Comparison of methods for developing dynamic reduced models for design optimization

Khaled, R.; Ni, Xiao; Vattam, Swaroop

doi:10.1007/s00500-003-0331-x

Cited by 38 publications

(26 citation statements)

References 10 publications

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“…The main idea of the IOs is to replace pure randomness in traditional GA operators with decisions that are guided by reduced models formed using the methods presented in [5,6,7]. The reduced models are approximations of the fitness function, formed using some approximation techniques, such as least squares approximation [5,7,8].…”

Section: Objective Exchange Genetic Algorithm For Design Optimizationmentioning

confidence: 99%

Constrained Multi-objective Optimization Using Steady State Genetic Algorithms

Chafekar

Jiang

Rasheed

2003

Genetic and Evolutionary Computation — GECCO 2003

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Abstract. In this paper we propose two novel approaches for solving constrained multi-objective optimization problems using steady state GAs. These methods are intended for solving real-world application problems that have many constraints and very small feasible regions. One method called Objective Exchange Genetic Algorithm for Design Optimization (OEGADO) runs several GAs concurrently with each GA optimizing one objective and exchanging information about its objective with the others. The other method called Objective Switching Genetic Algorithm for Design Optimization (OSGADO) runs each objective sequentially with a common population for all objectives. Empirical results in benchmark and engineering design domains are presented. A comparison between our methods and Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) shows that our methods performed better than NSGA-II for difficult problems and found Pareto-optimal solutions in fewer objective evaluations. The results suggest that our methods are better applicable for solving real-world application problems wherein the objective computation time is large.

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Section: Objective Exchange Genetic Algorithm For Design Optimizationmentioning

confidence: 99%

Constrained Multi-objective Optimization Using Steady State Genetic Algorithms

Chafekar

Jiang

Rasheed

2003

Genetic and Evolutionary Computation — GECCO 2003

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“…They evaluate a large part of the population with an ANN and a small part is still simultaneously evaluated with the original function. Rasheed et al propose to cluster data and to construct separate approximation models for the different clusters [30,32,29]. The approximation model can be used each time or alternatively with the real objective function.…”

Section: Replace the Evaluation Function By A Datamining Algorithmmentioning

confidence: 99%

Using Datamining Techniques to Help Metaheuristics: A Short Survey

Jourdan

Dhaenens

Talbi

2006

Lecture Notes in Computer Science

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Abstract. Hybridizing metaheuristic approaches becomes a common way to improve the efficiency of optimization methods. Many hybridizations deal with the combination of several optimization methods. In this paper we are interested in another type of hybridization, where datamining approaches are combined within an optimization process. Hence, we propose to study the interest of combining metaheuristics and datamining through a short survey that enumerates the different opportunities of such combinations based on literature examples.

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“…A step forward to enhance the efficiency of the implemented optimization tool corresponds to the introduction of modeling techniques. The model introduced in this paper follows a supervised learning strategy based on Support Vectors Machines [9] which, together with an evolutionary strategy, are used to create "feasibility" models to efficiently prune the design search space during the optimization process, therefore, reducing the overall number of required evaluations [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Analog circuits optimization based on evolutionary computation techniques

2010

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1 -This paper presents a new design automation tool based on a modified genetic algorithm kernel, in order to increase efficiency on the analog circuit and system design cycle. It combines a robust optimization with corners, machine learning modeling and distributed processing capability able to deal with multi-objective and highly constrained optimization problems. The resulting optimization tool, simulation capabilities, and extensible architecture are presented and the improvement in design productivity is demonstrated for the design of robust CMOS operational amplifiers.

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Comparison of methods for developing dynamic reduced models for design optimization

Cited by 38 publications

References 10 publications

Constrained Multi-objective Optimization Using Steady State Genetic Algorithms

Constrained Multi-objective Optimization Using Steady State Genetic Algorithms

Using Datamining Techniques to Help Metaheuristics: A Short Survey

Analog circuits optimization based on evolutionary computation techniques

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