evolutionary computation is a computing discipline that mimics biological evolution and genetic laws. In order to realize the modeling of the user's interest in learning, an evolutionary computation Based Modeling...
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ISBN:
(纸本)9781538669563
evolutionary computation is a computing discipline that mimics biological evolution and genetic laws. In order to realize the modeling of the user's interest in learning, an evolutionary computation Based Modeling (ECBM) method is proposed. The method uses a genetic algorithm to model the user's interest in learning and automatically evolves the model parameters. User interest modeling is the foundation and core of personalized learning.
Software Testing usually considers programs with parameters ranging over simple types. However, there are many programs using structured types. The main problem to test these programs is that it is not easy to select ...
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ISBN:
(数字)9781728169293
ISBN:
(纸本)9781728169293
Software Testing usually considers programs with parameters ranging over simple types. However, there are many programs using structured types. The main problem to test these programs is that it is not easy to select a relatively small test suite that can find most of the faults in these programs. In this paper we present a framework to generate test suites for unit testing of methods which have trees as parameters. We combine classical mutation testing with evolutionary computation techniques to evolve a population of trees. The final goal is to obtain a set of trees, representing good test cases, that will be used as the test suite to test the corresponding method.
This paper describes various methods used to encode artificial neural networks to chromosomes to be used in evolutionary computation. The target of this review is to cover the main techniques of network encoding and m...
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ISBN:
(纸本)9780956494429
This paper describes various methods used to encode artificial neural networks to chromosomes to be used in evolutionary computation. The target of this review is to cover the main techniques of network encoding and make it easier to choose one when implementing a custom evolutionary algorithm for finding the network topology. Most of the encoding methods are mentioned in the context of neural networks;however all of them could be generalized to automata networks or even oriented graphs. We present direct and indirect encoding methods, and given examples of their genotypes. We also describe the possibilities of applying genetic operators of mutation and crossover to genotypes encoded by these methods. Also, the dependencies of using special evolutionary algorithms with some of the encodings were considered.
Early lifecycle software design is an intensely human activity in which design scale and complexity can place a high cognitive load on the software designer. Recently, the use of evolutionary search has been suggested...
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ISBN:
(纸本)9781479938407
Early lifecycle software design is an intensely human activity in which design scale and complexity can place a high cognitive load on the software designer. Recently, the use of evolutionary search has been suggested to yield insights in the nature of software engineering problems generally, and so we have applied dynamic evolutionary computation using selfadaptive mutation to the object-oriented software design search space. Using three design problem instances of varying scale and complexity, initial investigations of the discrete search landscape reveal a redundancy in genotype-to-phenotype mapping enabling flexible and effective exploration. In further experiments, mutation probabilities and population diversity are observed to significantly increase in the face of increasing problem scale, but not for increasing complexity (in problems of the same scale). Based on these findings, we conclude that design problem scale rather than complexity has an effect on the software design process, emphasizing the role of decomposition as a design technique.
Solving jigsaw-puzzles has been of increasing importance in many real-world applications. The existing methods endure the problem of local or premature convergence, which perform inefficiently on some challenging imag...
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ISBN:
(纸本)9783030638320;9783030638337
Solving jigsaw-puzzles has been of increasing importance in many real-world applications. The existing methods endure the problem of local or premature convergence, which perform inefficiently on some challenging images. For an efficient optimizer of jigsaw puzzles, this paper utilizes the powerfulness of the global optimization technique and develops a multi-strategy evolution algorithm. The algorithm constantly generates jigsaw puzzle solutions by mimicking the process of natural evolution, while adopting a new objective function to evaluate the solutions. An elite-based crossover operator is designed to exploit the historically good patterns for generating competitive solutions. Then, a new mutation operator consisting of four perturbation strategies is developed to handle different puzzle situations. Experimental results verify the promising performance of the proposed algorithm that it outperforms the state-of-the-art methods on various image datasets.
In this paper, we address a novel approach to identify a nonlinear dynamic system for Wiener models, which are composed of a linear dynamic system part followed by a nonlinear static part. The aim of system identifica...
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ISBN:
(纸本)0780370872
In this paper, we address a novel approach to identify a nonlinear dynamic system for Wiener models, which are composed of a linear dynamic system part followed by a nonlinear static part. The aim of system identification here is to provide the optimal mathematical model of both the linear dynamic and the nonlinear static parts in some appropriate sense. Assuming the nonlinear static part is invertible, we approximate the inverse function by a piecewise linear function. We estimate the piecewise linear inverse function by using the evolutionary computation approach such as genetic algorithm (GA) and evolution strategies (ES), while we estimate the linear dynamic system part by the least, squares method. The results of numerical simulation studies indicate the usefulness of proposed approach to the Wiener model identification.
Initially, different areas of research in compute,science based on models inspired by Nature will be approached. The area entitled evolutionary computation is discussed in a general view. After, the emphasis is put on...
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ISBN:
(纸本)0769508626
Initially, different areas of research in compute,science based on models inspired by Nature will be approached. The area entitled evolutionary computation is discussed in a general view. After, the emphasis is put on the human tendency to copy and to find answers to new problems by adopting similar solutions of other equivalent issues already resolved by Nature. Finally, there is an attempt to demonstrate that in the case of evolutionary computation, even if success is achieved in many cases, most of what Nature has attained was either severely simplified or truncated in the simulation process. Also, in several cases a mol-e detailed and mor-e faithful copy could have yielded better results to already existing systems or to new ones.
This research explores the utility of ontological representations using object-oriented (OO) design principles, such that characteristics of the problem domain are directly mapped onto the representation of individual...
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ISBN:
(数字)9781665467087
ISBN:
(纸本)9781665467087
This research explores the utility of ontological representations using object-oriented (OO) design principles, such that characteristics of the problem domain are directly mapped onto the representation of individuals. A comparison against more traditional representations is performed in two problem domains of differing complexity: (i) Tangram, a simple geometric puzzle;and (ii) EvoRecSys, an evolutionary recommender system for health and well-being advice. We show that OO representations aid research and development as naturally decoupled components can be more easily modified and extended, which can in turn lead to the discovery of better solutions.
We use the convergence points estimated by our proposed method as elite individuals for evolutionary computation and evaluate the acceleration effect and analyze the effect and computational cost. The worst individual...
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ISBN:
(纸本)9781509006229
We use the convergence points estimated by our proposed method as elite individuals for evolutionary computation and evaluate the acceleration effect and analyze the effect and computational cost. The worst individuals in population are replaced with the convergence points estimated from the moving vectors between parent individuals and their offspring;i.e. these convergence points are used as elite individuals. Differential evolution (DE) and 14 benchmark functions are used in our evaluation experiments. The experimental results show that use of the estimated convergence points as elite can accelerate DE search in spite of the calculation cost of the convergence points. We finally analyze the components of the proposed estimation method to improve cost-performance.
Histogram feature representation is important in many classification applications for characterization of the statistical distribution of different pattern attributes, such as the color and edge orientation distributi...
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Histogram feature representation is important in many classification applications for characterization of the statistical distribution of different pattern attributes, such as the color and edge orientation distribution in images. While the construction of these feature representations is simple, this very simplicity may compromise the classification accuracy in those cases where the original histogram does not provide adequate discriminative information for making a reliable classification. In view of this, we propose an optimization approach based on evolutionary computation (Back, evolutionary algorithms in theory and practice, Oxford University Press, New York, 1996;Fogel, evolutionary computation: toward a new philosophy of machine intelligence, 2nd edn. IEEE, Piscataway, NJ 1998) to identify a suitable transformation on the histogram feature representation, such that the resulting classification performance based on these features is maximally improved while the original simplicity of the representation is retained. To facilitate this optimization process, we propose a hierarchical classifier structure to demarcate the set of categories in such a way that the pair of category subsets with the highest level of dissimilarities is identified at each stage for partition. In this way, the evolutionary search process for the required transformation can be considerably simplified due to the reduced level of complexities in classification for two widely separated category subsets. The proposed approach is applied to two problems in multimedia data classification, namely the categorization of 3D computer graphics models and image classification in the JPEG compressed domain. Experimental results indicate that the evolutionary optimization approach, facilitated by the hierarchical classification process, is capable of significantly improving the classification performance for both applications based on the transformed histogram representations.
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