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Genetic Network Programming(GNP) is a newly developed evolutionary computation method using a directed graph as its gene structure, which is its unique feature. It is competent for dealing with complex problems in dynamic environments and is now being well studied and applied to many real-world problems such as: elevator supervisory control, stock price prediction, traffic volume forecast and data...
When action rules of agents are created by evolutionary computation, it generally aims to create optimal individual which represents optimal rules. On the other hand, genetic network programming (GNP) with rule accumulation extracts a large number of rules throughout the generations and store them in the rule pools. In other words, the individuals of GNP with rule accumulation are regarded as rule...
Most evolutionary computation methods such as GA, GP, EP, ES, etc. mainly focus on obtaining the best solution, namely, the elite individual with the optimal gene structure. In this case, only the final result is taken into account rather than the evolutionary process. We noticed that some good experiences generated during the evolutionary period are also valuable for guiding the evolution or directing...
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