This paper presents a methodology for finding the optimal output power from a PEM fuel cell power plant (FCPP). The FCPP is used to supply power to a small micro-grid community. The technique used is based on evolutio...
详细信息
This paper presents a methodology for finding the optimal output power from a PEM fuel cell power plant (FCPP). The FCPP is used to supply power to a small micro-grid community. The technique used is based on evolutionary programming (EP) to find a near-optimal solution of the problem. The method incorporates the Hill-Climbing technique (HCT) to maintain feasibility during the solution process. An economic model of the FCPP is used. The model considers the production cost of energy and the possibility of selling and buying electrical energy from the local grid. In addition, the model takes into account the thermal energy output from the FCPP and the thermal energy requirement for the micro-grid community. The results obtained are compared against a solution based on genetic algorithms. Results are encouraging and indicate viability of the proposed technique. (C) 2004 Elsevier B.V. All rights reserved.
This paper presents the use of evolutionary programming to minimize the length of addition chains. Generating minimal addition chains is considered an NP-hard search problem. Addition chains are employed to reduce the...
详细信息
This paper presents the use of evolutionary programming to minimize the length of addition chains. Generating minimal addition chains is considered an NP-hard search problem. Addition chains are employed to reduce the number of multiplications in modular exponentiation for data encryption and decryption in public-key cryptosystems. The algorithm is based on a mutation operator able to generate a set of feasible addition chains from a single solution and a replacement mechanism with stochastic elements to favor diversity in the population. Furthermore, the proposed algorithm is coupled with a deterministic method with the aim to solve large exponents. Five experiments are carried out to test the approach in different types of exponents. The proposed algorithm is able to find competitive or even better results by requiring a lower number of evaluations with respect to those required by state-of-the-art nature-inspired algorithms. (C) 2014 Elsevier Ltd. All rights reserved.
In this article, a novel scattering center extraction method using genetic algorithm is proposed to deal with the ultra-wideband （UWB） localization image, which is called evolutionary programming （EP） CLEAN algori...
详细信息
In this article, a novel scattering center extraction method using genetic algorithm is proposed to deal with the ultra-wideband （UWB） localization image, which is called evolutionary programming （EP） CLEAN algorithm. Because of the UWB characters, the ideal point scattering model and EP method are used in the algorithm for optimizing the UWB localization images. After introducing the algorithm detail, the actual model is used to realize the EP CLEAN algorithm. Compared with the conventional localization imaging algorithm, this algorithm has advantages fitting the UWB characters such as accuracy, robustness, and better resolution, which are verified by the numerical simulations. Therefore the EP CLEAN algorithm could improve localization image performance to expand the UWB technique application.
evolutionary programming was originally proposed in 1962 as an alternative method for generating machine intelligence. This paper reviews some of the early development of the method and focuses on three current avenue...
详细信息
evolutionary programming was originally proposed in 1962 as an alternative method for generating machine intelligence. This paper reviews some of the early development of the method and focuses on three current avenues of research: pattern discovery, system identification and automatic control. Recent efforts along these lines are described. In addition, the application of evolutionary algorithms to autonomous system design on parallel processing computers is briefly discussed.
Recently, two evolutionary programming (EP) algorithms (Classical EP and Fast EP) have been proposed in order to design Finite Impulse Response digital filters [1]. The proposed techniques can be used to design digita...
详细信息
ISBN:
(纸本)9781424408290
Recently, two evolutionary programming (EP) algorithms (Classical EP and Fast EP) have been proposed in order to design Finite Impulse Response digital filters [1]. The proposed techniques can be used to design digital fillers for a wide range of applications, such as data transmission, subband coding or narrowband interference detection. Here, we focus our attention on the design of nearly perfect reconstruction Cosine Modulated Filter Banks. The EP algorithms are used to determine the optimum values for the samples of the magnitude response Fourier transform. located in the transition hand of the prototype filter. Thus. the technique is simplified by constraining most the Fourier transform magnitude of the prototype filter., leaving only a small number of values to be optimized. The analytical and simulation results show again that the designed system performance is extremely good.
In this paper, first manufacturing scheduling is briefly discussed and later the problem studied is introduced. The optimal solution to minimizing the average flow time in single machine scheduling is obtained by the ...
详细信息
In this paper, first manufacturing scheduling is briefly discussed and later the problem studied is introduced. The optimal solution to minimizing the average flow time in single machine scheduling is obtained by the Shortest Processing Time rule if ready times are zero for all jobs. In the case of non-zero ready times, preemption plays a key role in the solution. Preemption allowed version is solved optimally by using the Shortest Remaining Processing Time procedure. However, the version of preemption not allowed is known as NP-hard and delay and nondelay strategies might be used in a hybrid fashion. This paper focuses on minimizing the average flow time in the presence of non-zero times and when preemption is not allowed. The proposed method is evolutionary programming (EP). The results indicate that EP produces near optimal and consistent results in a short period of time. (C) 2003 Elsevier Science Ltd. All rights reserved.
Deep Learning (DL) has made significant changes to a large number of research areas in recent decades. For example, several astonishing Convolutional Neural Network (CNN) models have been built by researchers to fulfi...
详细信息
ISBN:
(纸本)9781728142722
Deep Learning (DL) has made significant changes to a large number of research areas in recent decades. For example, several astonishing Convolutional Neural Network (CNN) models have been built by researchers to fulfill image classification needs using large-scale visual datasets successfully. Transfer Learning (TL) makes use of those pre-trained models to ease the feature learning process for other target domains that contain a smaller amount of training data. Currently, there are numerous ways to utilize features generated by transfer learning. Pre-trained CNN models prepare mid-/high-level features to work for different targeting problem domains. In this paper, a DL feature and model selection framework based on evolutionary programming is proposed to solve the challenges in visual data classification. It automates the process of discovering and obtaining the most representative features generated by the pre-trained DL models for different classification tasks.
In this paper, we present exponential evolutionary programming (EEP) with the mutation based on double exponential probability distribution. By controlling the parameter value of EEP, an effective convergence of evolu...
详细信息
ISBN:
(纸本)0889865361
In this paper, we present exponential evolutionary programming (EEP) with the mutation based on double exponential probability distribution. By controlling the parameter value of EEP, an effective convergence of evolutionary programming (EP) can be expected comparing with conventional algorithm. Moreover, a new EEP without strategy parameter is proposed and shown that its performance is superior to other EP algorithm with simple parameter setting.
evolutionary programming is the most powerful method for inducing recursive functional programs from input/output examples while taking into account efficiency and complexity constraints for the target program. Howeve...
详细信息
ISBN:
(纸本)9789896740146
evolutionary programming is the most powerful method for inducing recursive functional programs from input/output examples while taking into account efficiency and complexity constraints for the target program. However, synthesis time can be considerably high. A strategy which is complementary to the generate-and -test based approaches of evolutionary programming is inductive analytical programming where program construction is example-driven, that is, target programs are constructed as minimal generalization over the given input/output examples. Synthesis with analytical approaches is fast, but the scope of synthesizable programs is restricted. We propose to combine both approaches in such a way that the power of evolutionary programming is preserved and synthesis becomes more efficient. We use the analytical system IGOR2 to generate seeds in form of program skeletons to guide the evolutionary system ADATE when searching for target programs. In an evaluations with several examples we can show that using such seeds indeed can speed up evolutionary programming considerably.
This paper presents a distributed scheduling architecture for a multi-service routing switch, based on evolutionary algorithms to solve a multi-objective optimization problem. The aim of the two-level scheduler is to ...
详细信息
ISBN:
(纸本)1880843498
This paper presents a distributed scheduling architecture for a multi-service routing switch, based on evolutionary algorithms to solve a multi-objective optimization problem. The aim of the two-level scheduler is to ensure better quality of service for individual flows and achieve near 100% throughput with minimal delay at the switch level under uniform traffic conditions. It is shown that evolutionary Algorithm provides an efficient scheduling mechanism, because the decision-making is dependent on the real traffic conditions. Simulation results show the performance of the distributed evolutionary scheduler is much better than the conventional fair queuing schemes and efficiently integrates flow and switch level scheduling. The scheduling scheme is simple to design and fairly inexpensive when implemented using FPGA technology.
暂无评论