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Fuzzy rules for describing subgroups from Influenza A virus using a multi-objective evolutionary algorithm

APPLIED SOFT COMPUTING 2013年第8期13卷 3439-3448页

作者： Carmona, C. J. Chrysostomou, C. Seker, H. del Jesus, M. J. Univ Jaen Dept Comp Sci Jaen 23071 Spain Univ Leicester Dept Genet Leicester LE1 7RH Leics England De Montfort Univ Sch Comp Ctr Computat Intelligent Leicester LE1 9BH Leics England

Extraction of biologically-meaningful knowledge is one of the important and challenging tasks in bioinformatics, in particular computational analysis of DNA and protein sequences, in order to identify biological function(s) and behaviour(s) of newly-extracted sequences. Computational intelligence techniques in corporation with sequence-driven features have been applied to tackle the problem and help classify different functional classes of the sequences. In order to study this problem, subgroup discovery algorithms together with a signal processing-based feature extraction method are applied, where the sequences are represented as a signal. The applicability of this method has been studied through four different Neuraminidase genes of Influenza A subtypes, H1N1, H2N2, H3N2 and H5N1. The results yielded not only higher predictive accuracy over these four classes of the proteins but also interpretable rule-based representation of the descriptive model with a significantly reduced feature set driven by means of the signal processing method. Subgroup discovery technique based on evolutionary fuzzy systems is expected to open new areas of research in bioinformatics and further help identify and understand more focused therapeutic protein targets. (C) 2013 Elsevier B. V. All rights reserved.

关键词： evolutionary fuzzy system Influenza A virus Neuraminidase Subgroup discovery multi-objective evolutionary algorithm

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Interpretability-based fuzzy decision tree classifier a hybrid of the subtractive clustering and the multi-objective evolutionary algorithm

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SOFT COMPUTING 2013年第9期17卷 1673-1686页

作者： Afsari, F. Eftekhari, M. Eslami, E. Woo, P. -Y. Shahid Bahonar Univ Kerman Dept Comp Engn Kerman Iran Shahid Bahonar Univ Kerman Dept Math Kerman Iran No Illinois Univ Dept Elect Engn De Kalb IL 60115 USA

The fuzzy decision tree (FDT) is a powerful, top-down, hierarchical search scheme to extract human interpretable classification rules. Furthermore, the FDT is considered an approach to model a system by making use of a descriptive language with fuzzy logic and fuzzy predicates. There exist two contradictory requirements with fuzzy modeling, namely, accuracy and interpretability. In this study, the subtractive clustering and a multi-objective evolutionary algorithm are used to develop a novel fuzzy modeling scheme based on the FDT classifier to construct an accurate and interpretable system that is defined as the interpretability-based fuzzy decision tree classifier. Two interpretability measures, namely, complexity interpretability and semantics interpretability are considered to reach acceptable accuracy. These measures are optimized as different objectives within the multi-objective framework. Results obtained in several benchmark classification problems are encouraging because they show the ability of the developed scheme while yielding accuracy comparable to that achieved by other methods like neural networks and the crisp decision tree (C4.5). The experimental results demonstrate the superiority of the developed scheme.

关键词： Fuzzy decision tree Interpretability measures Interpretability-based fuzzy decision tree classifier (IFDTC) multi-objective evolutionary algorithm

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Dynamic index tracking via multi-objective evolutionary algorithm

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APPLIED SOFT COMPUTING 2013年第7期13卷 3392-3408页

作者： Chiam, S. C. Tan, K. C. Al Mamun, A. Natl Univ Singapore Dept Elect & Comp Engn Singapore 117576 Singapore

Index tracking has been gaining in popularity in recent years, as sustainable and stable yields exceeding market returns proved to be elusive. Leveraging on the search capability of evolutionary algorithm, this paper proposed a multi-objective evolutionary index tracking platform that could simultaneously optimize both tracking performance and transaction costs throughout the investment horizon and address various real-world implementation issues in index tracking. For model evaluation, a realistic instantiation of the index tracking optimization problem that accounted for stochastic capital injections, practical transactional cost structures and other real-world constraints was formulated. Portfolio rebalancing strategies for the alignment of the tracker portfolio to time-varying market conditions were investigated also. Empirical studies based on equity indices from major global markets were conducted and the results validated the tracking capability of the proposed index tracking system in out-of-sample data sets, whilst minimizing transaction costs throughout the investment horizon. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Index tracking Portfolio rebalancing multi-objective evolutionary algorithm

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Novel multi objective evolutionary framework for solving next release problem

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第2期44卷 3315-3339页

作者： Ghasemi, Mohsen Bagherifard, Karamollah Parvin, Hamid Nejatian, Samad Islamic Azad Univ Dept Comp Engn Yasooj Branch Yasuj Iran Islamic Azad Univ Dept Comp Engn Nourabad Mamasani Branch Nourabad Mamasani Iran Islamic Azad Univ Dept Elect Engn Yasooj Branch Yasuj Iran

Software developers want to meet the requirements of customers in next versions. Choosing which set of requirements can be done according to cost and time is an NP-hard problem known as Next Release Problem (NRP). In this article, a multi objective evolutionary algorithm (MOEA) framework is proposed to solve NRP. The framework applies the nonrepetitive population, integrates solutions and external repository. Furthermore, a novel approach is implemented to satisfy the constraints of the problem. In this framework, six evolutionary algorithms are implemented and using seven quality indicators, the achieved results of that algorithms are compared with the original versions of same algorithms. Through using HV (the ratio of the region covered by Pareto Front) and NDS (the number of solutions in the Pareto Front) metrics, the effects of the proposed algorithms are compared with other works' results. The efficacy of the proposed MOEA framework is measured using three real world datasets. The gained results represent that the implemented algorithms perform better than other related algorithms previously published.

关键词： Next release problem multi-objective evolutionary algorithm search-based software engineering teaching learning based optimization non-repetitive population

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Hybridizing a multi-objective simulated annealing algorithm with a multi-objective evolutionary algorithm to solve a multi-objective project scheduling problem

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EXPERT SYSTEMS WITH APPLICATIONS 2013年第7期40卷 2421-2434页

作者： Yannibelli, Virginia Amandi, Analia UNCPBA Fac Cs Exactas ISISTAN Res Inst RA-7000 Buenos Aires DF Argentina Consejo Nacl Invest Cient & Tecn RA-1033 Buenos Aires DF Argentina

In this paper, a multi-objective project scheduling problem is addressed. This problem considers two conflicting, priority optimization objectives for project managers. One of these objectives is to minimize the project makespan. The other objective is to assign the most effective set of human resources to each project activity. To solve the problem, a multi-objective hybrid search and optimization algorithm is proposed. This algorithm is composed by a multi-objective simulated annealing algorithm and a multi-objective evolutionary algorithm. The multi-objective simulated annealing algorithm is integrated into the multi-objective evolutionary algorithm to improve the performance of the evolutionary-based search. To achieve this, the behavior of the multi-objective simulated annealing algorithm is self-adaptive to either an exploitation process or an exploration process depending on the state of the evolutionary-based search. The multi-objective hybrid algorithm generates a number of near non-dominated solutions so as to provide solutions with different trade-offs between the optimization objectives to project managers. The performance of the multi-objective hybrid algorithm is evaluated on nine different instance sets, and is compared with that of the only multi-objective algorithm previously proposed in the literature for solving the addressed problem. The performance comparison shows that the multi-objective hybrid algorithm significantly outperforms the previous multi-objective algorithm. (c) 2012 Elsevier Ltd. All rights reserved.

关键词： multi-objective project scheduling multi-objective hybrid algorithm multi-objective simulated annealing algorithm multi-objective evolutionary algorithm Non-dominated solutions

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Solving multi-objective energy-saving flexible job shop scheduling problem by hybrid search genetic algorithm☆

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COMPUTERS & INDUSTRIAL ENGINEERING 2025年 200卷

作者： Hao, Linyuan Zou, Zhiyuan Liang, Xu Beijing Informat Sci & Technol Univ Comp Sci Beijing Peoples R China

For the issue of energy consumption in the multi-objective flexible job shop scheduling problem (MOFJSP), balancing machine load is significant for enhancing environmental sustainability and cost efficiency in intelligent manufacturing. Most studies overlook the critical role of machine load in energy consumption. Therefore, this paper establishes a multi-objective energy-saving flexible job shop scheduling model with the objectives of minimizing the maximum and total machine load, makespan, then proposes a hybrid search genetic algorithm (HSGA) to solve this problem. Firstly, for enhancing population diversity, this paper proposes a cluster-based initial solution selection strategy, preventing the issue of limited search range caused by high similarity among initial solutions. Secondly, to broaden the search scope for multi-objective optimal solutions, this paper proposes a population selection strategy that considers individual neighborhood density and designed multiple neighborhood operators for variable neighborhood search (VNS). Finally, this paper proposes an adaptive strategy to dynamically adjust crossover and mutation probabilities for genetic operators, achieving a balance between global and local search. Experimental results show that the HSGA exhibits superior performance.

关键词： multi-objective optimization Flexible job shop scheduling problem multi-objective evolutionary algorithm

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Quantitative Trait Loci Mapping Problem: An Extinction-Based multi-objective evolutionary algorithm Approach

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algorithmS 2013年第3期6卷 546-564页

作者： Ghaffarizadeh, Ahmadreza Eftekhari, Mehdi Esmailizadeh, Ali K. Flann, Nicholas S. Utah State Univ Dept Comp Sci Logan UT 84341 USA Shahid Bahonar Univ Kerman Dept Comp Engn Kerman *** Iran Shahid Bahonar Univ Kerman Dept Anim Sci Kerman *** Iran Inst Syst Biol Seattle WA 98109 USA Synthet Biomfg Inst Logan UT 84322 USA

The Quantitative Trait Loci (QTL) mapping problem aims to identify regions in the genome that are linked to phenotypic features of the developed organism that vary in degree. It is a principle step in determining targets for further genetic analysis and is key in decoding the role of specific genes that control quantitative traits within species. Applications include identifying genetic causes of disease, optimization of cross-breeding for desired traits and understanding trait diversity in populations. In this paper a new multi-objective evolutionary algorithm (MOEA) method is introduced and is shown to increase the accuracy of QTL mapping identification for both independent and epistatic loci interactions. The MOEA method optimizes over the space of possible partial least squares (PLS) regression QTL models and considers the conflicting objectives of model simplicity versus model accuracy. By optimizing for minimal model complexity, MOEA has the advantage of solving the over-fitting problem of conventional PLS models. The effectiveness of the method is confirmed by comparing the new method with Bayesian Interval Mapping approaches over a series of test cases where the optimal solutions are known. This approach can be applied to many problems that arise in analysis of genomic data sets where the number of features far exceeds the number of observations and where features can be highly correlated.

关键词： QTL quantitative trait loci mapping multi-objective evolutionary algorithm partial least squares extinction-based EAs

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multi-objective evolutionary Hindsight Experience Replay for Robot Manipulation Tasks

Multi-Objective Evolutionary Hindsight Experience Replay for...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Sayar, Erdi Iacca, Giovanni Knoll, Alois Tech Univ Munich Munich Germany Univ Trento Trento Italy

ISBN: (纸本)9798400704949

Reinforcement learning (RL) algorithms often face challenges in efficiently learning effective policies for sparse-reward multi-goal robot manipulation tasks, thus requiring a vast amount of experiences. The state-of-the-art algorithm in the field, Hindsight Experience Replay (HER), addresses this issue by using failed trajectories and replacing the desired goal with hindsight goals. However, HER performs poorly when the desired goal is distant from the initial state. To address this limitation, Hindsight Goal Generation (HGG) has been proposed, which generates a curriculum of goals from already visited states. This curriculum generation is based on a single objective, and does not take obstacles into account. Here, we make a step forward by proposing multi-objective evolutionary Hindsight Experience Replay (MOEHER), a novel curriculum RL algorithm that reformulates curriculum generation considering multiple objectives and obstacles. MOEHER utilizes NSGA-II to generate a curriculum that is optimized w.r.t. four objectives, namely the Q-function, the goal-proximity function, and two distance metrics, while simultaneously satisfying constraints on the obstacles. We evaluate MOEHER on four different sparse-reward robot manipulation tasks, with and without obstacles, and compare it with HER and HGG. The results demonstrate that MOEHER surpasses or performs on par with these methods on the tested tasks.

关键词： multi-objective evolutionary algorithm Hindsight Experience Replay Reinforcement Learning Curriculum Learning Robot Manipulation

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multi-objective evolutionary design of central pattern generator network for biomimetic robotic fish

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COMPLEX & INTELLIGENT SYSTEMS 2023年第2期9卷 1707-1727页

作者： Li, Wei Kun Chen, Hao Cui, Wei Cheng Song, Chang Hui Chen, Lin Ke Westlake Inst Adv Study Inst Adv Technol 18 Shilongshan Rd Hangzhou 310024 Zhejiang Peoples R China Zhejiang Univ Zhejiang Univ Westlake Univ Joint Training Hangzhou 310024 Peoples R China Westlake Univ Sch Engn 18 Shilongshan Rd Hangzhou 310024 Zhejiang Peoples R China Key Lab Coastal Environm & Resources Zhejiang Pro 18 Shilongshan Rd Hangzhou 310024 Zhejiang Peoples R China

Fish-inspired motion is an important research area with many applications in real-world tasks such as underwater vehicles or robotic fish control design. Owing to robust, smooth, and coordinated oscillatory signals generated by Central Pattern Generators (CPGs) for locomotion control of robots with multiple degrees of freedom, CPGs are the most versatile solution for robotic control systems, especially in robotic fish. However, tuning central pattern generator parameters is difficult for complex mechanical system designs. Besides, most current CPG-based methods only consider one aspect (e.g., speed), which widens the gap between theory and practice in robotic fish design. Also, it may affect the practical applicability of the designed motion model to a certain extent. This paper addresses this problem by constructing a multi-objective evolutionary design of a central pattern generator network to control the proposed biomimetic robotic fish. A new CPG model is proposed to help biomimetic robotic fish swim efficiently. In addition, an efficient multi-objective evolutionary algorithm proposed in our previous work is also applied to assist the biomimetic robotic fish in obtaining faster-swimming speed, good stability of the head, and higher propulsive efficiency simultaneously. Considering that the result of multi-objective optimization is a set of non-dominated solutions rather than a solution, a screening method based on fuzzy theory is adopted to assist decision-makers in selecting the most appropriate solution. Based on this, the control model of biomimetic robotic fish is constructed. The proposed control model is simulated and compared with seven well-known algorithms and a series of robotic fish designs. After that, the proposed control model is validated with extensive experiments on the actual biomimetic robotic fish. Simulations and experiments demonstrate the proposed control model's effectiveness and good performance, especially when the control model has be

关键词： Central pattern generators Biomimetic robotic fish multi-objective evolutionary algorithm Robotic fish control model

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A pattern-driven solution for designing multi-objective evolutionary algorithms

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NATURAL COMPUTING 2020年第3期19卷 481-494页

作者： Guizzo, Giovani Vergilio, Silvia R. DInf Fed Univ Parana CP 19081 BR-19031970 Curitiba PR Brazil

multi-objective evolutionary algorithms (MOEAs) have been widely studied in the literature, which led to the development of several frameworks and techniques to implement them. Consequently, the reusability, scalability and maintainability became fundamental concerns in the development of such algorithms. To this end, the use of design patterns (DPs) can benefit, ease and improve the design of MOEAs. DPs are reusable solutions for common design problems, which can be applied to almost any context. Despite their advantages to decrease coupling, increase flexibility, and allow an easier design extension, DPs have been underexplored for MOEA design. In order to contribute to this research topic, we propose a pattern-driven solution for the design of MOEAs. The MOEA designed with our solution is compared to another MOEA designed without it. The comparison considered: the Integration and Test Order (ITO) problem and the Traveling Salesman problem (TSP). Obtained results show that the use of this DP-driven solution allows the reuse of MOEA components, without decreasing the quality, in terms of hypervolume. This means that the developer can extend the algorithms to include other components using only object-oriented mechanisms in an easier way, while maintaining the expected results.

关键词： Meta-heuristic design pattern multi-objective evolutionary algorithm Software testing Hyper-heuristic

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