It is important in digital signal processing (DSP) architectures to minimize the silicon area of the integrated circuits. This can be achieved by reducing the number of functional units such as adders and multipliers....
详细信息
It is important in digital signal processing (DSP) architectures to minimize the silicon area of the integrated circuits. This can be achieved by reducing the number of functional units such as adders and multipliers. In literature, folding technique is used to reduce the functional units by executing multiple algorithm operations on a single functional unit. Register minimization techniques are used to reduce the number of registers in a folded architecture. Retiming is a technique that needs to be performed before applying folding. In this paper, retiming is performed based on nature inspired evolutionary computation method. This technique generates the database of solutions from which best solution can be picked for folding further. As a part of this work, an efficient folded noise removal audio filter prototype is designed as an application example using evolutionary computation-based retiming and folding with register minimization. Folding technique will however increase the number of registers while multiplexing datapath adder and multiplier elements. Register minimization technique is used after folding to reduce the number of registers. After obtaining retimed, folded filter architecture, low level synthesis is performed which involves mapping of datapath adder and multiplier blocks to actual hardware. Various architectures of adders and multipliers are compared in area-power-performance space and depending on the user defined constraint, folded architecture with specific combination of data path elements is mapped on to hardware. A framework is designed in this paper to automate the entire process which reduces the design cycle time. All the designed filters are targeted for ASIC implementation. The results are compared and are provided as part of simulation results.
We consider algorithmic design, enhancement, and improvement of evolutionary computation as a mechanism design problem. All individuals or several groups of individuals can be considered as self-interested agents. The...
详细信息
We consider algorithmic design, enhancement, and improvement of evolutionary computation as a mechanism design problem. All individuals or several groups of individuals can be considered as self-interested agents. The individuals in evolutionary computation can manipulate parameter settings and operations by satisfying their own preferences, which are defined by an evolutionary computation algorithm designer, rather than by following a fixed algorithm rule. evolutionary computation algorithm designers or self-adaptive methods should construct proper rules and mechanisms for all agents (individuals) to conduct their evolution behaviour correctly in order to definitely achieve the desired and preset objective(s). As a case study, we propose a formal framework on parameter setting, strategy selection, and algorithmic design of evolutionary computation by considering the Nash strategy equilibrium of a mechanism design in the search process. The evaluation results present the efficiency of the framework. This primary principle can be implemented in any evolutionary computation algorithm that needs to consider strategy selection issues in its optimization process. The final objective of our work is to solve evolutionary computation design as an algorithmic mechanism design problem and establish its fundamental aspect by taking this perspective. This paper is the first step towards achieving this objective by implementing a strategy equilibrium solution (such as Nash equilibrium) in evolutionary computation algorithm.
Rapid prototyping by combining evolutionary computation with simulations is becoming a powerful tool for solving complex design problems in materials science. This method of optimization operates in a virtual design s...
详细信息
Rapid prototyping by combining evolutionary computation with simulations is becoming a powerful tool for solving complex design problems in materials science. This method of optimization operates in a virtual design space that simulates potential material behaviors and after completion needs to be validated by experiment. However, in principle an evolutionary optimizer can also operate on an actual physical structure or laboratory experiment directly, provided the relevant material parameters can be accessed by the optimizer and information about the material's performance can be updated by direct measurements. Here we provide a proof of concept of such direct, physical optimization by showing how a reconfigurable, highly nonlinear material can be tuned to respond to impact. We report on an entirely computer controlled laboratory experiment in which a 6×6 grid of electromagnets creates a magnetic field pattern that tunes the local rigidity of a concentrated suspension of ferrofluid and iron filings. A genetic algorithm is implemented and tasked to find field patterns that minimize the force transmitted through the suspension. Searching within a space of roughly 1010 possible configurations, after testing only 1500 independent trials the algorithm identifies an optimized configuration of layered rigid and compliant regions.
Integer Factorization is a vital number theoretic problem frequently finding application in public-key cryptography like RSA encryption systems, and other areas like Fourier transform algorithm. The problem is computa...
详细信息
Integer Factorization is a vital number theoretic problem frequently finding application in public-key cryptography like RSA encryption systems, and other areas like Fourier transform algorithm. The problem is computationally intractable because it is a one-way mathematical function. Due to its computational infeasibility, it is extremely hard to find the prime factors of a semi prime number generated from two randomly chosen similar sized prime numbers. There has been a recently growing interest in the community with regards to evolutionary computation and other alternative approaches to solving this problem as an optimization task. However, the results still seem to be very rudimentary in nature and there's much work to be done. This paper emphasizes on such approaches and presents a critic study in details. The paper puts forth criticism and ideas in this aspect.
The QRS complex is a very informative component of the electrocardiogram (ECG). It corresponds to ventricular depolarization of the human heart. The detection of QRS complexes provides the fundamental basis for any au...
详细信息
ISBN:
(纸本)9783319023090
The QRS complex is a very informative component of the electrocardiogram (ECG). It corresponds to ventricular depolarization of the human heart. The detection of QRS complexes provides the fundamental basis for any automated ECG analysis system. In this paper, evolutionary computation is applied for preprocessing filter design in QRS detection algorithm. In the proposed solution, ECG signal bandwidth is separated into multiple sub-bands and corresponding filters are optimized to minimize the number of false QRS detections. The algorithm performance has been evaluated with the MIT/BIH arrhythmia database. The obtained results show significant improvement, in terms of false detection reduction for ECG signals with high level of noise, when compared to the other available QRS detection algorithms.
The vehicle routing problem(VRP) is an important issue in practical use. VRPs are one of combinatorial optimization problems. For solving such combinatorial problems, several evolutionary computation methods have been...
详细信息
The vehicle routing problem(VRP) is an important issue in practical use. VRPs are one of combinatorial optimization problems. For solving such combinatorial problems, several evolutionary computation methods have been proposed. In practical situation, many complex constraint conditions and desired computation time are obstacles to use evolutionary computation methods to such problems. In this paper, an evolutionary computation based solver is developed. From some computational experiments for real tanker truck scheduling problem, effectiveness of the proposed method is shown.
This paper aims to examine the application of evolutionary algorithms to the form finding problem of high-rise buildings. In the light of mentioned purpose, this study concentrates on the conceptual phase of the desig...
详细信息
This paper aims to examine the application of evolutionary algorithms to the form finding problem of high-rise buildings. In the light of mentioned purpose, this study concentrates on the conceptual phase of the design process due to the importance of early design decisions. In this respect, multiobjective real-parameter constrained optimization is considered as the method of this study in order to solve high-rise design problem. From the point of evolutionary computation, we compare two evolutionary algorithms (NSGA-II and DE) focusing on their computational performance and architectural features of the resulting alternatives. Two objective functions are formulated that specifically focus on structural displacement minimization and construction cost per square meter minimization, which are clearly conflicting. As a conclusion, we discuss in the context of the high-rise design problem, the solutions identified by the NSGA-II and DE algorithms.
Attribute or feature selection is one of the basic strategies to improve the performances of data classification tasks, and, at the same time to reduce the complexity of classifiers, and it is a particularly fundament...
详细信息
Attribute or feature selection is one of the basic strategies to improve the performances of data classification tasks, and, at the same time to reduce the complexity of classifiers, and it is a particularly fundamental one when the number of attributes is relatively high. evolutionary computation has already proven itself to be a very effective choice to consistently reduce the number of attributes towards a better classification rate and a simpler semantic interpretation of the inferred classifiers. We propose the application of the multi-objective evolutionary algorithm ENORA to the task of feature selection for multi-class classification of data extracted from an integrated multi-channel multi-skill contact center, which include technical, service and central data for each session. Additionally, we propose a methodology to integrate feature selection for classification, model evaluation, and decision making to choose the most satisfactory model according to a "a posteriori" process in a multi-objective context. We check out our results by comparing the performance and the classification rate against the well-known multi-objective evolutionary algorithm NSGA-II. Finally, the best obtained solution is validated by a data expert's semantic interpretation of the classifier.
Global Sensitivity Analysis (GSA) studies how uncertainty in the inputs of a system influences uncertainty in its outputs. GSA is extensively used by experts to gather information about the behavior of models, through...
详细信息
ISBN:
(纸本)9781450334884
Global Sensitivity Analysis (GSA) studies how uncertainty in the inputs of a system influences uncertainty in its outputs. GSA is extensively used by experts to gather information about the behavior of models, through computationally-intensive stochastic sampling of parameters' space. Some studies propose to make use of the considerable quantity of data acquired in this way to optimize the model parameters, often resorting to evolutionary Algorithms (EAs). Nevertheless, efficiently exploiting information gathered from GSA might not be so straightforward. In this paper, we present a counterexample followed by experimental results to prove how naively combining GSA and EA can bring about negative outcomes.
Representation is a central issue in evolutionary computation. The no free lunch theorem demonstrates that there is no intrinsic advantage in a particular algorithm when considered against complete spaces of problems....
详细信息
Representation is a central issue in evolutionary computation. The no free lunch theorem demonstrates that there is no intrinsic advantage in a particular algorithm when considered against complete spaces of problems. The corollary is that algorithms should be fitted to the problems they are solving. Choice of representation is the primary point in the design of an evolutionary algorithm where the designer can incorporate domain knowledge and, in effect, choose the adaptive landscape he is searching. This tutorial will introduces representations for level design, agents for playing mathematical games, and for the design of non-player characters. Time permitting, other examples of representation will be included.
暂无评论