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6th International Conference on Hybrid Artificial Intelligence Systems (HAIS)

作者： Matthews, Stephen G. Gongora, Mario A. Hopgood, Adrian A. De Montfort Univ Ctr Computat Intelligence Leicester LE1 9BH Leics England

ISBN: (纸本)9783642212185

A novel method for mining association rules that are both quantitative and temporal using a multi-objective evolutionary algorithm is presented. This method successfully identifies numerous temporal association rules that occur more frequently in areas of a dataset with specific quantitative values represented with fuzzy sets. The novelty of this research lies in exploring the composition of quantitative and temporal fuzzy association rules and the approach of using a hybridisation of a multi-objective evolutionary algorithm with fuzzy sets. Results show the ability of a multi-objective evolutionary algorithm (NSGA-II) to evolve multiple target itemsets that have been augmented into synthetic datasets.

关键词： multi-objective evolutionary algorithm fuzzy association rules temporal association rules NSGA-II hyrbid

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A Large Scale multi-objective Ontology Matching Framework 13th

A Large Scale Multi-objective Ontology Matching Framework

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13th International Conference on Intelligent Information Hiding and multimedia Signal Processing (IIH-MSP)

作者： Xue, Xingsi Ren, Aihong Fujian Univ Technol Coll Informat Sci & Engn Fuzhou Fujian Peoples R China Fujian Univ Technol Fujian Prov Key Lab Big Data Min & Applicat Fuzhou Fujian Peoples R China Baoji Univ Arts & Sci Dept Math Baoji Shaanxi Peoples R China

ISBN: (纸本)9783319638560;9783319638553

multi-objective evolutionary algorithm (MOEA) is emerging as a state-of-the-art methodology to solve the ontology meta-matching problem. However, the huge search scale of large scale ontology matching problem stops MOEA based ontology matching technology from correctly and completely identifying the semantic correspondences. To this end, in this paper, a large scale multi-objective ontology matching framework is proposed, which works with three sequential steps: (1) partition the large scale ontologies into similar ontology segment pairs;(2) utilize MOEA to match the similar ontology segments in parallel;(3) select the representative ontology segment alignments, which are further aggregated to obtain the final ontology alignment. In addition, a novel multi-objective model is also constructed for ontology matching problem and the MOEA and entity similarity measure that could be used in this framework are also recommended. The experimental result shows the effectiveness of our proposal.

关键词： multi-objective evolutionary algorithm Large scale ontology matching Ontology partition

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Optimal selection of molecular descriptors for antimicrobial peptides classification: an evolutionary feature weighting approach

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BMC GENOMICS 2018年第Sup7期19卷 672-672页

作者： Beltran, Jesus A. Aguilera-Mendoza, Longendri Brizuela, Carlos A. Ctr Sci Res & Higher Educ Ensenada CICESE Dept Comp Sci Carretera Ensenada Tijuana 3918 Ensenada 22860 Baja California Mexico

Background: Antimicrobial peptides are a promising alternative for combating pathogens resistant to conventional antibiotics. Computer-assisted peptide discovery strategies are necessary to automatically assess a significant amount of data by generating models that efficiently classify what an antimicrobial peptide is, before its evaluation in the wet lab. Model's performance depends on the selection of molecular descriptors for which an efficient and effective approach has recently been proposed. Unfortunately, how to adapt this method to the selection of molecular descriptors for the classification of antimicrobial peptides and the performance it can achieve, have only preliminary been explored. Results: We propose an adaptation of this successful feature selection approach for the weighting of molecular descriptors and assess its performance. The evaluation is conducted on six high-quality benchmark datasets that have previously been used for the empirical evaluation of state-of-art antimicrobial prediction tools in an unbiased manner. The results indicate that our approach substantially reduces the number of required molecular descriptors, improving, at the same time, the performance of classification with respect to using all molecular descriptors. Our models also outperform state-of-art prediction tools for the classification of antimicrobial and antibacterial peptides. Conclusions: The proposed methodology is an efficient approach for the development of models to classify antimicrobial peptides. Particularly in the generation of models for discrimination against a specific antimicrobial activity, such as antibacterial. One of our future directions is aimed at using the obtained classifier to search for antimicrobial peptides in various transcriptomes.

关键词： Antimicrobial peptides Feature weighting Molecular descriptors Classification multi-objective evolutionary algorithm Peptide representation

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evolutionary Computation of multi-Band Antenna Using multi-objective evolutionary algorithm Based on Decomposition

Evolutionary Computation of Multi-Band Antenna Using Multi-O...

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2nd International Conference on Information Computing and Applications

作者： Ding, Dawei Wang, Hongjin Wang, Gang UJS Dept Telecommun Engn Zhenjiang Peoples R China USTC Dept Elect Engn & Informat Hefei Peoples R China

ISBN: (纸本)9783642252549

Design of multi-band antenna involves multiple characteristics such as return loss in multiple operation bands. To apply MOEA/D (multi-objective evolutionary algorithm based on decomposition) to antenna structure optimization, it was introduced into this field for the first time. MOEA/D framework was demonstrated at first. Then it was in conjunction with electromagnetic solver, HFSS (high frequency structure simulator) to optimize and design tri-band bow-tie antenna to serve as an example. The evolutionary results showed that MOEA/D worked efficiently and generated multiple candidate structures at one single iteration, and that MOEA/D had lower computational overhead than NSGA-II (non-dominated sorting genetic algorithm II) for this problem. Therefore, MOEA/D shows great potential for antenna structure optimization and design.

关键词： Bow-tie antenna multi-objective evolutionary algorithm multi-objective optimization

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The Investment Models Based on the Fundamental and Technical Indicators of Listed Companies 37

The Investment Models Based on the Fundamental and Technical...

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37th Chinese Control Conference (CCC)

作者： Meng, Xiaolian Chen, Xiuzhen Nanjing Univ Sci & Technol Sch Econ & Management Nanjing 210094 Jiangsu Peoples R China

ISBN: (纸本)9789881563958

The fundamental and technical approaches are often used to analyze the operation status of listed companies and the development tendency of the stock market. The investment models using financial ratios and technical indicators are proposed in this article, and a multi-objective evolutionary algorithm with benefits and risks is used to optimize them. Two different chromosomes are used for representing different investment models with real constraints. An empirical study based on 40 stocks of SSE A shares from Cathay Pacific database was conducted to illustrate the practicability of the proposed models. The results show that better stocks and better transaction decisions can be found and selected by the portfolio management method based on fundamental and technical analysis, which could be obtained a portfolio with better result than the market index. The effectiveness and practicality of this investment model is improved more with more fundamental indicators introduced, especially cash flow growth, capital expenditure growth rates and payout ratio.

关键词： multi-objective evolutionary algorithm fundamental analysis technical analysis stock investment

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Learning Fault Localisation for both Humans and Machines Using multi-objective GP 10th

Learning Fault Localisation for both Humans and Machines Usi...

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10th International Symposium on Search-Based Software Engineering (SSBSE)

作者： Choi, Kabdo Sohn, Jeongju Yoo, Shin Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (纸本)9783319992419;9783319992402

Genetic Programming has been successfully applied to fault localisation to learn ranking models that place the faulty program element as near the top as possible. However, it is also known that, when localisation results are used by Automatic Program Repair (APR) techniques, higher rankings of faulty program elements do not necessarily result in better repair effectiveness. Since APR techniques tend to use localisation scores as weights for program mutation, lower scores for non-faulty program elements are as important as high scores for faulty program elements. We formulate a multi-objective version of GP based fault localisation to learn ranking models that not only aim to place the faulty program element higher in the ranking, but also aim to assign as low scores as possible to non-faulty program elements. The results show minor improvements in the suspiciousness score distribution. However, surprisingly, the multi-objective formulation also results in more accurate fault localisation ranking-wise, placing 155 out of 386 faulty methods at the top, compared to 135 placed at the top by the single objective formulation.

关键词： Fault localisation multi-objective evolutionary algorithm

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Parallel biclustering detection using strength Pareto front evolutionary algorithm

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INFORMATION SCIENCES 2017年 415卷 283-297页

作者： Golchin, Maryam Liew, Alan Wee Chung Griffith Univ Sch Informat & Commun Technol Gold Coast Campus Nathan Qld 4222 Australia

Biclustering has become a popular technique to analyse gene expression datasets and extract valuable information by clustering rows and columns of a dataset simultaneously. Using a good merit function together with a suitable local search can lead to the detection of interesting biclusters. In this paper, a multi-objective evolutionary algorithm with local search is proposed to search for multiple biclusters concurrently in a single run of the evolutionary algorithm. We call our method PBD-SPEA2 (Parallel Biclustering Detection using Strength Pareto front evolutionary algorithm 2). In our algorithm, a new dynamic encoding scheme is used to encode multiple biclusters in each individual. Our multi-objective function consists of three objectives that simultaneously optimizes the homogeneity of the elements in the bicluster, the size of the bicluster, and the variance of the column in the bicluster with respect to the entire dataset. Crossover is done by selecting and combining the best biclusters among the encoded biclusters from both parents through a strategy of exploration and exploitation. Finally, a sequential selection procedure is used to select the final set of biclusters from individuals that constitute the Pareto front. Experimental results are presented to compare the performance and biological enrichment of detected biclusters with several existing algorithms. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Biclustering Gene expression dataset multi-objective evolutionary algorithm Local search

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Feature weighting for antimicrobial peptides classification: a multi-objective evolutionary approach

Feature weighting for antimicrobial peptides classification:...

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Biological Ontologies and Knowledge Bases Workshop at IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM)

作者： Beltran, Jesus A. Aguilera-Mendoza, Longendri Brizuela, Carlos A. Ctr Sci Res & Higher Educ Ensenada CICESE Comp Sci Dept Ensenada 22860 Baja California Mexico

ISBN: (纸本)9781509030507

Antimicrobial peptides might become crucial in fighting antibiotic resistant bacteria and other infections. Next Generation Sequencing technologies are generating a large amount of data where peptides with antimicrobial activity could be found. Therefore, algorithms that can efficiently determine whether or not a short sequence of amino acids is antimicrobial are needed. In this context, Quantitative Structure-Activity Relationship modeling has paved the way toward the association of the physicochemical properties of peptides to their biological activity. Nowadays, there are algorithms that can compute thousands of physicochemical properties known as molecular descriptors. However, some of these descriptors are irrelevant and some might even mislead the correct classification of the peptide activity. To mitigate this problem, a descriptor selection process must be performed, this will help to improve the classification accuracy and to decrease the computational time required for classification. In a recent work, a general method to weight and select features has been proposed. The method models the descriptor selection problem as a multi-objective optimization problem (MOOP). The main idea is to optimize simultaneously the intra-and inter-class distances. We follow this approach and apply it to the feature selection problem for the classification of antimicrobial peptides. To this aim we modify the original MOOP formulation to avoid bringing together non-antimicrobial peptides. Preliminary results indicate that our approach can substantially reduce the number of required molecular descriptors and improve the performance of classification with respect to the original formulation.

关键词： antimicrobial peptides AMPs feature weighting molecular descriptors classification multi-objective evolutionary algorithm peptide representation

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An improved MOEA/D algorithm for multi-objective multicast routing with network coding

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APPLIED SOFT COMPUTING 2017年 59卷 88-103页

作者： Xing, Huanlai Wang, Zhaoyuan Li, Tianrui Li, Hui Qu, Rong Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu 610031 Sichuan Peoples R China Xi An Jiao Tong Univ Sch Math & Stat Xian 710049 Shaanxi Peoples R China Univ Nottingham Sch Comp Sci Nottingham NG8 1BB England

Network coding enables higher network throughput, more balanced traffic, and securer data transmission. However, complicated mathematical operations incur when packets are combined at intermediate nodes, which, if not operated properly, lead to very high network resource consumption and unacceptable delay. Therefore, it is of vital importance to minimize various network resources and end-to-end delays while exploiting promising benefits of network coding. multicast has been used in increasingly more applications, such as video conferencing and remote education. In this paper the multicast routing problem with network coding is formulated as a multi-objective optimization problem (MOP), where the total coding cost, the total link cost and the end-to-end delay are minimized simultaneously. We adapt the multi-objective evolutionary algorithm based on decomposition (MOEA/D) for this MOP by hybridizing it with a population-based incremental learning technique which makes use of the global and historical information collected to provide additional guidance to the evolutionary search. Three new schemes are devised to facilitate the performance improvement, including a probability-based initialization scheme, a problem-specific population updating rule, and a hybridized reproduction operator. Experimental results clearly demonstrate that the proposed algorithm outperforms a number of state-of-the-art MOEAs regarding the solution quality and computational time. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Network coding multicast multi-objective evolutionary algorithm

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FEMOSAA: Feature-Guided and Knee-Driven multi-objective Optimization for Self-Adaptive Software

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ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY 2018年第2期27卷 5-5页

作者： Chen, Tao Li, Ke Bahsoon, Rami Yao, Xin Nottingham Trent Univ Dept Comp & Technol Nottingham NG11 8NS England Univ Birmingham Sch Comp Sci CERCIA Birmingham B15 2TT W Midlands England Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu Sichuan Peoples R China Univ Exeter Dept Comp Sci Exeter EX4 4QD Devon England Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Peoples R China Univ Elect Sci & Technol 5 China Chengdu Sichuan Peoples R China

Self-Adaptive Software (SAS) can reconfigure itself to adapt to the changing environment at runtime, aiming to continually optimize conflicted nonfunctional objectives (e.g., response time, energy consumption, throughput, cost, etc.). In this article, we present Feature-guided and knEe-driven multi-objective optimization for Self-Adaptive softwAre (FEMOSAA), a novel framework that automatically synergizes the feature model and multi-objective evolutionary algorithm (MOEA) to optimize SAS at runtime. FEMOSAA operates in two phases: at design time, FEMOSAA automatically transposes the engineers' design of SAS, expressed as a feature model, to fit the MOEA, creating new chromosome representation and reproduction operators. At runtime, FEMOSAA utilizes the feature model as domain knowledge to guide the search and further ex-tend the MOEA, providing a larger chance for finding better solutions. In addition, we have designed a new method to search for the knee solutions, which can achieve a balanced tradeoff. We comprehensively evaluated FEMOSAA on two running SAS: One is a highly complex SAS with various adaptable real-world software under the realistic workload trace;another is a service-oriented SAS that can be dynamically composed from services. In particular, we compared the effectiveness and overhead of FEMOSAA against four of its variants and three other search-based frameworks for SAS under various scenarios, including three commonly applied MOEAs, two workload patterns, and diverse conflicting quality objectives. The results reveal the effectiveness of FEMOSAA and its superiority over the others with high statistical significance and nontrivial effect sizes.

关键词： Feature model search-based software engineering multi-objective evolutionary algorithm multi-objective optimization self-adaptive system performance engineering

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