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165
Methods for Evaluating and Covering the Design Space during Early Design Development
 Integration, the VLSI Journal
, 2003
"... This paper gives an overview of methods used for Design Space Exploration (DSE) at the system and microarchitecture levels. The DSE problem is considered to be two orthogonal issues: (I) How could a single design point be evaluated, (II) how could the design space be covered during the explorat ..."
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This paper gives an overview of methods used for Design Space Exploration (DSE) at the system and microarchitecture levels. The DSE problem is considered to be two orthogonal issues: (I) How could a single design point be evaluated, (II) how could the design space be covered during the exploration process? The latter question arises since an exhaustive exploration of the design space by evaluating every possible design point is usually prohibitive due to the sheer size of the design space. We therefore reveal tradeo#s linked to the choice of appropriate evaluation and coverage methods. The designer has to balance the following issues: the accuracy of the evaluation, the time it takes to evaluate one design point (including the implementation of the evaluation model), the precision/granularity of the design space coverage, and last but not least the possibilities for automating the exploration process. We also list common representations of the design space and compare current system and microarchitecture level design frameworks. This review thus eases the choice of a decent exploration policy by providing a comprehensive survey and classification of recent related work. It is focused on SystemonaChip designs, particularly those used for network processors. These systems are heterogeneous in nature using multiple computation, communication, memory, and peripheral resources.
A Tutorial on Evolutionary Multiobjective Optimization
 In Metaheuristics for Multiobjective Optimisation
, 2003
"... Mu l ip often conflicting objectives arise naturalj in most real worl optimization scenarios. As evol tionaryalAxjO hms possess several characteristics that are desirabl e for this type of probl em, this clOv of search strategies has been used for mul tiobjective optimization for more than a decade. ..."
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Cited by 78 (0 self)
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Mu l ip often conflicting objectives arise naturalj in most real worl optimization scenarios. As evol tionaryalAxjO hms possess several characteristics that are desirabl e for this type of probl em, this clOv of search strategies has been used for mul tiobjective optimization for more than a decade. Meanwhil e evol utionary mul tiobjective optimization has become establ ished as a separate subdiscipl ine combining the fiel ds of evol utionary computation and cl assical mul tipl e criteria decision ma ing. This paper gives an overview of evol tionary mu l iobjective optimization with the focus on methods and theory. On the one hand, basic principl es of mu l iobjective optimization and evol tionary alA#xv hms are presented, and various al gorithmic concepts such as fitness assignment, diversity preservation, and el itism are discussed. On the other hand, the tutorial incl udes some recent theoretical resul ts on the performance of mu l iobjective evol tionaryalvDfifl hms and addresses the question of how to simpl ify the exchange of methods and appl ications by means of a standardized interface. 1
nozPérez, Multiobjective cooperative coevolution of artificial neural networks (multiobjective cooperative networks), Neural Networks 15
, 2002
"... Abstract—This paper presents a cooperative coevolutive approach for designing neural network ensembles. Cooperative coevolution is a recent paradigm in evolutionary computation that allows the effective modeling of cooperative environments. Although theoretically, a single neural network with a suf ..."
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Cited by 46 (4 self)
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Abstract—This paper presents a cooperative coevolutive approach for designing neural network ensembles. Cooperative coevolution is a recent paradigm in evolutionary computation that allows the effective modeling of cooperative environments. Although theoretically, a single neural network with a sufficient number of neurons in the hidden layer would suffice to solve any problem, in practice many realworld problems are too hard to construct the appropriate network that solve them. In such problems, neural network ensembles are a successful alternative. Nevertheless, the design of neural network ensembles is a complex task. In this paper, we propose a general framework for designing neural network ensembles by means of cooperative coevolution. The proposed model has two main objectives: first, the improvement of the combination of the trained individual networks; second, the cooperative evolution of such networks, encouraging collaboration among them, instead of a separate training of each network. In order to favor the cooperation of the networks, each network is evaluated throughout the evolutionary process using a multiobjective method. For each network, different objectives are defined, considering not only its performance in the given problem, but also its cooperation with the rest of the networks. In addition, a population of ensembles is evolved, improving the combination of networks and obtaining subsets of networks to form ensembles that perform better than the combination of all the evolved networks. The proposed model is applied to ten realworld classification problems of a very different nature from the UCI machine learning repository and proben1 benchmark set. In all of them the performance of the model is better than the performance of standard ensembles in terms of generalization error. Moreover, the size of the obtained ensembles is also smaller. Index Terms—Classification, cooperative coevolution, multiobjective optimization, neural network ensembles. I.
Minimum spanning trees made easier via multiobjective optimization
 In Proceedings of GECCO’05
, 2005
"... Many realworld problems are multiobjective optimization problems and evolutionary algorithms are quite successful on such problems. Since the task is to compute or approximate the Pareto front,multiobjective optimization problems are considered as more difficult than singleobjective problems. On ..."
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Cited by 43 (21 self)
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Many realworld problems are multiobjective optimization problems and evolutionary algorithms are quite successful on such problems. Since the task is to compute or approximate the Pareto front,multiobjective optimization problems are considered as more difficult than singleobjective problems. One should not forget that the fitness vector with respect to more than one objective contains more information that in principle can direct the search of evolutionary algorithms. Therefore,it is possible that a singleobjective problem can be solved more efficiently via a generalized multiobjective model of the problem. That this is indeed the case is proved by investigating the computation of minimum spanning trees.
Solving rotated multiobjective optimization problems using Differential Evolution
 In AI 2004: Advances in Artificial Intelligence: 17th Australian Joint Conference on Artificial Intelligence
, 2004
"... Abstract. This paper demonstrates that the selfadaptive technique of Differential Evolution (DE) can be simply used for solving a multiobjective optimization problem where parameters are interdependent. The realcoded crossover and mutation rates within the NSGAII have been replaced with a simple ..."
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Cited by 41 (4 self)
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Abstract. This paper demonstrates that the selfadaptive technique of Differential Evolution (DE) can be simply used for solving a multiobjective optimization problem where parameters are interdependent. The realcoded crossover and mutation rates within the NSGAII have been replaced with a simple Differential Evolution scheme, and results are reported on a rotated problem which has presented difficulties using existing Multiobjective Genetic Algorithms. The Differential Evolution variant of the NSGAII has demonstrated rotational invariance and superior performance over the NSGAII on this problem. 1
Faster SMetric Calculation by Considering Dominated Hypervolume as Klee’s Measure Problem
, 2006
"... The dominated hypervolume (or Smetric) is a commonly accepted quality measure for comparing approximations of Pareto fronts generated by multiobjective optimizers. Since optimizers exist, namely evolutionary algorithms, that use the Smetric internally several times per iteration, a faster determi ..."
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Cited by 40 (2 self)
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The dominated hypervolume (or Smetric) is a commonly accepted quality measure for comparing approximations of Pareto fronts generated by multiobjective optimizers. Since optimizers exist, namely evolutionary algorithms, that use the Smetric internally several times per iteration, a faster determination of the Smetric value is of essential importance. This paper describes how to consider the Smetric as a special case of a more general geometrical problem called Klee’s measure problem (KMP). For KMP, an algorithm exists with run time O(n logn + n d/2 log n), for n points of d ≥ 3 dimensions. This complex algorithm is adapted to the special case of calculating the Smetric. Conceptual simplifications of the implementation are concerned that save on a factor of O(logn) and establish an upper bound of O(n logn + n d/2) for the Smetric calculation, improving the previously known bound of O(n d−1).
Techniques for highly multiobjective optimisation: Some nondominated points are better than others
 in Proceedings GECCO 2007. ACM
, 2007
"... The research area of evolutionary multiobjective optimization (EMO) is reaching better understandings of the properties and capabilities of EMO algorithms, and accumulating much evidence of their worth in practical scenarios. An urgent emerging issue is that the favoured EMO algorithms scale poorly ..."
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Cited by 33 (1 self)
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The research area of evolutionary multiobjective optimization (EMO) is reaching better understandings of the properties and capabilities of EMO algorithms, and accumulating much evidence of their worth in practical scenarios. An urgent emerging issue is that the favoured EMO algorithms scale poorly when problems have ‘many ’ (e.g. five or more) objectives. One of the chief reasons for this is believed to be that, in manyobjective EMO search, populations are likely to be largely composed of nondominated solutions. In turn, this means that the commonlyused algorithms cannot distinguish between these for selective purposes. However, there are methods that can be used validly to rank points in a nondominated set, and may therefore usefully underpin selection in EMO search. Here we discuss and compare several such methods. Our main finding is that simple variants of the oftenoverlooked ‘Average Ranking ’ strategy usually outperform other methods tested, covering problems with 5—20 objectives and differing amounts of interobjective correlation. Categories and Subject Descriptors I.2.8 [Problem solving, control methods and search]: Heuristic methods
Improving PSObased multiobjective optimization using crowding, mutation and � dominance
 In EMO’2005, pages 505–519. LNCS 3410
, 2005
"... Abstract. In this paper, we propose a new MultiObjective Particle Swarm Optimizer, which is based on Pareto dominance and the use of a crowding factor to filter out the list of available leaders. We also propose the use of different mutation (or turbulence) operators which act on different subdivis ..."
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Cited by 32 (2 self)
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Abstract. In this paper, we propose a new MultiObjective Particle Swarm Optimizer, which is based on Pareto dominance and the use of a crowding factor to filter out the list of available leaders. We also propose the use of different mutation (or turbulence) operators which act on different subdivisions of the swarm. Finally, the proposed approach also incorporates the ¡dominance concept to fix the size of the set of final solutions produced by the algorithm. Our approach is compared against five stateoftheart algorithms, including three PSObased approaches recently proposed. The results indicate that the proposed approach is highly competitive, being able to approximate the front even in cases where all the other PSObased approaches fail. 1
Approximating the least hypervolume contributor: NPhard in general, but fast in practice
, 2008
"... ..."
An algebra of Pareto points
 Fundamenta Informaticae
, 2005
"... Multicriteria optimisation problems occur naturally in many engineering practices. Pareto analysis has proven to be a powerful tool to characterise potentially interesting realisations of a particular engineering problem. It is therefore used frequently for designspace exploration problems. Depend ..."
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Cited by 24 (10 self)
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Multicriteria optimisation problems occur naturally in many engineering practices. Pareto analysis has proven to be a powerful tool to characterise potentially interesting realisations of a particular engineering problem. It is therefore used frequently for designspace exploration problems. Depending on the optimisation goals, one of the Paretooptimal alternatives will be the optimal realisation. It often happens however, that partial design decisions have to be taken, leaving other aspects of the optimisation problem to be decided at a later stage, and that Paretooptimal configurations have to be composed (dynamically) from Paretooptimal configurations of components. These aspects are not supported by current analysis methods. This paper introduces a novel, algebraic approach to Pareto analysis. The approach is particularly designed to allow for describing incremental design decisions and composing sets of Paretooptimal configurations. The algebra can be used to study the operations on Pareto sets and the efficient computation of Pareto sets and their compositions. The algebra is illustrated with a casestudy based on transmitting an MPEG4 video stream from a server to a handheld device. 1