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11
A Comprehensive Survey of Fitness Approximation in Evolutionary Computation
, 2003
"... Evolutionary algorithms (EAs) have received increasing interests both in the academy and industry. One main difficulty in applying EAs to realworld applications is that EAs usually need a large number of fitness evaluations before a satisfying result can be obtained. However, fitness evaluations ar ..."
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Cited by 96 (6 self)
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Evolutionary algorithms (EAs) have received increasing interests both in the academy and industry. One main difficulty in applying EAs to realworld applications is that EAs usually need a large number of fitness evaluations before a satisfying result can be obtained. However, fitness evaluations are not always straightforward in many realworld applications. Either an explicit fitness function does not exist, or the evaluation of the fitness is computationally very expensive. In both cases, it is necessary to estimate the fitness function by constructing an approximate model. In this paper, a comprehensive survey of the research on fitness approximation in evolutionary computation is presented. Main issues like approximation levels, approximate model management schemes, model construction techniques are reviewed. To conclude, open questions and interesting issues in the field are discussed.
Constrained MultiObjective Optimization Using Steady State Genetic Algorithms
 In Proceedings of Genetic and Evolutionary Computation Conference
, 2003
"... In this paper we propose two novel approaches for solving constrained multiobjective optimization problems using steady state GAs. ..."
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Cited by 10 (0 self)
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In this paper we propose two novel approaches for solving constrained multiobjective optimization problems using steady state GAs.
Surrogate based Evolutionary Algorithm for Engineering Design Optimization
 Proceedings of the Eighth International Conference on Cybernetics, Informatics and Systemic (ICCIS 2005), ISBN
"... Abstract—Optimization is often a critical issue for most system design problems. Evolutionary Algorithms are populationbased, stochastic search techniques, widely used as efficient global optimizers. However, finding optimal solution to complex high dimensional, multimodal problems often require hi ..."
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Cited by 3 (3 self)
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Abstract—Optimization is often a critical issue for most system design problems. Evolutionary Algorithms are populationbased, stochastic search techniques, widely used as efficient global optimizers. However, finding optimal solution to complex high dimensional, multimodal problems often require highly computationally expensive function evaluations and hence are practically prohibitive. The Dynamic Approximate Fitness based Hybrid EA (DAFHEA) model presented in our earlier work [14] reduced computation time by controlled use of metamodels to partially replace the actual function evaluation by approximate function evaluation. However, the underlying assumption in DAFHEA is that the training samples for the metamodel are generated from a single uniform model. Situations like model formation involving variable input dimensions and noisy data certainly can not be covered by this assumption. In this paper we present an enhanced version of DAFHEA that incorporates a multiplemodel based learning approach for the SVM approximator. DAFHEAII (the enhanced version of the DAFHEA framework) also overcomes the high computational expense involved with additional clustering requirements of the original DAFHEA framework. The proposed framework has been tested on several benchmark functions and the empirical results illustrate the advantages of the proposed technique. Keywords—Evolutionary algorithm, Fitness function, Optimization, Metamodel, Stochastic method.
Using singular value decomposition to improve a genetic algorithm’s performance
 In Proceedings of the 2003 Congress on Evolutionary Computation CEC2003
, 2003
"... Abstract The focus of this work is to investigate the effects of applying the singular value decomposition (SVD), a linear algebra technique, to the domain of Genetic Algorithms. Empirical evidence, concerning document comparison, suggests that the SVD can be used to model information in such a way ..."
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Cited by 2 (2 self)
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Abstract The focus of this work is to investigate the effects of applying the singular value decomposition (SVD), a linear algebra technique, to the domain of Genetic Algorithms. Empirical evidence, concerning document comparison, suggests that the SVD can be used to model information in such a way that provides both a saving in storage space and an improvement in information retrieval. It will be shown that these beneficial properties can be extended to many other different types of comparison as well. Briefly, vectors representing the genes of individuals are projected into a new lowdimensional space, obtained by the singular value decomposition of a geneindividual matrix. The information about what it means to be a good or bad individual serves as a basis for qualifying candidate individuals for reinsertion into the next generation. Positive results from different approaches of this application are presented and evaluated. In addition, several possible alternative techniques are proposed and considered. expose the most strikinz similarities between a given individual and a strategically chosen population of individuals. These similarities are used to influence the direction of the GAS search process by qualifying candidate individuals for reinsertion into the next generation based on their proximity to other individuals, whose fitnesses have already been computed. Initial results from the application of this process indicate significant improvements in the CA’s performance. The intent is to evaluate several different tested approaches of using SVD qualifiers to enhance the performance of GAS, justify any apparent performance improvement over traditional GAS, and to speculate about other ways of using SVD in GAS. In the remainder of this paper we provide some background information in Section 2, followed by a description of the proposed method for SVD integration into a genetic algorithm in Section 3. Section 4 describes the results achieved by using the proposed method in several different problem domains. Finally. Section 5 provides some promising opportunities for future research.
Improving Performance of Genetic Algorithms by Using Novel Fitness Functions
, 2004
"... This item was submitted to Loughborough’s Institutional Repository by the ..."
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Cited by 1 (0 self)
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This item was submitted to Loughborough’s Institutional Repository by the
Constrained MultiObjective GA Optimization Using Reduced Models
"... In this paper we propose a novel approach for solving constrained multiobjective optimization problems using a steady state GA and reduced models. Our method called Objective Exchange Genetic Algorithm for Design optimization (OEGADO) is intended for solving realworld application problems that hav ..."
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Cited by 1 (0 self)
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In this paper we propose a novel approach for solving constrained multiobjective optimization problems using a steady state GA and reduced models. Our method called Objective Exchange Genetic Algorithm for Design optimization (OEGADO) is intended for solving realworld application problems that have many consmints and very small feasible regions. OEGADO runs several GAs concurrently with each GA optimizing one objective and exchanging information about its objective with others. Empirical results in benchmark and engineering design domains are presented. A comparison between OEGADO and NonDominated Sorting Genetic AlgorithmII (NSGAII) shows that OEGADO performed better than NSGAII for difficult problems, and found Paretooptimal solutions in fewer objective evaluations. The results suggest that our method may be better for solving realworld application problems wherein the objective computation time is large.
Expensive Optimization, Uncertain Environment: An EABased Solution
"... Real life optimization problems often require finding optimal solution to complex high dimensional, multimodal problems involving computationally very expensive fitness function evaluations. Use of any population based iterative technique such as evolutionary algorithm in such problem domains is thu ..."
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Cited by 1 (0 self)
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Real life optimization problems often require finding optimal solution to complex high dimensional, multimodal problems involving computationally very expensive fitness function evaluations. Use of any population based iterative technique such as evolutionary algorithm in such problem domains is thus practically prohibitive. A feasible alternative is to build surrogates or use an approximation of the actual fitness functions to be evaluated. Naturally these surrogate or meta models are order of magnitude cheaper to evaluate compared to the actual function evaluation. This paper presents two evolutionary algorithm frameworks which involve surrogate based fitness function evaluation. The first framework, namely the Dynamic Approximate Fitness based Hybrid EA (DAFHEA) model [1] reduces computation time by controlled use of metamodels (in this case approximate model generated by Support Vector Machine regression) to partially replace the actual function evaluation by approximate function evaluation. However, the underlying assumption in DAFHEA is that the training samples for the metamodel are generated from a single uniform model. This does not take into account problem domains involving uncertain environment. The second model, DAFHEAII, an enhanced version of the original DAFHEA framework, incorporates a multiplemodel based learning approach for the support vector machine approximator to handle uncertain environment [2]. Empirical evaluation results have been presented based on application of the frameworks to commonly used benchmark functions.
Machine Learning Techniques for the Evaluation of External Skeletal Fixation Structures
"... In this paper we compare several machine learning techniques for evaluating external skeletal fixation proposals. We experimented in the context of dog bone fractures but the potential applications are numerous. Decision trees tend to give both binary and multiple class prediction quickly and accura ..."
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In this paper we compare several machine learning techniques for evaluating external skeletal fixation proposals. We experimented in the context of dog bone fractures but the potential applications are numerous. Decision trees tend to give both binary and multiple class prediction quickly and accurately. The classifier system method does worse due to the small size of the data set and missing values. The use of artificial Neural Networks is promising, although it takes more time in training. A Genetic Algorithm is also employed to find the best structure of the Neural Network. Experimental results for the different methods are presented and compared. 1.
electrical impedance tomography
, 2005
"... A reduced model technique based on polynomial approximations is developed in order to increase the rate of convergence of an evolution strategy (ES) when solving a nondestructive evaluation problem. The inverse problem investigated consists of identifying the geometry of discontinuities in a conduc ..."
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A reduced model technique based on polynomial approximations is developed in order to increase the rate of convergence of an evolution strategy (ES) when solving a nondestructive evaluation problem. The inverse problem investigated consists of identifying the geometry of discontinuities in a conductive material from Cauchy data measurements taken on the boundary. In this study we use polynomial approximation models in order to increase the rate of convergence of the optimisation algorithm and to e#ciently detect, from a computational time point of view, a subsurface cavity, such as a circle. The algorithm developed by combining evolution strategies and polynomial approximations is found to be a robust, fast and e#cient method for detecting the size and location of subsurface cavities.
Meta Model Based EA for Complex Optimization
"... Evolutionary Algorithms are populationbased, stochastic search techniques, widely used as efficient global optimizers. However, many real life optimization problems often require finding optimal solution to complex high dimensional, multimodal problems involving computationally very expensive fitn ..."
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Evolutionary Algorithms are populationbased, stochastic search techniques, widely used as efficient global optimizers. However, many real life optimization problems often require finding optimal solution to complex high dimensional, multimodal problems involving computationally very expensive fitness function evaluations. Use of evolutionary algorithms in such problem domains is thus practically prohibitive. An attractive alternative is to build meta models or use an approximation of the actual fitness functions to be evaluated. These meta models are order of magnitude cheaper to evaluate compared to the actual function evaluation. Many regression and interpolation tools are available to build such meta models. This paper briefly discusses the architectures and use of such metamodeling tools in an evolutionary optimization context. We further present two evolutionary algorithm frameworks which involve use of meta models for fitness function evaluation. The first framework, namely the Dynamic Approximate Fitness based Hybrid EA (DAFHEA) model [14] reduces computation time by controlled use of metamodels (in this case approximate model generated by Support Vector Machine regression) to partially replace the actual function evaluation by approximate function evaluation. However, the underlying assumption in DAFHEA is that the training samples for the metamodel are generated from a single uniform model. This does not take into account uncertain scenarios involving noisy fitness functions. The second model, DAFHEAII, an enhanced version of the original DAFHEA framework, incorporates a multiplemodel based learning approach for the support vector machine approximator to handle noisy functions [15]. Empirical results obtained by evaluating the frameworks using several benchmark functions demonstrate their efficiency