Cultural Algorithm Application in Java

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Cultural Algorithm Application
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Cultural algorithms have been applied to solve different problems, with one of the first applications modeling the evolution of agriculture in the Valley of Oaxaca (Mexico) [Reynolds 1979]. Other applications of cultural algorithms include concept learning [Sverdlik et al. 1992], real-valued function optimization [Michalewicz 1994], optimizing semantic networks [Rychtyckyj and Reynolds 1999], software testing [Ostrowski and Reynolds 1999], and assessing the quality of genetic programs [Cowan and Reynolds 1999]. This section overviews one application, i.e. using a CA for real-valued function optimization. Consider finding the minimum of the (fitness) function f ( x 1 , X 2 , . . , X 1 ) , with Xi [ ai,c*2] and ai,a2 G [0, oo]. Assume that an EP is used on the population level: the resulting CA is referred to as a CAEP. The population space consists of N individuals, Cg,i, ..., Cg,N, each of / dimensions, for a specific generation g. The belief space will contain the best individual since evolution, as well as the interval believed to house the minimum point. These will respectively be referred to as the situational knowledge component, Sg, and the normative knowledge component, Ng (for a specific generation g). While Sg contains only one component (the best individual), the normative knowledge component consists of / elements, one for each parameter xi. For each element, Ng,i, a minimum value Nmin and a maximum value Nmax are kept to indicate the range wherein it is believed the best value for that parameter is located. The initial normative knowledge component reflects the initial range constraints placed on the parameters. That is, Nmin
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The influence function is used in conjunction with the mutation operator to generate 10 offspring. In this application, the influence function determines the mutation step size. That is, the variance of the Gaussian distribution function used to determine
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the step size, is a function of the range constraints. Individuals within the range constraints as given by the normative knowledge component have a small mutation variance. Individuals further away from the range constraints have a larger mutation variance, hence larger adjustments. Only the top y% of individuals are accepted to influence the belief space. Let Cg denote the set of parents and offspring. Then, after the new generation has been selected,
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The above illustrates the steps of one generation.
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Cultural evolution is still a young EC paradigm, and still needs much research to mine the potential in improving standard EAs.
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1. Discuss how CE can be used to train a NN. 2. What are the similarities and differences between CE and ES 3. Discuss the validity of the following statement: The belief space used for CE can be likened to a blackboard system.
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Coevolution is the complementary evolution of closely associated species. The coevolution between two species is nicely illustrated using Holland's example of the competitive interaction between a plant and insects [Holland 1990]. Consider a certain species of plant living in an environment containing insects that eat the plant. The survival "game" consists of two parts: (1) to survive, the plant needs to evolve mechanisms to defend itself from the insects, and (2) the insects need the plant as food source to survive. Both the plant and the insects evolve in complexity to obtain characteristics that will enable them to survive. For example, the plant may evolve a tough exterior, but then the insect evolves stronger jaws. Next the plant may evolve a poison to kill the insects. Next generations of the insect evolve an enzyme to digest the poison. The effect of this coevolutionary process is that, with each generation, both the plant and the insects become better at their defensive and offensive roles. In the next generation, each species change in response to the actions of the other species during the previous generation. The biological example described above is an example of predator-prey Coevolution, where there is an inverse fitness interaction between the two species. A win for the one species means a failure for the other. To survive, the "losing" species adapt to counter the "winning" species in order to become the new winner. During this process the complexity of both the predator and the prey increases. An alternative coevolutionary process is symbiosis, in which case the different species cooperate instead of competing. In this case a success in one species improves the survival strength of the other species. Symbiotic Coevolution is thus achieved through a positive fitness feedback among the species that take part in this cooperating process. In standard EAs, evolution is usually viewed as if the population attempts to adapt in a fixed physical environment. In contrast, coevolutionary (CoE) algorithms (CoEA) realize that in natural evolution the physical environment is influenced by other
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