Optimum Link Allocation of Fixed Topology Networks

Kontoleon, J.M.

doi:10.1109/tr.1979.5220527

Cited by 24 publications

(4 citation statements)

References 3 publications

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“…With its advent in 1980s, weights of the complex network were obtained using evolutionary approach keeping the architecture unchanged. This approach was called fixed-topology neuroevolution [38]. Although, by changing weights, the intensity or strength of neurons' knowledge was evolved but a new knowledge could not be evolved since the architecture was still the same.…”

Section: Neuroevolutionmentioning

confidence: 99%

Analytics of Heterogeneous Breast Cancer Data Using Neuroevolution

et al. 2019

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Breast cancer prognostic modeling is difficult since it is governed by many diverse factors. Given the low median survival and large scale breast cancer data, which comes from high throughput technology, the accurate and reliable prognosis of breast cancer is becoming increasingly difficult. While accurate and timely prognosis may save many patients from going through painful and expensive treatments, it may also help oncologists in managing the disease more efficiently and effectively. Data analytics augmented by machine-learning algorithms have been proposed in past for breast cancer prognosis; and however, most of these could not perform well owing to the heterogeneous nature of available data and model interpretability related issues. A robust prognostic modeling approach is proposed here whereby a Pareto optimal set of deep neural networks (DNNs) exhibiting equally good performance metrics is obtained. The set of DNNs is initialized and their hyperparameters are optimized using the evolutionary algorithm, NSGAIII. The final DNN model is selected from the Pareto optimal set of many DNNs using a fuzzy inferencing approach. Contrary to using DNNs as the black box, the proposed scheme allows understanding how various performance metrics (such as accuracy, sensitivity, F1, and so on) change with changes in hyperparameters. This enhanced interpretability can be further used to improve or modify the behavior of DNNs. The heterogeneous breast cancer database requires preprocessing for better interpretation of categorical variables in order to improve prognosis from classifiers. Furthermore, we propose to use a neural networkbased entity-embedding method for categorical features with high cardinality. This approach can provide a vector representation of categorical features in multidimensional space with enhanced interpretability. It is shown with evidence that DNNs optimized using evolutionary algorithms exhibit improved performance over other classifiers mentioned in this paper. INDEX TERMS Breast cancer prognostic modelling, entity embedding, deep learning networks, evolutionary algorithms, fuzzy inferencing.

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Section: Neuroevolutionmentioning

confidence: 99%

Analytics of Heterogeneous Breast Cancer Data Using Neuroevolution

et al. 2019

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“…For the optimal assignment, Kontoleon [17] gave an iterative algorithm to calculate the optimal permutation. This algorithm could assign the component with the lowest reliability into all the positions in the system and order the component based on the Birnbaum importance and finally place the component into the system through iterative allocation.…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

“…On the basis of LKA, Yao et al [21] built the LK type algorithms, which included LKA, LKB, LKC, and LKD, and designed a Birnbaum importance-based two-stage approach (BITA) according to the numerical experimentation of ZK and LK algorithms. BI is one of the most widely investigated importance measures and has been applied to the CAP especially in [17][18][19][20][21]; therefore, this article focuses on the BI applied to Cir/Con/ / systems for the CAP.…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

Reliability Optimization and Importance Analysis of Circular‐Consecutive k‐out‐of‐n System

Zhang

Zhao

et al. 2017

Mathematical Problems in Engineering

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The circular-consecutive -out-of-: ( ) system (Cir/Con/ / : ( ) system) usually consists of components arranged in a circle where the system fails (works) if consecutive components fail (work). The optimization of the Cir/Con/ / system is a typical case in the component assignment problem. In this paper, the Birnbaum importance-based genetic algorithm (BIGA), which takes the advantages of genetic algorithm and Birnbaum importance, is introduced to deal with the reliability optimization for Cir/Con/ / system. The detailed process and property of BIGA are put forward at first. Then, some numerical experiments are implemented, whose results are compared with two classic Birnbaum importance-based search algorithms, to evaluate the effectiveness and efficiency of BIGA in Cir/Con/ / system. Finally, three typical cases of Cir/Con/ / systems are introduced to demonstrate the relationships among the component reliability, optimal permutation, and component importance.

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“…There are two groups of the BI-based heuristics for the SCAP: the local search methods and the genetic-algorithm based methods. Among the BI-based local search methods, Yao et al [9] propose a two-stage method and show that it is the most efficient local search in terms of solution quality by comparing with the iterative assignment methods [9,10,21] and pair-wise exchange methods [9,22]. The iterative assignment methods first assign either the least or the most reliable component to all positions and then iteratively assign the available components to the positions according to their ranks of the BI values until all components are assigned to the system.…”

Section: Introductionmentioning

confidence: 99%

Birnbaum importance based heuristics for multi-type component assignment problems

Zhu

Yuan

et al. 2017

Reliability Engineering & System Safety

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This paper proposes a new type of system reliability design problem, namely, the multi-type component assignment problem (MCAP). The MCAP is to assign more than one type of components to positions in a system with the objective of maximizing system reliability. Each type of components has multiple units, and a unit of a particular type can be assigned to a position of the same type in the system. We present the Birnbaum-importance based local search methods and the Birnbaum-importance based genetic algorithms for addressing the MCAP. The comprehensive numerical experiments are conducted for the Lin/Con/k/n:F(G) systems with multiple types of components and four cases of component reliability. The computational results demonstrate the efficiencies of the heuristics, highlighting the methods of dealing with multiple types of components. It shows that the Birnbaum importance in designing the algorithms is promising because of its effectiveness.

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Optimum Link Allocation of Fixed Topology Networks

Cited by 24 publications

References 3 publications

Analytics of Heterogeneous Breast Cancer Data Using Neuroevolution

Analytics of Heterogeneous Breast Cancer Data Using Neuroevolution

Reliability Optimization and Importance Analysis of Circular‐Consecutive k‐out‐of‐n System

Birnbaum importance based heuristics for multi-type component assignment problems

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