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106
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
 ACM COMPUTING SURVEYS
, 2003
"... The field of metaheuristics for the application to combinatorial optimization problems is a rapidly growing field of research. This is due to the importance of combinatorial optimization problems for the scientific as well as the industrial world. We give a survey of the nowadays most important meta ..."
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Cited by 194 (14 self)
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The field of metaheuristics for the application to combinatorial optimization problems is a rapidly growing field of research. This is due to the importance of combinatorial optimization problems for the scientific as well as the industrial world. We give a survey of the nowadays most important metaheuristics from a conceptual point of view. We outline the different components and concepts that are used in the different metaheuristics in order to analyze their similarities and differences. Two very important concepts in metaheuristics are intensification and diversification. These are the two forces that largely determine the behaviour of a metaheuristic. They are in some way contrary but also complementary to each other. We introduce a framework, that we call the I&D frame, in order to put different intensification and diversification components into relation with each other. Outlining the advantages and disadvantages of different metaheuristic approaches we conclude by pointing out the importance of hybridization of metaheuristics as well as the integration of metaheuristics and other methods for optimization.
An Indexed Bibliography of Genetic Algorithms in Power Engineering
, 1995
"... s: Jan. 1992  Dec. 1994 ffl CTI: Current Technology Index Jan./Feb. 1993  Jan./Feb. 1994 ffl DAI: Dissertation Abstracts International: Vol. 53 No. 1  Vol. 55 No. 4 (1994) ffl EEA: Electrical & Electronics Abstracts: Jan. 1991  Dec. 1994 ffl P: Index to Scientific & Technical Proceed ..."
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Cited by 79 (10 self)
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s: Jan. 1992  Dec. 1994 ffl CTI: Current Technology Index Jan./Feb. 1993  Jan./Feb. 1994 ffl DAI: Dissertation Abstracts International: Vol. 53 No. 1  Vol. 55 No. 4 (1994) ffl EEA: Electrical & Electronics Abstracts: Jan. 1991  Dec. 1994 ffl P: Index to Scientific & Technical Proceedings: Jan. 1986  Feb. 1995 (except Nov. 1994) ffl EI A: The Engineering Index Annual: 1987  1992 ffl EI M: The Engineering Index Monthly: Jan. 1993  Dec. 1994 The following GA researchers have already kindly supplied their complete autobibliographies and/or proofread references to their papers: Dan Adler, Patrick Argos, Jarmo T. Alander, James E. Baker, Wolfgang Banzhaf, Ralf Bruns, I. L. Bukatova, Thomas Back, Yuval Davidor, Dipankar Dasgupta, Marco Dorigo, Bogdan Filipic, Terence C. Fogarty, David B. Fogel, Toshio Fukuda, Hugo de Garis, Robert C. Glen, David E. Goldberg, Martina GorgesSchleuter, Jeffrey Horn, Aristides T. Hatjimihail, Mark J. Jakiela, Richard S. Judson, Akihiko Konaga...
Ideal Evaluation from Coevolution
 Evolutionary Computation
, 2004
"... In many problems of interest, performance can be evaluated using tests, such as examples in concept learning, test points in function approximation, and opponents in gameplaying. Evaluation on all tests is often infeasible. Identification of an accurate evaluation or fitness function is a difficult ..."
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Cited by 62 (6 self)
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In many problems of interest, performance can be evaluated using tests, such as examples in concept learning, test points in function approximation, and opponents in gameplaying. Evaluation on all tests is often infeasible. Identification of an accurate evaluation or fitness function is a difficult problem in itself, and approximations are likely to introduce human biases into the search process. Coevolution evolves the set of tests used for evaluation, but has so far often led to inaccurate evaluation. We show that for any set of learners, a Complete Evaluation Set can be determined that provides ideal evaluation as specified by Evolutionary MultiObjective Optimization. This provides a principled approach to evaluation in coevolution, and thereby brings automatic ideal evaluation within reach. The Complete Evaluation Set is of manageable size, and progress towards it can be accurately measured. Based on this observation, an algorithm named DELPHI is developed. The algorithm is tested on problems likely to permit progress on only a subset of the underlying objectives. Where all comparison methods result in overspecialization, the proposed method and a variant achieve sustained progress in all underlying objectives. These findings demonstrate that ideal evaluation may be approximated by practical algorithms, and that accurate evaluation for testbased problems is possible even when the underlying objectives of a problem are unknown.
Evolutionary computation in structural design
 Journal of Engineering with Computers
, 2001
"... Evolutionary computation is emerging as a new engineering computational paradigm, which may significantly change the present structural design practice. For this reason, an extensive study of evolutionary computation in the context of structural design has been conducted in the Information Technolog ..."
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Cited by 37 (5 self)
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Evolutionary computation is emerging as a new engineering computational paradigm, which may significantly change the present structural design practice. For this reason, an extensive study of evolutionary computation in the context of structural design has been conducted in the Information Technology and Engineering School at George Mason University and its results are reported here. First, a general introduction to evolutionary computation is presented and recent developments in this field are briefly described. Next, the field of evolutionary design is introduced and its relevance to structural design is explained. Further, the issue of creativity/novelty is discussed and possible ways of achieving it during a structural design process are suggested. Current research progress in building engineering systems ’ representations, one of the key issues in evolutionary design, is subsequently discussed. Next, recent developments in constrainthandling methods in evolutionary optimization are reported. Further, the rapidly growing field of evolutionary multiobjective optimization is presented and briefly described. An emerging subfield of coevolutionary design is subsequently introduced and its current advancements reported. Next, a comprehensive review of the applications of evolutionary computation in structural design is provided and chronologically classified. Finally, a summary of the current research status and a discussion on the most promising paths of future research are also presented.
MultiObjective Optimization Using Genetic Algorithms: A Tutorial
"... abstract – Multiobjective formulations are a realistic models for many complex engineering optimization problems. Customized genetic algorithms have been demonstrated to be particularly effective to determine excellent solutions to these problems. In many reallife problems, objectives under consid ..."
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Cited by 34 (0 self)
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abstract – Multiobjective formulations are a realistic models for many complex engineering optimization problems. Customized genetic algorithms have been demonstrated to be particularly effective to determine excellent solutions to these problems. In many reallife problems, objectives under consideration conflict with each other, and optimizing a particular solution with respect to a single objective can result in unacceptable results with respect to the other objectives. A reasonable solution to a multiobjective problem is to investigate a set of solutions, each of which satisfies the objectives at an acceptable level without being dominated by any other solution. In this paper, an overview and tutorial is presented describing genetic algorithms developed specifically for these problems with multiple objectives. They differ from traditional genetic algorithms by using specialized fitness functions, introducing methods to promote solution diversity, and other approaches. 1.
Multiobjective optimization problems with complicated pareto sets
 MOEA/D and NSGAII,” Trans. Evolutionary Computation
, 2009
"... Abstract—Partly due to lack of test problems, the impact of the Pareto set (PS) shapes on the performance of evolutionary algorithms has not yet attracted much attention. This paper introduces a general class of continuous multiobjective optimization test instances with arbitrary prescribed PS shap ..."
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Cited by 22 (2 self)
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Abstract—Partly due to lack of test problems, the impact of the Pareto set (PS) shapes on the performance of evolutionary algorithms has not yet attracted much attention. This paper introduces a general class of continuous multiobjective optimization test instances with arbitrary prescribed PS shapes, which could be used for studying the ability of multiobjective evolutionary algorithms for dealing with complicated PS shapes. It also proposes a new version of MOEA/D based on differential evolution (DE), i.e., MOEA/DDE, and compares the proposed algorithm with NSGAII with the same reproduction operators on the test instances introduced in this paper. The experimental results indicate that MOEA/D could significantly outperform NSGAII on these test instances. It suggests that decomposition based multiobjective evolutionary algorithms are very promising in dealing with complicated PS shapes. Index Terms—Aggregation, decomposition, differential evolution, evolutionary algorithms, multiobjective optimization, Pareto optimality, test problems. I.
A Multiobjective Genetic Algorithm for Radio Network Optimization
 In Proceedings of the 2000 Congress on Evolutionary Computation
, 2000
"... Engineering of mobile telecommunication networks endures two major problems: the design of the network, and the frequency assignment. We address the first problem in this paper, which has been formulated as a multiobjective constrained combinatorial optimisation problem. We propose a genetic algorit ..."
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Cited by 21 (4 self)
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Engineering of mobile telecommunication networks endures two major problems: the design of the network, and the frequency assignment. We address the first problem in this paper, which has been formulated as a multiobjective constrained combinatorial optimisation problem. We propose a genetic algorithm that aims to approximate the Pareto frontier of the problem. Advanced techniques have been used such as Pareto ranking, sharing and elitism. The GA has been implemented in parallel on a network of workstations to speed up the search. To evaluate the performances of the GA, we have introduced two new quantitative indicators: the entropy and the contribution. Encouraging results are obtained on real life problems. 1 Introduction Engineering of mobile telecommunication networks evolves two major problems, the design of the network, and the frequency planning. The design consists in positioning base stations (BS) on potential sites, in order to fulfil some objectives and constraints [GZBS8...
Optimal security hardening using multiobjective optimization on attack tree models of networks
 In CCS’07
, 2007
"... Researchers have previously looked into the problem of determining if a given set of security hardening measures can effectively make a networked system secure. Many of them also addressed the problem of minimizing the total cost of implementing these hardening measures, given costs for individual m ..."
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Cited by 19 (1 self)
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Researchers have previously looked into the problem of determining if a given set of security hardening measures can effectively make a networked system secure. Many of them also addressed the problem of minimizing the total cost of implementing these hardening measures, given costs for individual measures. However, system administrators are often faced with a more challenging problem since they have to work within a fixed budget which may be less than the minimum cost of system hardening. Their problem is how to select a subset of security hardening measures so as to be within the budget and yet minimize the residual damage to the system caused by not plugging all required security holes. In this work, we develop a systematic approach to solve this problem by formulating it as a multiobjective optimization problem on an attack tree model of the system and then use an evolutionary algorithm to solve it.
Designing BGPbased outbound traffic engineering techniques for stub ASes
 Comput. Commun. Rev
, 2004
"... Today, most multiconnected autonomous systems (AS) need to control the flow of their interdomain traffic for both performance and economical reasons. This is usually done by manually tweaking the BGP configurations of the routers on an errorprone trialanderror basis. In this paper, we demonstrat ..."
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Cited by 18 (3 self)
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Today, most multiconnected autonomous systems (AS) need to control the flow of their interdomain traffic for both performance and economical reasons. This is usually done by manually tweaking the BGP configurations of the routers on an errorprone trialanderror basis. In this paper, we demonstrate that designing systematic BGPbased traffic engineering techniques for stub ASes are possible. Our approach to solve this traffic engineering problem is to allow the network operator to define objective functions on the interdomain traffic. Those objective functions are used by an optimization box placed inside the AS that controls the interdomain traffic by tuning the iBGP messages distributed inside the AS. We show that the utilization of an efficient evolutionary algorithm allows to both optimize the objective function and limit the number of iBGP messages. By keeping a lifetime on the tweaked routes, we also show that providing stability to the interdomain path followed by the traffic is possible. We evaluate the performance of solution based on traffic traces from two stub ASes of different sizes. Our simulations show that the interdomain traffic can be efficiently engineered by using not more than a few iBGP advertisements per minute. Our contribution in this paper...
A Multipleobjectives Evolutionary Perspective to Interdomain Traffic Engineering
 in the Internet,” in Workshop on Nature Inspired Approaches to Networks and Telecommunications
, 2004
"... We present an application of multipleobjectives evolutionary optimization to the problem of engineering the distribution of the interdomain traffic in the Internet. We show that this practical problem requires such a heuristic due to the potential conflicting nature of the traffic engineering objec ..."
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Cited by 13 (2 self)
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We present an application of multipleobjectives evolutionary optimization to the problem of engineering the distribution of the interdomain traffic in the Internet. We show that this practical problem requires such a heuristic due to the potential conflicting nature of the traffic engineering objectives. Furthermore, having to work on the parameter's space of the real problem makes such techniques as evolutionary optimization very easy to use. We show the successful application of our algorithm to two important problems in interdomain traffic engineering.