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17
Shape representations and evolution schemes
 Proceedings of the 5th Annual Conference on Evolutionary Programming
, 1996
"... The choice of a representation i.e. the definition of the search space, is of vital importance in all Evolutionary Optimization processes. In the context of Topological Optimum Design in Structural Mechanics, this paper investigates possible representations for evolutionary shape design. ..."
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Cited by 15 (5 self)
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The choice of a representation i.e. the definition of the search space, is of vital importance in all Evolutionary Optimization processes. In the context of Topological Optimum Design in Structural Mechanics, this paper investigates possible representations for evolutionary shape design.
Mechanical Inclusions Identification by Evolutionary Computation.
, 1996
"... The problem of the identification of mechanical inclusion is theoretically illposed, and todate numerical algorithms have demonstrated to be inaccurate and unstable. On the other hand, Evolutionary Algorithms provide a general approach to inverse problem solving. However, great care must be taken ..."
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Cited by 9 (5 self)
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The problem of the identification of mechanical inclusion is theoretically illposed, and todate numerical algorithms have demonstrated to be inaccurate and unstable. On the other hand, Evolutionary Algorithms provide a general approach to inverse problem solving. However, great care must be taken during the implementation: The choice of the representation, which determines the search space, is critical. Three representations are presented and discussed. Whereas the straightforward meshdependent representation suffers strong limitations, both meshindependent representation provide outstanding results on simple instances of the identification problem, including experimental robustness in presence of noise.
Voronoi quantized crossover for traveling salesman problem
 In Genetic and Evolutionary Computation Conference
, 2002
"... It is known that the performance of a genetic algorithm depends on the survival environment and the reproducibility of building blocks. In this paper, we propose a new encoding/crossover scheme that uses genic distance which explicitly defines the distance between each pair of genes in the chromosom ..."
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Cited by 8 (4 self)
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It is known that the performance of a genetic algorithm depends on the survival environment and the reproducibility of building blocks. In this paper, we propose a new encoding/crossover scheme that uses genic distance which explicitly defines the distance between each pair of genes in the chromosome. It pursues both relatively high survival probabilities of more epistatic gene groups and diverse crossover operators for the high creativity of new schemata. The experimental results on benchmark traveling salesman problems showed remarkable improvement in tour cost and running time over stateoftheart genetic algorithms for the problem. 1
A survey on chromosomal structures and operators for exploiting topological linkages of genes
 In Genetic and Evolutionary Computation Conference
, 2003
"... Abstract. The building block hypothesis implies that the epistatic property of a given problem must be connected well to the linkage property of the employed representation and crossover operator in the design of genetic algorithms. A good handling of building blocks has much to do with topological ..."
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Cited by 6 (3 self)
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Abstract. The building block hypothesis implies that the epistatic property of a given problem must be connected well to the linkage property of the employed representation and crossover operator in the design of genetic algorithms. A good handling of building blocks has much to do with topological linkages of genes in the chromosome. In this paper, we provide a taxonomy of the approaches that exploit topological linkages of genes. They are classified into three models: static linkage model, adaptive linkage model, and evolvable linkage model. We also provide an overview on the chromosomal structures, encodings, and operators supporting each of the models. 1
The Natural Crossover for the 2D Euclidean TSP
"... For the traveling salesman problem various search algorithms have been suggested for decades. In the field of genetic algorithms, many genetic operators have beenintroduced for the problem. Most genetic encoding schemes have some restrictions that cause moreorless loss of information contained in ..."
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Cited by 6 (4 self)
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For the traveling salesman problem various search algorithms have been suggested for decades. In the field of genetic algorithms, many genetic operators have beenintroduced for the problem. Most genetic encoding schemes have some restrictions that cause moreorless loss of information contained in problem instances. We suggest a new encoding/crossover pair which pursues minimal information loss in chromosomal encoding and minimal restriction in recombination for the 2D Euclidean traveling salesman problem. The most notable feature of the suggested crossover is that it is based on a totally new concept of encoding. We also prove the theoretical validity of the new crossover by an equivalenceclass analysis. The proposed encoding/crossover pair outperformed both distancepreserving crossover and edgeassembly crossover, two stateoftheart crossovers in the literature.
A Survey of Linkage Learning Techniques in Genetic and Evolutionary Algorithms
, 2007
"... This paper reviews and summarizes existing linkage learning techniques for genetic and evolutionary algorithms in the literature. It first introduces the definition of linkage in both biological systems and genetic algorithms. Then, it discusses the importance for genetic and evolutionary algorithms ..."
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Cited by 5 (0 self)
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This paper reviews and summarizes existing linkage learning techniques for genetic and evolutionary algorithms in the literature. It first introduces the definition of linkage in both biological systems and genetic algorithms. Then, it discusses the importance for genetic and evolutionary algorithms to be capable of learning linkage, which is referred to as the relationship between decision variables. Existing linkage learning methods proposed in the literature are reviewed according to different facets of genetic and evolutionary algorithms, including the means to distinguish between good linkage and bad linkage, the methods to express or represent linkage, and the ways to store linkage information. Studies related to these linkage learning methods and techniques are also investigated in this survey.
Identification of Mechanical Inclusions
 Evolutionary Computation in Engeneering, 477494
, 1997
"... Evolutionary Algorithms provide a general approach to inverse problem solving: As optimization methods, they only require the computation of values of the function to optimize. Thus, the only prerequisite to efficiently handle inverse problems is a good numerical model of the direct problem, and a r ..."
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Cited by 3 (3 self)
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Evolutionary Algorithms provide a general approach to inverse problem solving: As optimization methods, they only require the computation of values of the function to optimize. Thus, the only prerequisite to efficiently handle inverse problems is a good numerical model of the direct problem, and a representation for potential solutions. The identification of mechanical inclusion, even in the linear elasticity framework, is a difficult problem, theoretically illposed: Evolutionary Algorithms are in that context a good tentative choice for a robust numerical method, as standard deterministic algorithms have proven inaccurate and unstable. However, great attention must be given to the implementation. The representation, which determines the search space, is critical for a successful application of Evolutionary Algorithms to any problem. Two original representations are presented for the inclusion identification problem, together with the associated evolution operators (crossover and muta...
Exploiting Synergies of Multiple Crossovers: Initial Studies
 In Proceedings of the IEEE International Conference on Evolutionary Computation
"... Genetic algorithms (GAs) are believed to exploit the synergy between different traversals of the solution space that are afforded by crossover and mutation operators. While dozens of different crossovers are known, comparatively little attention has been devoted to improving performance by using mul ..."
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Cited by 3 (1 self)
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Genetic algorithms (GAs) are believed to exploit the synergy between different traversals of the solution space that are afforded by crossover and mutation operators. While dozens of different crossovers are known, comparatively little attention has been devoted to improving performance by using multiple crossover operators within a given GA implementation. Here, we examine various aspects of combining different crossovers; we demonstrate that mixtures of crossovers can outperform any single crossover, and that choosing appropriate mixing proportions is critical for good performance. We conjecture that good crossover mixtures are characterized by "balance" in the crossovers' respective influences in the population, and explore three adaptive strategies for mixing crossovers. 1 Introduction The crossover and mutation operators of genetic algorithms (GAs) navigate the solution space in synergetic ways: crossover generates new solutions by combining traits of alreadyvisited solutions, w...
A hybrid neurogenetic approach for stock forecasting
 IEEE Transactions on Neural Networks
, 2007
"... In this paper, we propose a hybrid neurogenetic system for stock trading. A recurrent neural network having one hidden layer is used for the prediction model. The input features are generated from a number of technical indicators being used by financial experts. The genetic algorithm optimizes the ..."
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Cited by 3 (0 self)
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In this paper, we propose a hybrid neurogenetic system for stock trading. A recurrent neural network having one hidden layer is used for the prediction model. The input features are generated from a number of technical indicators being used by financial experts. The genetic algorithm optimizes the neural network’s weights under a twodimensional encoding and crossover. We devised a contextbased ensemble method of neural networks which dynamically changes on the basis of the test day’s context. To reduce the time in processing mass data, we parallelized the genetic algorithm on a Linux cluster system using message passing interface. We tested the proposed method with 36 companies in NYSE and NASDAQ for 13 years from 1992 to 2004. The neurogenetic hybrid showed notable improvement on the average over the buyandhold strategy and the contextbased ensemble further improved the results. We also observed that some companies were more predictable than others, which implies that the proposed neurogenetic hybrid can be used for financial portfolio construction. Index Terms – Stock prediction, parallel genetic algorithm, recurrent neural network, ensemble model, message passing interface
NonParametric Identification of Geological Models
"... Many problems to be solved in geophysical processing can be expressed in terms of identification of spatial geological models : given a function OE applied to a geological model fl, producing a result R, the problem is to find fl such that OE(fl ) = R , where R is the expected result : a seismog ..."
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Cited by 2 (2 self)
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Many problems to be solved in geophysical processing can be expressed in terms of identification of spatial geological models : given a function OE applied to a geological model fl, producing a result R, the problem is to find fl such that OE(fl ) = R , where R is the expected result : a seismogram, a pressure curve, a seismic crosssection etc. The presented research deals with the joint use of evolutionary algorithms and Voronoi diagrams to address some nonparametric instances of identification problems in geophysics, i.e. without a priori hypothesis about the geometrical layout of possible solutions. In this paper, a first application in velocity determination for seismic imaging demonstrates the ability of this approach to identify both the geometry and the velocities of the underground from experimental seismograms. 1 Introduction 1.1 The geophysical problem A seismic experiment starts with an artificial explosion at some point near the surface. Elastic waves propagate thr...