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For genetic programming algorithms new variants of uniform crossover operators that introduce selective pressure on the recombination stage are proposed. Operators probabilistic rates based approach to GP self-configuration is suggested. Proposed modifications usefulness is demonstrated on benchmark test and real world problems.
The use of evolutionary algorithms in the boolean synthesis is an attractive alternative to generate interesting and efficient hardware structures, with a high computational load. This paper presents the implementation of a parallel genetic programming (PGP) for boolean synthesis on a GPU-CPU based platform. Our implementation uses the island model, that allows the parallel and independent evolution...
The University of North Dakota is developing airspace within the state where Unmanned Aircraft Systems (UASs) can be flown without an onboard sense and avoid system or Temporary Flight Restrictions (TFRs). With funding from the U.S. Air Force, a mobile ground-based radar system capable of detecting aircraft operating in Class E airspace and the software to display such information to UAS operators...
In this paper, a fuzzy model based on genetic programming (GPFM) is proposed to diagnose the fault types of insulation of power transformers. The proposed GPFM algorithm constructs the fuzzy relationship between input and output fuzzy variables by genetic programming algorithms. The parameters of memberships of fuzzy subsets and the fuzzy relationship of system are represented by the GP candidates...
In this paper, programming methods of constructing filters for choosing target images from pathology images are discussed. Automatic construction of these filters would be very useful in the medical field. Image processing filters can be expressed as tree topology operations. Genetic Programming (GP) is an evolutionary computation algorithm that can design tree topology operations. Simulated Annealing...
PSO is a parallel stochastic optimization algorithm with advantages of less parameters and high efficiency. This paper describes the programming problem in the method of two linear tables with discrete and continuous quantity, then uses discrete PSO algorithm to discrete optimization and continuous PSO to optimize continuous quantity in the solving process respectively, based on these proposes the...
For the evolutionary electronic circuit design, the representation of the circuit is important, because the representation of the circuit may affected the significance solution circuit or the optimize solution, and also should speeds up the convergence speed of the algorithm search. The hardware representation methods mainly include binary bit string representation and Cartesian Genetic Programming...
Parameter setting of Evolutionary Algorithms is a time consuming task with two main approaches: parameter tuning and parameter control. In this work we describe a new methodology for tuning parameters of Genetic Programming algorithms using factorial designs, one-factor designs and multiple linear regression. Our experiments show that factorial designs can be used to determine which parameters have...
The prediction of fill levels in stormwater tanks is an important practical problem in water resource management. In this study state-of-the-art CI methods, i.e., Neural Networks (NN) and Genetic Programming (GP), are compared with respect to their applicability to this problem. The performance of both methods crucially depends on their parametrization. We compare different parameter tuning approaches,...
The paper suggests a customized elite based genetic programming technique for the identification of complex nonlinear systems. The models generated by the proposed method are nonlinear, linear in parameters, as the universal approximation capacities of such a mathematical formalism have been rigorously proven. To better exploit the models' parameter wise linearity, the authors propose a memetic approach...
This paper presents a combination of intelligent learning algorithm, the Support Vector Machine, and the recognition of star pattern in Celestial Navigation. Considering the star pattern recognition's character, noticing the advantages of SVM in learning competence, the paper proposes a solution to star pattern recognition with multi-kernel SVM. A multi-kernel algorithm bases on Genetic Programming...
The usability of model-aided decision relies on intellectualized level of model selection. An algorithm of Model selection based sample data is proposed in the paper. The meta-models are classified by characters of the sample data, and the assembled models are built as tree format. The genetic operations are performed under several restrictions to provide the model selection scheme. Its process hardly...
An elitist multiobjective optimization methodology, based on genetic programming, is suggested in the following, as means of identifying complex nonlinear systems. The structure and parameters of the nonlinear models are selected simultaneously as result of the conjoint usage of customized genetic operators and of a deterministic parameter computation procedure. This symbiosis is configured to efficiently...
In pattern detection systems, the general techniques of feature extraction and selection perform linear transformations from primitive feature vectors to new vectors of lower dimensionality. At times, new extracted features might be linear combinations of some primitive features that are not able to provide better classification accuracy. To solve this problem, we propose the integration of genetic...
Recently, it has been shown that synthesis of some circuits is quite difficult for conventional methods. In this paper we present a method of minimization of multi-level logic networks which can solve these difficult circuit instances. The synthesis problem is transformed on the search problem. A search algorithm called Cartesian genetic programming (CGP) is applied to synthesize various difficult...
One of the central areas in network intrusion detection is how to build effective systems that are able to distinguish normal from intrusive traffic. In this paper we explore the use of Genetic Programming (GP) for such a purpose. Although GP has already been studied for this task, the inner features of network intrusion detection have been systematically ignored. To avoid the blind use of GP shown...
Many existing software reliability models are based on some subjective assumptions those could be easily impractical in reality. Genetic Programming(GP for short) does not need some subjective assumption due to the basic characteristic of the data. Also, this method doesn't require to understand the inherent processes for failures, but to create models based on the given data for a "true"...
The present study aims at improving the problem solving ability of the canonical genetic programming algorithm. The proposed method can be described as follows. The first investigates initialising population, the second investigates reproduction operator, the third investigates crossover operator, the fourth investigates mutation operation. This approach is examined on two experiments about symbolic...
GAPSO hybrid programming algorithm, which is a concise, effective and stable algorithm to solve the hierarchical problem based on GP algorithm. In terms of the specific characteristics of discrete magnitude and continuous magnitude, as well as the superiority of PSO in continuous quantity optimization, in this paper we propose an improved algorithm, which optimizes continuous magnitude by PSO while...
A mechanism involving evolutionary genetic programming (GP) and the expectation maximization algorithm (EM) is proposed to generate feature functions automatically, based on the primitive features, for an image pattern recognition system on the diagnosis of the disease OPMD. Prior to the feature function generation, we introduce a novel technique of the primitive texture feature extraction, which...
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