Genetic Programming for Classification
نویسنده
چکیده
This paper presents an approach for designing classifiers for a multiclass problem using Genetic Programming (GP). GP can discover relationships among observed data and express them mathematically. The proposed approach takes an integrated view of all classes when GP evolves. For a multiclass problem, an individual of the population will contain tree for every class. The GP is trained with a set of N training samples in step-wise manner, increasing the number of training samples in steps. Fitness of each individual is calculated during and after step-wise learning. According to this fitness, individuals are selected for different genetic operations. Once the individual is selected, it’s trees are selected for genetic operations on the basis of unfitness. Weak trees having poor performance are given more chance to participate in the genetic operations, and thus improve themselves. In this context, a new mutation operation called nondestructive directed point mutation is used, which reduces the destructive nature of mutation operation. The approach is being demonstrated by experimenting on some datasets.
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تاریخ انتشار 2014