A Genetic Algorithm with Entropy Based Probabilistic Initialization and Memory for Automated Rule Mining

Saroj, .; Kapila,; Kumar, Dinesh; Kanika, .

doi:10.1007/978-3-642-17857-3_60

Cited by 8 publications

(4 citation statements)

References 11 publications

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“…Nevertheless, it is unknown if this could be generalized to different problems. Also, Saroj et al (2011) extended a GA by incorporating a form of entropy-based probabilistic initialization to automate the process of rule mining.…”

Section: Generating Diversified Sequences Of Random Solutionsmentioning

confidence: 99%

Initialization of metaheuristics: comprehensive review, critical analysis, and research directions

Sarhani

Voß

Jovanović

2022

Int Trans Operational Res

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Initialization of metaheuristics is a crucial topic that lacks a comprehensive and systematic review of the state of the art. Providing such a review requires in‐depth study and knowledge of the advances and challenges in the broader field of metaheuristics, especially with regard to diversification strategies, in order to assess the proposed methods and provide insights for initialization. Motivated by the aforementioned research gap, we provide a related review and begin by describing the main metaheuristic methods and their diversification mechanisms. Then, we review and analyze the existing initialization approaches while proposing a new categorization of them. Next, we focus on challenging optimization problems, namely constrained and discrete optimization. Lastly, we give insights on the initialization of local search approaches.

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Section: Generating Diversified Sequences Of Random Solutionsmentioning

confidence: 99%

Initialization of metaheuristics: comprehensive review, critical analysis, and research directions

Sarhani

Voß

Jovanović

2022

Int Trans Operational Res

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“…However, if the population is randomly initialized, data will be uniformly distributed, and the uniform distribution will increase uncertainty. Saroj et al [44] also mentioned that the poor initial population increases the running time for seeking optimal solutions. In other words, in this study, the initial population must be set up with various data so that the speed of finding optimal solutions can be improved.…”

Section: Comparison Of Item Parameters Based On Item Responsementioning

confidence: 99%

Applying Genetic Algorithm to Generation of High-Dimensional Item Response Data

Kim

Lee

2015

Mathematical Problems in Engineering

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The item response data is thenm-dimensional data based on the responses made bymexaminees to the questionnaire consisting ofnitems. It is used to estimate the ability of examinees and item parameters in educational evaluation. For estimates to be valid, the simulation input data must reflect reality. This paper presents the effective combination of the genetic algorithm (GA) and Monte Carlo methods for the generation of item response data as simulation input data similar to real data. To this end, we generated four types of item response data using Monte Carlo and the GA and evaluated how similarly the generated item response data represents the real item response data with the item parameters (item difficulty and discrimination). We adopt two types of measurement, which are root mean square error and Kullback-Leibler divergence, for comparison of item parameters between real data and four types of generated data. The results show that applying the GA to initial population generated by Monte Carlo is the most effective in generating item response data that is most similar to real item response data. This study is meaningful in that we found that the GA contributes to the generation of more realistic simulation input data.

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“…A better fit initial population is likely to converge to more reliable rule set with high predictive accuracy in lesser number of generations, consecutively, enhances the performance of genetic algorithm for rule mining techniques. Also, in data mining applications with large datasets, this approach is anticipated to reduce the number of fitness evaluations significantly resulting into a considerable gain on efficiency front [37].…”

Section: Initializationmentioning

confidence: 99%

Mining Comprehensible and Interesting Rules: A Genetic Algorithm Approach

Vashishtha¹,

Kumar²,

Ratnoo³

et al. 2011

IJCA

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A majority of contribution in the domain of rule mining overemphasize on maximizing the predictive accuracy of the discovered patterns. The user-oriented criteria such as comprehensibility and interestingness are have been given secondary importance. Recently, it has been widely acknowledged that even highly accurate discovered knowledge might be worthless if it scores low on the qualitative parameters of comprehensibility and interestingness. This paper presents a classification algorithm based on evolutionary approach that discovers comprehensible and interesting in CNF form in which along with conjunction in between various attributes there is disjunction among the values of an attribute. A flexible encoding scheme, genetic operators with appropriate syntactic constraints and a suitable fitness function to measure the goodness of rules are proposed for effective evolution of rule sets. The proposed genetic algorithm is validated on several datasets of UCI data set repository and experimental results are presented which clearly indicate lower error rates and more comprehensibility across a range of datasets. Some of the rules show the interesting and valuable nuggets of knowledge discovered from small disjuncts of high accuracy and low support which are very difficult to capture otherwise.

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A Genetic Algorithm with Entropy Based Probabilistic Initialization and Memory for Automated Rule Mining

Cited by 8 publications

References 11 publications

Initialization of metaheuristics: comprehensive review, critical analysis, and research directions

Initialization of metaheuristics: comprehensive review, critical analysis, and research directions

Applying Genetic Algorithm to Generation of High-Dimensional Item Response Data

Mining Comprehensible and Interesting Rules: A Genetic Algorithm Approach

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