Journal Article•
An Adaptive Genetic Algorithm and Application in a Luggage Design Center
TL;DR: The methodology provides the novel function of adaptive parameter adjustment during each evolution generation of GA to enhance search efficiency towards optimal solutions and improve search effectiveness and algorithm robustness.
Abstract: This paper presents a new methodology for improving the efficiency and generality of Genetic Algorithms (GA). The methodology provides the novel function of adaptive parameter adjustment during each evolution generation of GA. The important characteristics of the methodology are mainly from the following two aspects: (1) superior performance members in GA are preserved and inferior performance members are deteriorated to enhance search efficiency towards optimal solutions; (2) adaptive crossover and mutation management is applied in GA based on the transformation functions to explore wider spaces so as to improve search effectiveness and algorithm robustness. The research was successfully applied for a luggage design chain to generate optimal solutions (minimized lifecycle cost). Experiments were conducted to compare the work with the prior art to demonstrate the characteristics and advantages of the research.
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TL;DR: A dynamic grey platform to modify the traditional algorithms by applying two new prediction algorithms for forecasting management and reveals that the better prediction accuracy reduces the cost of Taiwan's green gross domestic product (GDP).
14 citations
Journal Article•
TL;DR: In this paper, a practical solution for data grounding is introduced and a mapping language to relate data structures from services definition in WSDL documents to concepts, properties and instances of a business domain is presented.
Abstract: Grounding is the process in charge of linking requests and responses of web services with the semantic web services execution platform, and it is the key activity to automate their execution in a real business environment. In this paper, the authors introduce a practical solution for data grounding. On the one hand, we need a mapping language to relate data structures from services definition in WSDL documents to concepts, properties and instances of a business domain. On the other hand, two functions that perform the lowering and lifting processes using these mapping specifications are also presented.
10 citations
TL;DR: The proposed adaptive mechanism for improving the availability efficiency of green component design (GCD) process incorporates a wide range of GCD strategies to increase availability of the recycled/reused/remanufactured components.
Abstract: This paper proposes an adaptive mechanism for improving the availability efficiency of green component design (GCD) process. The proposed approach incorporates a wide range of GCD strategies to increase availability of the recycled/reused/remanufactured components. We have also designed a self-adjusting mechanism to enhance the versatility and generality of a genetic algorithm (GA) to improve GCD availability efficiency. The mechanism allows refinement of the GA parameters for the selections of operators in each generation. Our research contribution includes the development of a novel mechanism for the evaluation of optimal selections of reproduction strategies, adjustment and optimization of the crossover and mutation rates in evolutions, and design of Taguchi Orthogonal Arrays with a GA optimizer. The effectiveness of the proposed algorithms has been examined in a GCD chain. From the experimental results, we can conclude that the proposed approach resulted in better reproduction optimization than the traditional ones.
5 citations
Cites background or methods from "An Adaptive Genetic Algorithm and A..."
...Another challenge in the GCD optimization processes is the existing methods with the limited search ability due to large and complex search space (Tsai et al. 2011; Yu 2012; Sbihi and Eglese 2010)....
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...There are two major factors that influence GA explorations: population diversity and selective pressure (Tsai et al. 2011)....
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Journal Article•
TL;DR: The experiments described in the paper show that the focus is on reducing the problems arising from data sparsity, one of the main difficulties for CF algorithms, has a significantly positive impact on prediction accuracy, particularly when the user-item matrix is sparse.
Abstract: This paper addresses issues related to recommending Semantic Web Services (SWS) using collaborative filtering (CF). The focus is on reducing the problems arising from data sparsity, one of the main difficulties for CF algorithms. Two CF algorithms are presented and discussed: a memory-based algorithm, using the k-NN method, and a model-based algorithm, using the k-means method. In both algorithms, similarity between users is computed using the Pearson Correlation Coefficient (PCC). One of the limitations of using the PCC in this context is that in those instances where users have not rated items in common it is not possible to compute their similarity. In addition, when the number of common items that were rated is low, the reliability of the computed similarity degree may also be low. To overcome these limitations, the presented algorithms compute the similarity between two users taking into account services that both users accessed and also semantically similar services. Likewise, to predict the rating for a not yet accessed target service, the algorithms consider the ratings that neighbor users assigned to the target service, as is normally the case, while also considering the ratings assigned to services that are semantically similar to the target service. The experiments described in the paper show that this approach has a significantly positive impact on prediction accuracy, particularly when the user-item matrix is sparse.
4 citations
TL;DR: Information retrieval techniques are brought in and applied to intent resolving and an adaptive intent resolving scheme is designed which is integrated into the Intents user agent developed in a previous project.
Abstract: Service discovery and integration is an important research area with efforts invested to explore the potential advantages of collaborative computing in general and service-oriented computing in particular. However, current technologies still limit their application within the reach of enterprise systems or privately available services. Intents is an emerging and innovative technique aimed to discover and integrate publically available services. In Intents, intent message resolving is a critical step which is deemed to decide the quality of the whole system. However, existing schemes applied in intent resolving adopt the exact-matching strategy which may rule out services desired by the user. This paper brings in information retrieval techniques and applies them to intent resolving. We take an empirical approach through extensive experiments and analyses on a real dataset to obtain guiding principles. Based on the resulting principles, an adaptive intent resolving scheme is designed. Afterwards, we integrate the scheme into the Intents user agent developed in a previous project.
3 citations
References
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TL;DR: This tutorial covers the canonical genetic algorithm as well as more experimental forms of genetic algorithms, including parallel island models and parallel cellular genetic algorithms.
Abstract: This tutorial covers the canonical genetic algorithm as well as more experimental forms of genetic algorithms, including parallel island models and parallel cellular genetic algorithms. The tutorial also illustrates genetic search by hyperplane sampling. The theoretical foundations of genetic algorithms are reviewed, include the schema theorem as well as recently developed exact models of the canonical genetic algorithm.
3,967 citations
"An Adaptive Genetic Algorithm and A..." refers methods in this paper
...In [Whitley 94], the Hamming distance between parent solutions was used to improve GA performance but this work was most applicable to steady state GA with elitism....
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01 Apr 1994
TL;DR: An efficient approach for multimodal function optimization using genetic algorithms (GAs) and the use of adaptive probabilities of crossover and mutation to realize the twin goals of maintaining diversity in the population and sustaining the, convergence capacity of the GA are described.
Abstract: In this paper we describe an efficient approach for multimodal function optimization using genetic algorithms (GAs). We recommend the use of adaptive probabilities of crossover and mutation to realize the twin goals of maintaining diversity in the population and sustaining the, convergence capacity of the GA. In the adaptive genetic algorithm (AGA), the probabilities of crossover and mutation, p/sub c/ and p/sub m/, are varied depending on the fitness values of the solutions. High-fitness solutions are 'protected', while solutions with subaverage fitnesses are totally disrupted. By using adaptively varying p/sub c/ and p/sub ,/ we also provide a solution to the problem of deciding the optimal values of p/sub c/ and p/sub m/, i.e., p/sub c/ and p/sub m/ need not be specified at all. The AGA is compared with previous approaches for adapting operator probabilities in genetic algorithms. The Schema theorem is derived for the AGA, and the working of the AGA is analyzed. We compare the performance of the AGA with that of the standard GA (SGA) in optimizing several nontrivial multimodal functions with varying degrees of complexity. >
2,359 citations
Proceedings Article•
01 Dec 1989
1,035 citations
"An Adaptive Genetic Algorithm and A..." refers methods in this paper
...However, effective rules and methodologies to choose suitable parameters have not been developed [Angelova and Pencheva 11] [Schwefel et al. 89] [Felix et al. 08]....
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Proceedings Article•
18 Aug 1985TL;DR: New techniques for applying adaptive algorithms to epistatic domains, while retaining some of the strength of Holland's convergence proof are described, for two-dimensional bin-packing problems and graph coloring problems.
Abstract: John Holland has shown that when adaptive algorithms are used to search certain kinds of extremely large problem spaces, they will converge on a "good" solution fairly quickly. Such problem spaces are characterized by a low degree of epistasis. A host of classical search problems, however, are epistatic in nature. The present paper describes some new techniques for applying adaptive algorithms to epistatic domains, while retaining some of the strength of Holland's convergence proof. These techniques are described for two-dimensional bin-packing problems, and summarized for graph coloring problems. What makes these problems amenable to an adaptive approach is a two-stage evaluation procedure. Encodings of solutions are mutated and reproduced as they are in non-epistatic domains, but their evaluation is carried out after a decoding process. Using the techniques described, convergence is promoted in two ways: one of the natural mutation operators is a weaker version of Holland's crossover, and domain knowledge may be built into decoding processes so that the size of the search space is radically cut down.
745 citations
"An Adaptive Genetic Algorithm and A..." refers methods in this paper
...For instance, research of adapting operator probabilities based on the performance of the operators in GA was conducted [Kapoor et al. 10] [Davis 85] [Tahera et al. 08]....
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Proceedings Article•
01 Oct 1987
259 citations
"An Adaptive Genetic Algorithm and A..." refers background in this paper
...In [Schaffer and Morishima 87], a punctuated crossover operator with cost penalty was applied for specific problems....
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