from. In the realm of genetic algorithms (GAs) some results have been found by Miller and Goldberg [10] and by Rattray and Shapiro [13] Based on the building block hypothesis, Miller and Goldberg analyzed the effects of noise on different selection mechanisms for GAs. Rattray and Shapiro investigated the effects of noise on GA performance on the OneMax function and on a perceptron learning problem ....
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....in the open society context we are discussing here. Other examples of social theory transformations are the game theory [22] which has been applied to agent societies, e.g. by Rosenschein and Zlotkin [26] and the genetic theory of natural selection [10] which has resulted in genetic algorithms [12]. When applying social theories and models to artificial agent societies there are some things that one should bear in mind. For instance, great care should be taken when deciding whether to make a complete or only a partial transformation of the social theory. Some parts of the social theory may ....
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....the ESS can be seen as a collection of evolved successful strategies. It is possible to simulate a game through a process of two crucial steps: mutation (changes in the ways agents act) and selection (choice of the preferred strategies) Different kinds of evolutionary computations (see e.g. [11], 12] have been applied within the MAS society, but the similarities to biology are restricted. In section 5 we introduce noise and the agents become uncertain about the outcome of the game, even if they have complete knowledge about the context. Firstly, EC, use a fitness function instead ....
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....specific. The simple GA or binary GA is a good tool for studying the theory of GA, but more and more modern applications of GA are hybrids. 3. 1 Genetic Algorithm Genetic Algorithm (GA) is a search algorithm that borrows its operators from the biologic model of natural selection and evolution [Gol89]. GA have been very successful in combinatorially explosive problems such as; Job Shop Scheduling [LHM87, SD85, WS89, CS89] Non linear Transportation Model [MJxx, MJB90, MVHxx] and the Traveling Salesman Problem [GR85, OSH87, GGRV85] PSD89, LHRP90, Lid91, WSD89] The problem of mission ....
....MJB90, MVHxx] and the Traveling Salesman Problem [GR85, OSH87, GGRV85] PSD89, LHRP90, Lid91, WSD89] The problem of mission planning for large numbers of airborne platforms contains elements of all three of these classic GA problems. GA differ from traditional optimization and search algorithms [Gol89, pp 7]: Genetic Algorithm Traditional Optimization and Search work with coding of the parameters manipulate parameters directly search from a population of points use a single point to search use payoff (objective function) information use derivatives or auxiliary knowledge use probabilistic ....
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