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SGERD: A steady-state genetic algorithm for Extracting Fuzzy Classification Rules From Data

IEEE TRANSACTIONS ON FUZZY SYSTEMS 2008年第4期16卷 1061-1071页

作者： Mansoori, Eghbal G. Zolghadri, Mansoor J. Katebi, Seraj D. Shiraz Univ Dept Comp Sci & Engn Shiraz 71935 Iran

This paper considers the automatic design of fuzzy-rule-based classification systems from labeled data. The performance of classifiers and the interpretability of generated rules are of major importance in these systems. In past research, some genetic-based algorithms have been used for the rule learning process. These genetic fuzzy systems have utilized different approaches to encode rules. In this paper, we have proposed a novel steady-state genetic algorithm to extract a compact set of good fuzzy rules from numerical data (SGERD). The selection mechanism of this algorithm is nonrandom, and only the best individuals can survive. Our approach is very simple and fast, and can be applied to high-dimensional problems with numerical attributes. To select the rules having high generalization capabilities, our algorithm makes use of some rule- and data-dependent parameters. We have also proposed an enhancing function that modifies the rule evaluation measures in order to assess the candidate rules more effectively before their selection. Experiments on some well-known data sets are performed to show the performance of SGERD.

关键词： Data mining fuzzy-rule-based classification system fuzzy rule learning steady-state genetic algorithm

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A hybrid steady-state genetic algorithm for the min-degree constrained minimum spanning tree problem

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2019年第1期276卷 88-105页

作者： Singh, Kavita Sundar, Shyam Natl Inst Technol Raipur Dept Comp Applicat Raipur 492010 Madhya Pradesh India

Given an undirected, connected and edge-weighted graph, the min-degree constrained minimum spanning tree (md-MST) problem aims to find a minimum spanning tree (T) in such a way that each non leaf vertex in T has degree at least d, where d is given a positive integer constant. This paper proposes a hybrid steady-state genetic algorithm (HSSGA) for the and-MST problem. The proposed HSSGA combines various components- such as problem-specific genetic operators (crossover operator and mutation operator) that are designed in such a way that the min-degree constraint is always maintained, hence resulting into a feasible child solution;and a population updating strategy that helps in maintaining diversity in the population- of the steady-state genetic algorithm with a fast local search. On a set of standard benchmark instances, the proposed HSSGA shows its effectiveness in comparison to state-ofthe-art approaches such as four versions of variable neighborhood search, branch and cut algorithm and three versions of generational genetic algorithm. Moreover, HSSGA finds new best values on 32 out of 105 benchmark instances. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Combinatorial optimization Min-degree constrained spanning tree problem steady-state genetic algorithm Problem-specific genetic operators Fast local search

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A Hybrid steady-state genetic algorithm for the Minimum Conflict Spanning Tree Problem 9th

A Hybrid Steady-State Genetic Algorithm for the Minimum Conf...

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9th Annual Conference on Machine Learning, Optimization and Data science (LOD)

作者： Chaubey, Punit Kumar Sundar, Shyam Natl Inst Technol Raipur Dept Comp Applicat Raipur 492010 Madhya Pradesh India

ISBN: (纸本)9783031539657;9783031539664

This paper studies a hybrid approach for the minimum conflict spanning tree (MCST) problem, where the MCST problem deals with finding a spanning tree (T) with the minimum number of conflicting edge-pairs. The problem finds some important real-world applications. In this hybrid approach (hSSGA), a steady-state genetic algorithm generates a child solution with the help of crossover operator and mutation operator which are applied in a mutually exclusive way, and the generated child solution is further improved through a local search based on reduction of conflicting edge-pairs. The proposed crossover operator is problem-specific operator that attempt to create a fitter child solution. All components of SSGA and local search effectively coordinate in finding a conflict-free solution or a solution with a minimal number of conflicting edge-pairs. Experimental results, particularly, on available 12 instances of type 1 benchmark instances whose conflict solutions are not known show that the proposed hybrid approach hSSGA is able to find better solution quality in comparison to state-of-the-art approaches. Also, hSSGA discovers new values on 8 instances out of 12 instances of type 1.

关键词： Conflict Spanning Tree steady-state genetic algorithm Local search

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A steady-state genetic algorithm for the Single Machine Scheduling Problem with Periodic Machine Availability

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SN Computer Science 2023年第5期4卷 1-11页

作者： Chaubey, Punit Kumar Sundar, Shyam Computer Applications Department National Institute of Technology Raipur Raipur 492010 India

This paper presents an evolutionary algorithm-based steady-state grouping genetic algorithm (SSGGA) for the single-machine scheduling problem with periodic machine availability (SinMSPMA problem) whose objective is to minimize the makespan. This problem is NP -hard which arises in several real production scenarios, where industries are giving importance of maintenance activities in their production scheduling systems due to not only improving the efficiency and safety of production, but also increasing the productivity. The SinMSPMA problem belongs to a class of grouping problems. Due to its grouping-aspect structure, the proposed SSGGA encodes each chromosome as a set of periods (groups) and relies on combining specialized genetic operators with a problem-specific repair operator in order to generate an offspring. On available benchmark instances, computational results of SSGGA indicate that SSGGA outperforms the best three approaches out of 19 existing approaches. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Evolutionary algorithm Makespan Production scheduling steady-state genetic algorithm

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SVR kernel parameters selection based on steady-state genetic algorithm

SVR kernel parameters selection based on steady-state geneti...

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6th World Congress on Intelligent Control and Automation

作者： Li, Jie Gao, Feng Guan, Xiaohong Xu, Hui Xi An Jiao Tong Univ Syst Engn Inst SKLMS Xian 710049 Peoples R China Tsinghua Univ Ctr Int & Networked Syst Beijing 100084 Peoples R China

ISBN: (纸本)1424403316

The hyper parameters selection has a great affection on the accuracy of support vector regression algorithm. We chose the optimal hyper parameters including kernel parameters based on steady genetic algorithm for the support vector regression model. Selection of usually used RBF kernel parameters was thoroughly investigated. Two selection strategies for single and diagonal kernel parameters selection were applied on the standard sample data for Boston housing forecasting, and for electrical power demand forecasting. The testing results show that applying steady GA is effective in selecting multiple parameters.

关键词： support vector machine steady-state genetic algorithm hyper parameters selection kernel parameters

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A steady-state Grouping genetic algorithm for the Rainbow Spanning Forest Problem

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SN Computer Science 2023年第4期4卷 1-16页

作者： Ghoshal, Sudishna Sundar, Shyam Department of Computer Applications National Institute of Technology Raipur Raipur 492010 India

Given a connected, undirected and edge-colored graph, the rainbow spanning forest (RSF) problem aims to find a rainbow spanning forest with the minimum number of rainbow trees, where a rainbow tree is a connected acyclic subgraph of the graph whose each edge is associated with a different color. This problem is NP-hard and finds several applications in distinguishing among various types of connections. Being a grouping problem, this paper proposes a steady-state grouping genetic algorithm (SSGGA) for the RSF problem. To the best of our knowledge, this is the first work on steady-state grouping genetic algorithm for this problem. While keeping in view of grouping aspects of the problem, each individual, in the proposed SSGGA, is encoded as a group of rainbow trees, and accordingly, a problem-specific crossover operator is designed. Moreover, SSGGA uses the idea of two steps in its replacement scheme. All such elements of SSGGA coordinate effectively and overall help in finding high-quality solutions. Computational results obtained over a set of benchmark instances show that overall SSGGA, in terms of solution quality, is superior to all other existing approaches in the literature for this problem. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Grouping problem Rainbow spanning forest Replacement schemes steady-state genetic algorithm

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A hybrid genetic algorithm for the degree-constrained minimum spanning tree problem

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SOFT COMPUTING 2020年第3期24卷 2169-2186页

作者： Singh, Kavita Sundar, Shyam Natl Inst Technol Raipur Dept Comp Applicat Raipur 492010 Madhya Pradesh India

Given an undirected, connected, edge-weighted graph G and a positive integer d, the degree-constrained minimum spanning tree (dc-MST) problem aims to find a minimum spanning tree T on G subject to the constraint that each vertex is either a leaf vertex or else has degree at most d in T, where d is a given positive integer. The dc-MST is NP-hard problem for d >= 2 and finds several real-world applications. This paper proposes a hybrid approach (HSSGA) combining a steady-state genetic algorithm and local search strategies for the this problem. An additional step (based on perturbation strategy at a regular interval of time) in the replacement strategy is applied in order to maintain diversity in the population throughout the search process. On a set of available 107 benchmark instances, computational results show the superiority of our proposed HSSGA in comparison with the state-of-the-art metaheuristic techniques.

关键词： Degree-constrained Spanning tree steady-state genetic algorithm Problem-specific crossover operator Local search Replacement strategy

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Improved heuristics for the bounded-diameter minimum spanning tree problem

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SOFT COMPUTING 2007年第10期11卷 911-921页

作者： Singh, Alok Gupta, Ashok K. Univ Allahabad Fac Sci JK Inst Appl Phys & Technol Allahabad 211002 Uttar Pradesh India

Given an undirected, connected, weighted graph and a positive integer k, the bounded-diameter minimum spanning tree (BDMST) problem seeks a spanning tree of the graph with smallest weight, among all spanning trees of the graph, which contain no path with more than k edges. In general, this problem is NP-Hard for 4 <= k < n - 1, where n is the number of vertices in the graph. This work is an improvement over two existing greedy heuristics, called randomized greedy heuristic (RGH) and centre-based tree construction heuristic (CBTC), and a permutation-coded evolutionary algorithm for the BDMST problem. We have proposed two improvements in RGH/CBTC. The first improvement iteratively tries to modify the bounded-diameter spanning tree obtained by RGH/CBTC so as to reduce its cost, whereas the second improves the speed. We have modified the crossover and mutation operators and the decoder used in permutation-coded evolutionary algorithm so as to improve its performance. Computational results show the effectiveness of our approaches. Our approaches obtained better quality solutions in a much shorter time on all test problem instances considered.

关键词： bounded-diameter minimum spanning tree problem constrained optimization greedy heuristic steady-state genetic algorithm uniform order-based crossover

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Microwave imaging of a partially immersed non-uniform conducting cylinder

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INTERNATIONAL JOURNAL OF APPLIED ELECTROMAGNETICS AND MECHANICS 2012年第3期40卷 215-225页

作者： Chien, Wei Chiu, Chien-Ching Li, Ching-Lieh Sun, Chi-Hsien De Lin Inst Technol Dept Elect Engn Taipei Taiwan Tamkang Univ Dept Elect Engn Tamsui Taiwan

In this paper, we investigate the imaging problem to determine both the shape and the conductivity of a partially immersed non-uniform conducting cylinder from the knowledge of scattered far-field pattern of TM waves by solving the ill-posed nonlinear equation. Based on the boundary condition and the measured scattered field, a set of nonlinear integral equations is derived and the inverse problem is reformulated into an optimization one. The steady-state genetic algorithm is then employed to find out the global extreme solution of the object function. As a result, the shape and the conductivity of the conductor can be obtained. Numerical results are given to demonstrate that even in the presence of noise, good reconstruction can be obtained.

关键词： Inverse problem partially immersed non-uniform conducting cylinder steady-state genetic algorithm

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A hybrid genetic algorithm to optimize device allocation in industrial Ethernet networks with real-time constraints

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Journal of Zhejiang University-Science C(Computers and Electronics) 2011年第12期12卷 965-975页

作者： Lei ZHANG Mattias LAMPE Zhi WANG State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou 310027 China Corporate Technology Siemens Ltd. Beijing 100102 China

With the advance of automation technology,the scale of industrial communication networks at field level is *** real-time performance of these networks is therefore becoming an increasingly difficult *** paper addresses the optimization of device allocation in industrial Ethernet networks with real-time constraints (DAIEN-RC).Considering the inherent diversity of real-time requirements of typical industrial applications,a novel optimization criterion based on relative delay is proposed.A hybrid genetic algorithm incorporating a reduced variable neighborhood search (GA-rVNS) is developed for *** results show that the proposed novel scheme achieves a superior performance compared to existing schemes,especially for large scale industrial networks.

关键词： Optimization Real-time Industrial Ethernet Device allocation steady-state genetic algorithm Variable neighbor-hood search

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