A Time-Critical Investigation of Parameter Tuning in Differential Evolution for Non-Linear Global Optimization

Ong, Jia Hui; Teo, Jason

doi:10.18517/ijaseit.6.4.799

Cited by 3 publications

(3 citation statements)

References 12 publications

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“…Since fraud is seen as a failure of monitoring among the staffs by the management, the management may opt in the finding the cause by using the optimization problem-solving. The differential evolution as one of the approaches under parameter searching may help any companies to find the reasons for the problems as early as possible [28].…”

Section: Discussionmentioning

confidence: 99%

Credit Card Detection System Based on Ridit Approach

Tukiman¹,

Ahmad²,

Saad³

et al. 2017

International Journal on Advanced Science, Engineering and Information Technology

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Fraud detection is one of the important agendas in financial and insurance institutions to protect the institutions from fraudsters and loss. The losses to the financial institutions are huge, and the need to detect the fraud at an early stage is critical to the institutions. If the numbers of fraud are not properly managed, the impact may lead to the closure of the institutions. Many predictive analytic systems or models have been proposed to identify and detect the frauds. Hence, this paper examines the effect of different response variables of credit card history as the reference group which used an unsupervised scoring method namely an Identified Distribution (RIDIT) based on a statistically significant test. We illustrate the method using German Credit card dataset retrieved from UCI Machine Learning Data System. The result generates scores and significant value of chi-square test that reflect response variables being classified as reference group or comparison groups, which more or less affected by the response credit card history in fraud detection.

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Section: Discussionmentioning

confidence: 99%

Credit Card Detection System Based on Ridit Approach

Tukiman¹,

Ahmad²,

Saad³

et al. 2017

International Journal on Advanced Science, Engineering and Information Technology

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“…DE is one of the population-based algorithm used to solve many optimization problems. The advantages of using DE include for its ease of use, speed, a simple structure where it used the real number and its efficiency a robustness over many problems [19]- [22]. DE operates by maintaining a population of candidate solutions known as chromosome for an optimization problem.…”

Section: A Differential Evolution Algorithmmentioning

confidence: 99%

Multi-objective Optimization of Biochemical System Production Using an Improve Newton Competitive Differential Evolution Method

Ismail¹,

Mezhuyev²,

Deris³

et al. 2017

International Journal on Advanced Science, Engineering and Information Technology

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In this paper, an improved method of multi-objective optimization for biochemical system production is presented and discussed in detail. The optimization process of biochemical system production become hard and difficult when involved a large biochemical system that contains many components. In addition, the multi-objective problem also needs to be considered. Due to that, this study proposed and improved a method that comprises with Newton method, differential evolution algorithm (DE) and competitive co-evolutionary algorithm(ComCA). The aim of the proposed method is to maximize the production and simultaneously minimize the total amount of chemical concentrations involves. The operation of the proposed method starts with Newton method by dealing with biochemical system production as a nonlinear equations system. Then DE and ComCA are used to represent the variables in nonlinear equation system and tune the variables in order to find the best solution. The used of DE is to maximize the production while ComCA is to minimize the total amount of chemical concentrations involves. The effectiveness of the proposed method is evaluated using two benchmark biochemical systems, and the experimental results show that the proposed method performs well compared to other works.

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“…XDE algorithm [1] was recently proposed as an improvement over the original DE algorithm [2,18,19]. It proposes a novel reversal of genetic operations in the original DE algorithm.…”

Section: A Related Materialsmentioning

confidence: 99%

Analyzing the Scalability Performance of Crossover-First and Self-Adaptive Differential Evolution Algorithms for Complex Numerical Optimization

Teo

2017

International Journal on Advanced Science, Engineering and Information Technology

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Two Crossover-first Differential Evolution (XDE) algorithms as well as four self-adaptive DE algorithms are compared in this study in terms of their optimization accuracy for solving a set of 15 complex, non-linear numerical optimization functions across 4 different dimensions of 10, 30, 50 and 100 optimization variables. XDE is a crossover-first variant of the original DE algorithm where XjDE is the crossover-first variant of the self-adaptive jDE algorithm. The original DE representing a fixed parameter strategy is tested against four self-adaptive algorithms, namely the DESACR, DESACRF, SDE and jDE algorithms. Although XDE is able to outperform XjDE in all 15 test problems for the lowest dimensional benchmark test setting of 10 variables, the crossover-first approach in XjDE is able to improve its performance and obtained better results over XDE in some of the test problems for the higher-dimensional benchmark test settings of 30, 50 and 100 variables. As such, this shows that there is some merit in adopting the crossover-first approach into the self-adaptive XjDE algorithm since the CR and F parameters are automatically adjusted and optimized by the algorithm itself as compared to the fixed CR and F in XDE which has to be manually tuned by hand. The results also show that different self-adaptive parameter tuning schemes have significantly different effects on the performance of DE as the number of optimization dimensions increases.

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A Time-Critical Investigation of Parameter Tuning in Differential Evolution for Non-Linear Global Optimization

Cited by 3 publications

References 12 publications

Credit Card Detection System Based on Ridit Approach

Credit Card Detection System Based on Ridit Approach

Multi-objective Optimization of Biochemical System Production Using an Improve Newton Competitive Differential Evolution Method

Analyzing the Scalability Performance of Crossover-First and Self-Adaptive Differential Evolution Algorithms for Complex Numerical Optimization

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