A rigorous runtime analysis of evolutionary multi-objective optimization for the classical vertex cover problem in the context of parameterized complexity analysis has been presented by Kratsch and Neumann [1]. In thi...
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ISBN:
(纸本)9783319458236;9783319458229
A rigorous runtime analysis of evolutionary multi-objective optimization for the classical vertex cover problem in the context of parameterized complexity analysis has been presented by Kratsch and Neumann [1]. In this paper, we extend the analysis to the weighted vertex cover problem and provide a fixed parameter evolutionary algorithm with respect to OPT, the cost of the optimal solution for the problem. Moreover, using a diversity mechanism, we present a multi-objective evolutionary algorithm that finds a 2-approximation in expected polynomial time.
It is a broadly accepted fact that evolutionary algorithms (EA) have to be developed problem-specifically. Usually this is based on experience and experiments. Though, most EB environments are not suited for such an a...
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ISBN:
(纸本)3540650784
It is a broadly accepted fact that evolutionary algorithms (EA) have to be developed problem-specifically. Usually this is based on experience and experiments. Though, most EB environments are not suited for such an approach. Therefore, this paper proposes a few basic concepts which should be supplied by modern EA simulators in order to serve as a toolkit for the development of such algorithms.
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.
This paper proposed a new evolutionary algorithm based on the Ito stochastic process, term as ITO algorithm, which is provided with not only a self-adaptation feature, but a tightly coupling with GAs, PSOs and the SAs...
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ISBN:
(纸本)9783642163876
This paper proposed a new evolutionary algorithm based on the Ito stochastic process, term as ITO algorithm, which is provided with not only a self-adaptation feature, but a tightly coupling with GAs, PSOs and the SAs as well. In this paper, the detail designs of drift and wave operator are proposed. A lot of experiments of numerical optimization verify that the performance of ITO algorithm is excellent. Compared with several famous algorithms, Ito algorithm achieves an improved precision and obvious reduction of the computation.
Multiple sequence alignment (MSA) is a central problem in bioinformatics. In this study, we extended previous efforts using evolutionary algorithms (EAs) for MSA. Candidate solutions in the initial population were der...
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ISBN:
(纸本)0780378040
Multiple sequence alignment (MSA) is a central problem in bioinformatics. In this study, we extended previous efforts using evolutionary algorithms (EAs) for MSA. Candidate solutions in the initial population were derived from the well-known alignment program Clustal X. evolutionary computation was then used to evolve increasingly appropriate solutions. Three new alignment operators were introduced and tested within the framework of protein sequence alignment. Statistics on alignment quality were generated with respect to selected alignment benchmarks from the BAliBASE database using the BLOSUM 62 substitution matrix. Our results indicate the degree to which EAs can enhance the results of Clustal X. Moreover, the experimental results show that the commonly used sum-of-pairs scoring scheme sometimes tails to correlate higher scoring alignments with increase in alignment quality in terms of the BAliBASE sum-of-pairs score.
This paper discusses the asymptotic convergence of evolutionary algorithms based on finite search space by using the properties of Markov chains and Perron-Frobenius Theorem. First, some convergence results of general...
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ISBN:
(纸本)9783540743767
This paper discusses the asymptotic convergence of evolutionary algorithms based on finite search space by using the properties of Markov chains and Perron-Frobenius Theorem. First, some convergence results of general square matrices are given. Then, some useful properties of homogeneous Markov chains with finite states are investigated. Finally, the geometric convergence rates of the transition operators, which is determined by the revised spectral of the corresponding transition matrix of a Markov chain associated with the EA considered here, are estimated by combining the acquired results in this paper.
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.
We present a new representation for individuals in problems that have cyclic permutations as solutions. To demonstrate its usefulness, we analyze a simple randomized local search and a (1+1) evolutionary algorithm for...
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ISBN:
(纸本)9781424407033
We present a new representation for individuals in problems that have cyclic permutations as solutions. To demonstrate its usefulness, we analyze a simple randomized local search and a (1+1) evolutionary algorithm for the Eulerian cycle problem utilizing this representation. Both have an expected run-time of 19(m(2) log(M)), where m denotes the number of edges of the input graph. This clearly beats previous solutions ' which all have an expected optimization time of 8(m(3)) or worse (PPSN 106, CEC '04). We are optimistic that our representation also allows superior solutions for other cyclic permutation problems. For NP-complete ones like the TSP, however, other means than theoretical run-time analyses are necessary.
In this paper further investigation of the previously proposed method of speeding up single-objective evolutionary algorithms is done. The method is based on reinforcement learning which is used to choose auxiliary fi...
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ISBN:
(纸本)9780769549132;9781467346511
In this paper further investigation of the previously proposed method of speeding up single-objective evolutionary algorithms is done. The method is based on reinforcement learning which is used to choose auxiliary fitness functions. The requirements for this method are formulated. The compliance of the method with these requirements is illustrated on model problems such as Royal Roads problem and H-IFF optimization problem. The experiments confirm that the method increases the efficiency of evolutionary algorithms.
A detailed review of a wide range of meta-heuristic and evolutionary algorithms in a systematic manner and how they relate to engineering optimization problems This book introduces the main metaheuristic algorithms an...
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ISBN:
(数字)9781119387053
ISBN:
(纸本)9781119386995
A detailed review of a wide range of meta-heuristic and evolutionary algorithms in a systematic manner and how they relate to engineering optimization problems This book introduces the main metaheuristic algorithms and their applications in optimization. It describes 20 leading meta-heuristic and evolutionary algorithms and presents discussions and assessments of their performance in solving optimization problems from several fields of engineering. The book features clear and concise principles and presents detailed descriptions of leading methods such as the pattern search (PS) algorithm, the genetic algorithm (GA), the simulated annealing (SA) algorithm, the Tabu search (TS) algorithm, the ant colony optimization (ACO), and the particle swarm optimization (PSO) technique. Chapter 1 of Meta-heuristic and evolutionary algorithms for Engineering Optimization provides an overview of optimization and defines it by presenting examples of optimization problems in different engineering domains. Chapter 2 presents an introduction to meta-heuristic and evolutionary algorithms and links them to engineering problems. Chapters 3 to 22 are each devoted to a separate algorithm— and they each start with a brief literature review of the development of the algorithm, and its applications to engineering problems. The principles, steps, and execution of the algorithms are described in detail, and a pseudo code of the algorithm is presented, which serves as a guideline for coding the algorithm to solve specific applications. This book: Introduces state-of-the-art metaheuristic algorithms and their applications to engineering optimization; Fills a gap in the current literature by compiling and explaining the various meta-heuristic and evolutionary algorithms in a clear and systematic manner; Provides a step-by-step presentation of each algorithm and guidelines for practical implementation and coding of algorithms; Discusses and assesses the performance of metaheuristic algorithms in mu
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