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The imbalanced data sets are often encountered in business, industry and real life applications. In this paper, the novel fitness function in genetic algorithms to optimize neural networks is proposed for solving the classification problems in imbalanced data sets. Not only the parameters of neural networks but also the links-pruning between neurons are regarded as an optimization problem in this...
Throughout the past decade, the discovery of turbo codes has been considered as one of the most successful researches whose impact significantly enhanced the quality of modern communication systems. To obtain an excellent performance, it is necessary to design robust turbo code interleaver. In the recent time, there were several attempts to apply genetic algorithms to improve the performance of turbo...
This paper presents a new evolutionary method for the cluster validation index (CVI), namely eCVI. The proposed method learns CVI from the generated training data set using the genetic programming (GP), and then outputs the optimal number of clusters after taking parameters of a test data set into the learned CVI. Each chromosome encodes a possible CVI as a function of the number of clusters, density...
This paper describes the use of soft computing based techniques toward the acquisition of adaptive behaviors to be used in mobile exploration by cooperating robots. Navigation within unknown environments and the obtaining of dynamic behavior require some method of unsupervised learning given the impossibility of programming strategies to follow for each individual case and for every possible situation...
A self-learning model based on genetic algorithms is put forward with application to path tracking in computer generated forces (CGF). On the basis of agent, the model is constructed to improve the autonomous performance of CGF entities under path tracking environments. First, the framework of the proposed self-learning model is presented. Second, it elaborates the realization, including the principles...
In this paper, a new evolutionary algorithm (EA) to solve multi-objective constrained optimization problem (MCOP) is proposed. First, the rank of the individual and the scalar constraint violation of the individual are defined. Then, based on the rank and the scalar constraint violation of the individual, a new fitness function and a switch selection operator are presented. Accordingly, when the individuals...
We evolve a neural network controller for a boat that learns to maintain a given bearing and range with respect to a moving target in the Lagoon 3D game environment. Simulating realistic physics makes maneuvering boats difficult and thus makes an evolutionary approach an attractive alternative to hand coded methods. We evolve the weights of simple recurrent neural networks trained with a fitness function...
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