EAs combined in various instances with numerical methods in electromagnetics have increased their impact on antenna design and propagation problems.[...]hybrid combinations of EAs with other algorithms inspired by phy...
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EAs combined in various instances with numerical methods in electromagnetics have increased their impact on antenna design and propagation problems.
[...]hybrid combinations of EAs with other algorithms inspired by physics or chemistry are also emerging.
L. A. R. Ramirez and J. C. A. dos Santos in the paper “Design, Simulation, and Optimization of an Irregularly Shaped Microstrip Patch Antenna for Air-to-Ground Communications” combine the finite difference time-domain method (FDTD) in conjunction with a genetic algorithm (GA).
In the paper “Application of Genetic Algorithm to Estimation of Function Parameters in Lightning Currents Approximations,” the genetic algorithm is applied for the estimation of the parameters of two-peaked analytically extended function (2P-AEF) which are used for approximation of measured and typical lightning discharge currents.
Hepatitis, is one of the most common and dangerous diseases which affects liver. If hepatitis does not detect early, some side effects such as cirrhosis, hepatocellular carcinoma, liver failure and mature death will b...
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ISBN:
(数字)9783319953120
ISBN:
(纸本)9783319953120;9783319953113
Hepatitis, is one of the most common and dangerous diseases which affects liver. If hepatitis does not detect early, some side effects such as cirrhosis, hepatocellular carcinoma, liver failure and mature death will be occurred. Among different types of this disease, hepatitis C arises from HCV viruses, is the leading cause of liver disease. Although hepatitis C can be easily diagnosed by a simple test, the intensity rate of this disease is a qualitative and controversial issue. This paper attempts to design a fuzzy expert system for diagnosing the intensity rate of hepatitis C with FibroScan results. The proposed system includes three steps: pre-processing, create the primary fuzzy system and optimize the membership functions' parameters. KNN method is used for filling missing data;moreover, feature selection is done by decision tree and genetic algorithm. The primary fuzzy system is established and in the third step, three different evolutionary algorithms are implemented to optimize the parameters of primary system. Results portray that Differential Evolution algorithm presents better performance in learning the pattern of data and decreases the error around 30%.
evolutionary algorithms are bio-inspired algorithms that can easily adapt to changing environments. In this paper, we study single- and multi-objective baseline evolutionary algorithms for the classical knapsack probl...
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ISBN:
(纸本)9783319992532;9783319992525
evolutionary algorithms are bio-inspired algorithms that can easily adapt to changing environments. In this paper, we study single- and multi-objective baseline evolutionary algorithms for the classical knapsack problem where the capacity of the knapsack varies over time. We establish different benchmark scenarios where the capacity changes every tau iterations according to a uniform or normal distribution. Our experimental investigations analyze the behavior of our algorithms in terms of the magnitude of changes determined by parameters of the chosen distribution, the frequency determined by tau and the class of knapsack instance under consideration. Our results show that the multi-objective approaches using a population that caters for dynamic changes have a clear advantage on many benchmarks scenarios when the frequency of changes is not too high.
evolutionary algorithms are generic and flexible optimization algorithms which can be applied to many optimization problems in different domains. Depending on the specific type of evolutionary algorithm, they offer se...
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ISBN:
(纸本)9783319747187;9783319747170
evolutionary algorithms are generic and flexible optimization algorithms which can be applied to many optimization problems in different domains. Depending on the specific type of evolutionary algorithm, they offer several parameters such as population size, mutation probability, crossover and mutation operators, or number of elite solutions. How these parameters are set has a crucial impact on the algorithm's search behavior and thus affects its performance. Therefore, parameter tuning is an important and challenging task in each application of evolutionary algorithms in order to retrieve satisfying results. In this paper, we show how software frameworks for evolutionary algorithms can support this task. As an example of such a framework, we describe how HeuristicLab enables automated execution of extensive parameter tests as well as its capabilities to analyze and visualize the obtained results. We also introduce a new chart of HeuristicLab, which can be used to compare the performance of many different parameter configurations and to drill down on different configurations in an interactive way. By this means this new chart helps users to visualize the influence of different parameter values as well as their interdependencies and is therefore a powerful feature in order to gain a deeper understanding of the behavior of evolutionary algorithms.
This paper deals with the optimization problem subject to an objective function evaluation-iteration constraint. This problem is practical when the objective function is expensive on money and/or time because practiti...
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ISBN:
(数字)9781728121437
ISBN:
(纸本)9781728121444
This paper deals with the optimization problem subject to an objective function evaluation-iteration constraint. This problem is practical when the objective function is expensive on money and/or time because practitioners usually face money and/or time constraints in any real-world project. For the problem, this paper suggests a solver framework which adapt evolutionary algorithms to the evaluation-iteration constraint. Specifically, the proposed framework updates setting parameters of a target evolutionary algorithm every generation by solving a small size optimization problem related to the evaluation-iteration constraint. Effectiveness of the proposed framework was confirmed for the standard particle swarm optimization algorithm through the numerical experiment.
One of the easiest randomized greedy optimization algorithms is the following evolutionary algorithm which aims at maximizing a function f : {0,1}(n) -> R. The algorithm starts with a random search point..{0,1} n, ...
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One of the easiest randomized greedy optimization algorithms is the following evolutionary algorithm which aims at maximizing a function f : {0,1}(n) -> R. The algorithm starts with a random search point..{0,1} n, and in each round it flips each bit of. with probability c/n independently at random, where c > 0 is a fixed constant. The thus created offspring. xi' replaces xi if and only if f (xi') >= f (xi). The analysis of the runtime of this simple algorithm for monotone and for linear functions turned out to be highly non-trivial. In this paper we review known results and provide new and self-contained proofs of partly stronger results.
This work shows the comparison among three evolutionary algorithms used to estimate the parameters of the equivalent circuit of a three-phase induction motor. With the parameters of the motor is possible to calculate ...
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ISBN:
(纸本)9781538667408
This work shows the comparison among three evolutionary algorithms used to estimate the parameters of the equivalent circuit of a three-phase induction motor. With the parameters of the motor is possible to calculate its efficiency. Applying statistical methods, the number of runs needed to obtain a confidence level of 95% is calculated. With this value each algorithm is used to estimate the motor's parameters and, according to the results, is possible to find the best
In this paper we propose a data-driven approach for the construction of survey-based indicators using large data sets. We make use of agents' expectations about a wide range of economic variables contained in the ...
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In this paper we propose a data-driven approach for the construction of survey-based indicators using large data sets. We make use of agents' expectations about a wide range of economic variables contained in the World Economic Survey, which is a tendency survey conducted by the Ifo Institute for Economic Research. By means of genetic programming we estimate a symbolic regression that links survey-based expectations to a quantitative variable used as a yardstick, deriving mathematical functional forms that approximate the target variable. We use the evolution of GDP as a target. This set of empirically-generated indicators of economic growth, are used as building blocks to construct an economic indicator. We compare the proposed indicator to the Economic Climate Index, and we evaluate its predictive performance to track the evolution of the GDP in ten European economies. We find that in most countries the proposed indicator outperforms forecasts generated by a benchmark model.
Change-proneness prediction of software components has become a significant research area wherein the quest for the best classifier still persists. Although numerous statistical and Machine Learning (ML) techniques ha...
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ISBN:
(纸本)9781538666739
Change-proneness prediction of software components has become a significant research area wherein the quest for the best classifier still persists. Although numerous statistical and Machine Learning (ML) techniques have been presented and employed in the past literature for an efficient generation of change-proneness prediction models, evolutionary algorithms, on the other hand, remain vastly unexamined and unaddressed for this purpose. Bearing this in mind, this research work targets to probe the potency of six evolutionary algorithms for developing such change prediction models, specifically for source code files. We employ apposite object oriented metrics to construct four software datasets from four consecutive releases of a software project. Furthermore, the prediction capability of the selected evolutionary algorithms is evaluated, ranked and compared against two statistical classifiers using the Wilcoxon signed rank test and Friedman statistical test. On the basis of the results obtained from the experiments conducted in this article, it can be ascertained that the evolutionary algorithms possess a capability for predicting change-prone files with high accuracies, sometimes even higher than the selected statistical classifiers.
As automatic optimization techniques find their way into industrial applications, the behavior of many complex systems is determined by some form of planner picking the right actions to optimize a given objective func...
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ISBN:
(纸本)9781538651391
As automatic optimization techniques find their way into industrial applications, the behavior of many complex systems is determined by some form of planner picking the right actions to optimize a given objective function. In many cases, the mapping of plans to objective reward may change due to unforeseen events or circumstances in the real world. In those cases, the planner usually needs some additional effort to adjust to the changed situation and reach its previous level of performance. Whenever we still need to continue polling the planner even during re-planning, it oftentimes exhibits severely lacking performance. In order to improve the planner's resilience to unforeseen change, we argue that maintaining a certain level of diversity amongst the considered plans at all times should be added to the planner's objective. Effectively, we encourage the planner to keep alternative plans to its currently best solution. As an example case, we implement a diversity-aware genetic algorithm using two different metrics for diversity (differing in their generality) and show that the blow in performance due to unexpected change can be severely lessened in the average case. We also analyze the parameter settings necessary for these techniques in order to gain an intuition how they can be incorporated into larger frameworks or process models for software and systems engineering.
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