In this work, considering the solution of analog filter approximation problem, evolutionary algorithms are used to obtain nth order transfer functions. Coefficients of denominator polynomial of low pass analog filter ...
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ISBN:
(纸本)9781479948741
In this work, considering the solution of analog filter approximation problem, evolutionary algorithms are used to obtain nth order transfer functions. Coefficients of denominator polynomial of low pass analog filter are optimized and this process is carried out for three different orders of transfer functions. Simulation results show that error values obtained with evolutionary algorithms are less than that of traditional methods. The feasibility of the proposed method on circuit realization is investigated by designing passive and active analog filter circuits which realize 3rd order transfer function.
One name that comes to mind in connection with the word evolution is Darwin. One evolutionist however, who is rarely talked about, especially in the Artificial Intelligence community, is Peirce. The Darwinian model is...
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ISBN:
(纸本)9781424418220
One name that comes to mind in connection with the word evolution is Darwin. One evolutionist however, who is rarely talked about, especially in the Artificial Intelligence community, is Peirce. The Darwinian model is based on the concepts of absolute chance, mechanistic laws, and inexplicable interaction between the two. In contrast, Peirce's framework posits a dynamic interaction between possibility, necessity and regularity to describe the process of evolution. The theory of evolution proposed by Peirce is superior to the one proposed by Darwin because it is more general and it has greater explanatory power. Peirce's insights are significant enough to be used to improve the existing evolutionary algorithms. It was observed during our literature review that almost all evolutionary algorithms are fundamentally based on Darwinian principles of evolution. The present paper highlights the differences between Darwinian and Peircian evolutionary theories and provides the theoretical foundation for developing a novel Peirce based evolutionary Algorithm. Preliminary experiments have been conducted and results seem very promising.
The testing process of electronic control units (ECU) is time-consuming and cost-intensive. Virtual electronic control units (vECU) can solve these problems and also offer the advantage of a variety of observation poi...
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ISBN:
(纸本)9781728153650
The testing process of electronic control units (ECU) is time-consuming and cost-intensive. Virtual electronic control units (vECU) can solve these problems and also offer the advantage of a variety of observation points that are not available with classical ECUs. The additional observation points can be memory access or instruction monitoring. This paper shows a novel possibility to use evolutionary algorithms, which employ the observation points, to generate new test cases for safety and IT-security in automotive systems. The novel test cases and their ability to detect error classes are described in detail. It is shown, that evolutionary test methods are able to detect different error classes. However the evolutionary methods cannot detect all given error classes within one method. Hence, a combination of different tests methods is needed.
This work is based on research in a field of intelligent agent systems, negotiation algorithm solving tasks of energy saving, optimal electric vehicle control and transport flow control in traffic jam. Main goal of re...
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ISBN:
(纸本)9781424425044
This work is based on research in a field of intelligent agent systems, negotiation algorithm solving tasks of energy saving, optimal electric vehicle control and transport flow control in traffic jam. Main goal of research is energy saving for public electric transport. Mathematical model and evolutionary algorithm is proposed in the paper to solve multi-criteria optimization task minimizing idle time and electric energy used by public electric transport and maximize average speed of the flow in traffic jam. Paper presents a computer experiment to test proposed mathematical model and workability of evolutionary algorithm. The specific dynamic model of city transport system is created and results of evolutionary optimization are simulated.
Even though genetic algorithms (GAs) have been used for solving the project scheduling problem (PSP), it is not well understood which problem characteristics make it difficult/easy for GAs. We present the first runtim...
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ISBN:
(纸本)9781450311779
Even though genetic algorithms (GAs) have been used for solving the project scheduling problem (PSP), it is not well understood which problem characteristics make it difficult/easy for GAs. We present the first runtime analysis for the PSP, revealing what problem features can make PSP easy or hard. This allows to assess the performance of GAs and to make informed design choices. Our theory has inspired a new evolutionary design, including normalisation of employees' dedication for different tasks to eliminate the problem of exceeding their maximum dedication. Theoretical and empirical results show that our design is very effective in terms of hit rate and solution quality.
In this work, the denominator coefficients of a low-pass filter transfer function are optimized with evolutionary algorithms in order to obtain minimum approximation error and to reduce the distortion over the passban...
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ISBN:
(纸本)9781479930203
In this work, the denominator coefficients of a low-pass filter transfer function are optimized with evolutionary algorithms in order to obtain minimum approximation error and to reduce the distortion over the passband and stopband separately. For each design case, three different orders of transfer function are optimized. Simulation results show that evolutionary algorithms used in this work results in a short computation time with less approximation error than the conventional methods. Passive and active circuit realizations of filter transfer functions obtained with the most efficient EA method are also provided in order to show the feasibility of the proposed approach for circuit implementation.
In this paper, we empirically analyze the convergence behavior of evolutionary algorithms (evolution strategies - ES and genetic algorithms - GA) for two noisy optimization problems which belong to the class of functi...
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ISBN:
(纸本)9781424407033
In this paper, we empirically analyze the convergence behavior of evolutionary algorithms (evolution strategies - ES and genetic algorithms - GA) for two noisy optimization problems which belong to the class of functions with noise induced multi-modality (FNIMs). Although, both functions are qualitatively very similar, the ES is only able to converge to the global optimizer state for one of them. Additionally, we observe that canonical GA exhibits similar problems. We present a theoretical analysis which explains the different behaviors for the two functions and which suggests to resort to resampling strategies to solve the problem. Although, resampling is an inefficient way to cope with noisy optimization problems, it turns out that depending on the properties of the problem, (moderate) resampling might be necessary to guarantee convergence to the robust optimizer.
The application of pattern recognition techniques to radiology has the potential to detect cancer earlier and save lives, and consequently much research has been devoted to this problem. This worked tackled a subset o...
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ISBN:
(纸本)9781424407071
The application of pattern recognition techniques to radiology has the potential to detect cancer earlier and save lives, and consequently much research has been devoted to this problem. This worked tackled a subset of the problem, investigating a novel method of classifying mammograms using an evolutionary approach known as Cartesian Genetic Programming (CGP). Microcalcifications, one of two major indicators of cancer on mammograms, were used for the classification. A large software framework was written in order to investigate this, which allows the viewing of images, manual segmentation of lesions and then automatic classification. Two classification approaches were pursued, the first classifying on texture features and the second, a new approach, classifying by using the lesion's raw pixel array. Early results using the system showed some potential. It was found that during training, networks could obtain correct classification rates of between 80 and 100%. The best results were approaching those in the contemporary literature and suggest the technique warrants further investigation.
Dielectric elastomer stack actuators are a promising configuration of electroactive polymer actuators due to their favourable balance of output force and stroke capabilities. These performance characteristics are high...
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ISBN:
(纸本)9780819494702
Dielectric elastomer stack actuators are a promising configuration of electroactive polymer actuators due to their favourable balance of output force and stroke capabilities. These performance characteristics are highly dependent on many factors including layer geometry, mechanical and electrical material properties, etc. Thus, the specification of an optimal actuator design remains a challenging task. This study aims to assess the relationship of these factors on actuator performance by the application of evolutionary optimization algorithms in conjunction with a coupled multi-physics finite element simulation. This approach rapidly identifies the optimal actuator performance without the computational expense of simulating the entire design space.
Experimental results have suggested that evolutionary algorithms may produce higher quality solutions for instances of Vertex Cover than a very well known approximation algorithm for this NP-Complete problem. A theore...
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ISBN:
(纸本)9781424413393
Experimental results have suggested that evolutionary algorithms may produce higher quality solutions for instances of Vertex Cover than a very well known approximation algorithm for this NP-Complete problem. A theoretical analysis of the expected runtime of the (1+1)-EA on a well studied instance class confirms such a conjecture for the considered class. Furthermore, a class for which the (1+1)-EA takes exponential optimization time is examined. Nevertheless, given polynomial time, the evolutionary algorithm still produces a better solution than the approximation algorithm. Recently, the existence of an instance class has been proved for which the (1+1)-EA produces poor approximate solutions, given polynomial time. Here it is pointed out that, by using multiple runs, the (1+1)-EA finds the optimal cover of each instance of the considered graph class in polynomial time.
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