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Design of a dynamic model of genes with multiple autonomous regulatory modules by evolutionary computations

Procedia computer science 2010年第1期1卷 999-1008页

作者： Alexander V Spirov David M Holloway State University of New York at Stony Brook Computer Science Department and Center of Excellence in Wireless & Information Technology Stony Brook University Research & Development Park 1500 Stony Brook Road Stony Brook NY 11794-6040 USA.

A new approach to design a dynamic model of genes with multiple autonomous regulatory modules by evolutionary computations is proposed. The approach is based on Genetic Algorithms (GA), with new crossover operators especially designed for these purposes. The new operators use local homology between parental strings to preserve building blocks found by the algorithm. The approach exploits the subbasin-portal architecture of the fitness functions suitable for this kind of evolutionary modeling. This architecture is significant for Royal Road class fitness functions. Two real-life Systems Biology problems with such fitness functions are implemented here: evolution of the bacterial promoter rrnPl and of the enhancer of the Drosophila even-skipped gene. The effectiveness of the approach compared to standard GA is demonstrated on several benchmark and real-life tasks.

关键词： evolutionary computations Genetic algorithms Crossover operators Building blocks Dynamic models of genes Multiple autonomous regulatory elements

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Divide-and-conquer initialization and mutation operators for the large-scale mixed Capacitated Arc Routing Problem

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2025年第2期321卷 383-396页

作者： Oliveira, Diogo F. Martins, Miguel S. E. Sousa, Joao M. C. Vieira, Susana M. Figueira, Jose Rui Univ Lisbon MARETEC Inst Super Tecn Ave Rovisco Pais 1 P-1049001 Lisbon Portugal Univ Lisbon IDMEC Inst Super Tecn P-1049001 Lisbon Portugal Univ Lisbon CEGIST Inst Super Tecn P-1049001 Lisbon Portugal

As cities continue to grow, thus will the size of the routing problems necessary to the functioning of all cities. Applications such as waste collection, road maintenance, or winter gritting span over an entire city, therefore algorithms must be designed to solve large-scale problems. The Capacitated Arc Routing Problem (CARP) is an important combinatorial optimization problem that is typically used to model these applications. Classical algorithms for CARP struggle to find quality solutions for large-scale instances with thousands of services within a reasonable computational budget. To address the issue of scalability, several divide-and-conquer heuristics have recently been proposed. In this paper, we propose to integrate divide-and-conquer heuristics into a memetic algorithm by adapting these as an initialization method and as a mutation operator. The resulting algorithm, which we call Memetic Algorithm with Divide-and-Conquer Mutation (MADCoM), outperforms stateof-the-art algorithms on large-scale instances and new best solutions are found for 17 instances of MCARP, 2 of which are optimal solutions, and for 23 large-scale CARP instances. These results demonstrate the potential of the integration of divide-and-conquer heuristics into metaheuristics as a strategy to efficiently solve large-scale problems.

关键词： evolutionary computations Dynamic programming Divide-and-conquer Large scale optimization Capacitated arc routing

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evolutionary Multiobjective Optimization for Large-Scale Portfolio Selection With Both Random and Uncertain Returns

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IEEE TRANSACTIONS ON evolutionary COMPUTATION 2025年第1期29卷 76-90页

作者： Liu, Weilong Zhang, Yong Liu, Kailong Quinn, Barry Yang, Xingyu Peng, Qiao Guangdong Univ Technol Sch Management Guangzhou 510520 Guangdong Peoples R China Shandong Univ Sch Control Sci & Engn Jinan 250061 Peoples R China Queens Univ Queens Business Sch Belfast BT9 5EE North Ireland Queens Univ Belfast Grp Informat Technol Analyt & Operat Belfast BT9 5EE North Ireland

With the advent of Big Data, managing large-scale portfolios of thousands of securities is one of the most challenging tasks in the asset management industry. This study uses an evolutionary multiobjective technique to solve large-scale portfolio optimization problems with both long-term listed and newly listed securities. The future returns of long-term listed securities are defined as random variables whose probability distributions are estimated based on sufficient historical data, while the returns of newly listed securities are defined as uncertain variables whose uncertainty distributions are estimated based on experts' knowledge. Our approach defines security returns as theoretically uncertain random variables and proposes a three-moment optimization model with practical trading constraints. In this study, a framework for applying arbitrary multiobjective evolutionary algorithms to portfolio optimization is established, and a novel evolutionary algorithm based on large-scale optimization techniques is developed to solve the proposed model. The experimental results show that the proposed algorithm outperforms state-of-the-art evolutionary algorithms in large-scale portfolio optimization.

关键词： Optimization Portfolios Security Random variables Uncertainty evolutionary computation Mathematical models evolutionary computations large-scale investment multiobjective optimization portfolio optimization uncertain random variable

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A multi-objective evolutionary algorithm with mutual-information-guided improvement phase for feature selection in complex manufacturing processes

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2025年第3期323卷 952-965页

作者： Li, An-Da He, Zhen Wang, Qing Zhang, Yang Ma, Yanhui Tianjin Univ Commerce Sch Management Tianjin 300134 Peoples R China Tianjin Univ Commerce Res Ctr Management Innovat & Evaluat Tianjin 300134 Peoples R China Tianjin Univ Coll Management & Econ Tianjin 300072 Peoples R China Tianjin Univ Technol Sch Management Tianjin 300384 Peoples R China

Complex manufacturing processes (CMP) involve numerous features that impact product quality. Therefore, selecting key process features (KPF) is crucial for effective quality prediction and control in CMPs. This paper proposes a KPF (feature) selection method for the high-dimensional CMP data. The KPF selection problem is formulated as a bi-objective combinatorial optimization task of maximizing the geometric mean measure and minimizing the number of selected features. To solve this challenging high-dimensional KPF selection problem, we propose a novel multi-objective evolutionary algorithm (MOEA) called NSGAII-MIIP. NSGAIIMIIP applies an improvement phase (called MIIP) to purify the non-dominated solutions obtained by genetic operators during the iteration process to improve the FS performance. The improvement phase is guided by a mutual-information-based feature importance measure considering both a feature's relevance degree to class (product quality level) and its redundancy degree to selected features. This allows MIIP to efficiently update non-dominated solutions by selecting relevant features and eliminating redundant features. Moreover, MIIP is seamlessly integrated into the solution ranking process of NSGAII-MIIP so that solutions from the improvement phase can be ranked together with original solutions in the population efficiently. Experiments on eight datasets show that NSGAII-MIIP has better KPF selection performance than eight state-of-the-art multi- objective FS methods. Moreover, NSGAII-MIIP exhibits superior search performance compared to eight typical multi-objective optimization algorithms.

关键词： evolutionary computations Multi-objective optimization Feature selection Improvement phase Quality prediction

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A fully parallel multi-objective genetic algorithm for optimization of flexible shop floor production performance and schedule stability under dynamic environments

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ANNALS OF OPERATIONS RESEARCH 2025年 1-36页

作者： Luo, Jia El Baz, Didier Xue, Rui Hu, Jinglu Shi, Lei Beijing Univ Technol Coll Econ & Management 100 Ping Yuan Beijing 100124 Peoples R China Beijing Univ Technol Chongqing Res Inst Chongqing 401121 Peoples R China Waseda Univ Grad Sch Informat Prod & Syst 2-7 Hibikino Kitakyushu Fukuoka 8080135 Japan Univ Toulouse LAAS CNRS CNRS 7 Ave Colonel Roche F-31031 Toulouse France Commun Univ China State Key Lab Media Convergence & Commun Beijing 100024 Peoples R China Yunnan Normal Univ Key Lab Educ Informatizat Nationalities Minist Educ Kunming 650092 Peoples R China

As the work environment changes dynamically in real-world manufacturing systems, the dynamic flexible job shop scheduling is an essential problem in operations research. Some works have taken rescheduling approaches to solve it as the multi-objective optimization problem. However, previous studies focus more on solution quality improvements while ignoring computation time. To get a quick response in the dynamic scenario, this paper develops a fully parallel Non-dominated Sorting Genetic Algorithm-II (NSGA-II) on GPUs and uses it to solve the multi-objective dynamic flexible job shop scheduling problem. The mathematical model is NP-hard which considers new arrival jobs and seeks a trade-off between shop efficiency and schedule stability. The proposed algorithm can be executed entirely on GPUs with minimal data exchange while parallel strategies are used to accelerate ranking and crowding mechanisms. Finally, numerical experiments are conducted. As our approach keeps the original structure of the conventional NSGA-II without sacrificing the solutions' quality, it gains better performance than other GPU-based parallel methods from four metrics. Moreover, a case study of a large-size instance is simulated at the end and displays the conflicting relationship between the two objectives.

关键词： evolutionary computations Parallel NSGA-II GPU computing Multi-objective optimization Flexible job shop scheduling Dynamic scheduling

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Multi-objective cooperative co-evolution algorithm with hypervolume-based Q-learning for hybrid seru system

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2025年第3期324卷 839-854页

作者： Zhang, Zhecong Yu, Yang Qi, Xuqiang Lu, Yangguang Li, Xiaolong Kaku, Ikou Dalian Univ Technol Inst Adv Intelligence Sch Econ & Management Dalian 116024 Liaoning Peoples R China Sany Heavy Machinery Ltd Big Data Inst Suzhou Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang Liaoning Peoples R China Fuyao Univ Sci & Technol Sch Digital Econ & Management Fuzhou Fujian Peoples R China

The hybrid seru system (HSS), which is an innovative production pattern that emerges from real-world production situations, is practical because it includes both serus and a flow line, allowing temporary workers who are unable to complete all tasks to be assigned to the flow line. We focus on the HSS by minimising both makespan and total labour time. The HSS includes two complicated coupled NP-hard subproblems: hybrid seru formation and hybrid seru scheduling. Thus, we developed a multi-objective cooperative co-evolution algorithm with hypervolume-based Q-learning (MOCC-HVQL) involving hybrid seru formation and scheduling subpopulations, evolved using a genetic algorithm. To achieve balance between exploration and exploitation, a hypervolumebased Q-learning mechanism is proposed to adaptively adjust the number of non-dominated hybrid seru formations/scheduling in co-evolution. To reduce computational time and enhance population diversity, a population partitioning mechanism is proposed. Extensive comparative results demonstrate that the MOCC-HVQL outperforms state-of-the-art algorithms in terms of solution convergence and diversity, with the hypervolume metric increasing by 22 % and inverse generational distance metric decreasing by 76 %. Compared with a pure seru system (PSS), the HSS can significantly reduce training tasks, thereby conserving the training budget. In scenarios with fewer workers and more batches, a positive phenomenon, where the HSS significantly decreases the training tasks relative to PSS while only slightly increasing the makespan, was observed. In specific instances, the HSS reduced the number of training tasks by 50 %, while only increasing the makespan by 10.5 %.

关键词： evolutionary computations Hybrid seru system Cooperative co-evolution algorithm Multi-objective optimisation Hypervolume-based Q -learning

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A fuzzy system based self-adaptive memetic algorithm using population diversity control for evolutionary multi-objective optimization

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SCIENTIFIC REPORTS 2025年第1期15卷 1-26页

作者： Subburaj, Brindha Miruna Joe Amali, S. Vellore Inst Technol Sch Comp Sci & Engn Chennai Tamilnadu India KLN Coll Engn Dept Comp Sci & Engn Sivagangai Tamilnadu India

Simulated by nature's evolution, numerous evolutionary algorithms had been proposed. These algorithms perform better for a particular problem domain and extensive parameter fine tuning and adaptations are required in optimizing problems of varied domain. This paper aims to develop robust and self-adaptive memetic algorithm by combining Differential Evolution based algorithm, a popular population based global search method with the Controlled Local search procedure to solve multi-objective optimization problems. Memetic Algorithm is an enhanced evolutionary algorithm, it combines global search method with local search techniques for faster convergence. Memetic algorithm improves both exploration and exploitation, preventing premature convergence and also refines the current best solutions efficiently. Proposed algorithm is named as Fuzzy based Memetic Algorithm using Diversity control (F-MAD). In F-MAD, population diversity is controlled through the control parameters self-adaptation of Differential Evolution algorithm (DE) such as, crossover rate and scaling factor by using two fuzzy systems. A controlled local search procedure is adapted for guiding convergence process thus balancing explore-exploit cycle. The control parameter self-adaptation and enhanced selection method with controlled local search method aid population diversity control in decision space and attaining optimal solutions with uniform distribution in terms of diversity and convergence metrics in objective space. These characteristics help the proposed method suitable to be extended to different application domain without the need of trial-and-error fine tuning of the parameters. The performance is tested through standard benchmark test problems-CEC 2009 test problems and DTLZ test problem and further validated through performance metrics and statistical test. It is compared with popular optimization algorithms and experiment results indicate that F-MAD perform well than State of-The-Art (SOTA) alg

关键词： evolutionary computations Multi-objective optimization Fuzzy system Population diversity Memetic algorithm

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Mean-VaR portfolio optimization: A nonparametric approach

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2017年第2期260卷 751-766页

作者： Lwin, Khin T. Qu, Rong MacCarthy, Bart L. Anglia Ruskin Univ Fac Sci & Technol Anglia Ruskin IT Res Inst Chelmsford CM1 1SQ Essex England Univ Nottingham Sch Comp Sci Automated Scheduling Optimisat & Planning ASAP Gr Nottingham NG8 1BB England Univ Nottingham Nottingham Univ Business Sch Nottingham NG8 1BB England

Portfolio optimization involves the optimal assignment of limited capital to different available financial assets to achieve a reasonable trade-off between profit and risk. We consider an alternative Markowitz's mean-variance model in which the variance is replaced with an industry standard risk measure, Value-at Risk (VaR), in order to better assess market risk exposure associated with financial and commodity asset price fluctuations. Realistic portfolio optimization in the mean-VaR framework is a challenging problem since it leads to a non-convex NP-hard problem which is computationally intractable. In this work, an efficient learning-guided hybrid multi-objective evolutionary algorithm (MODE-GL) is proposed to solve mean-VaR portfolio optimization problems with real-world constraints such as cardinality, quantity, pre-assignment, round-lot and class constraints. A learning-guided solution generation strategy is incorporated into the multi-objective optimization process to promote efficient convergence by guiding the evolutionary search towards promising regions of the search space. The proposed algorithm is compared with the Non-dominated Sorting Genetic Algorithm (NSGA-II) and the Strength Pareto evolutionary Algorithm (SPEA2). Experimental results using historical daily financial market data from S & P 100 and S & P 500 indices are presented. The results show that MODE-GL outperforms two existing techniques for this important class of portfolio investment problems in terms of solution quality and computational time. The results highlight that the proposed algorithm is able to solve the complex portfolio optimization without simplifications while obtaining good solutions in reasonable time and has significant potential for use in practice. (C) 2017 Elsevier B.V. All rights reserved.

关键词： evolutionary computations Multi-objective constrained portfolio optimization Value at risk Nonparametric historical simulation

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Investigating Multi-View Differential Evolution for solving constrained engineering design problems

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EXPERT SYSTEMS WITH APPLICATIONS 2013年第9期40卷 3370-3377页

作者： de Melo, Vinicius V. Carosio, Grazieli L. C. Univ Fed Sao Paulo Inst Sci & Technol Sao Jose Dos Campos SP Brazil Inst Aeronaut & Espaco Depto Ciencia & Tecnol Aeroespacial Sao Jose Dos Campos SP Brazil

Several constrained and unconstrained optimization problems have been adequately solved over the years thanks to advances in the metaheuristics area. In the last decades, different metaheuristics have been proposed employing new ideas, and hybrid algorithms that improve the original metaheuristics have been developed. One of the most successfully employed metaheuristics is the Differential Evolution. In this paper it is proposed a Multi-View Differential Evolution algorithm (MVDE) in which several mutation strategies are applied to the current population to generate different views at each iteration. The views are then merged according to the winner-takes-all paradigm, resulting in automatic exploration/exploitation balance. MVDE was tested to solve a set of well-known constrained engineering design problems and the obtained results were compared to those from many state-of-the-art metaheuristics. Results show that MVDE was very competitive in the considered problems, largely outperforming several of the compared algorithms. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： evolutionary computations Global optimization Constrained optimization Metaheuristics Differential Evolution

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Feature information prediction algorithm for dynamic multi-objective optimization problems

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2021年第3期295卷 965-981页

作者： Ma, Xuemin Yang, Jingming Sun, Hao Hu, Ziyu Wei, Lixin Yanshan Univ Sch Elect Engn Qinhuangdao 066004 Hebei Peoples R China Yanshan Univ Key Lab Ind Comp Control Engn Hebei Prov Qinhuangdao 066004 Hebei Peoples R China

Dynamic multi-objective optimization problems (DMOPs) contain multiple conflicting goals while track -ing the changing Pareto-optimal front (PF) or Pareto-optimal set (PS). Most algorithms treat the solutions of DMOPs as if they were dealing with static multi-objective optimization problems. However, solutions under different environments may obey different distributions. To solve some of the existing limitations of currently available methods, a dynamic multi-objective optimization algorithm based on feature in-formation prediction (FIP) is proposed. To identify the distribution of solutions after an environmental change, joint distribution adaptation (JDA) is used to construct a mapping function. The feature informa-tion, which is extracted from the objective space at the current time step, is mapped to a higher dimen-sional space. Then the feature information of decision space at the next time step is obtained using the interior point method. Based on this information, the initial population at the next time step is gener-ated when a change is detected. The performance of FIP is validated by comparing it with respect to four state-of-the-art evolutionary algorithms on eight benchmark functions. Experimental results demonstrate that FIP can quickly cover the front with rapidly changing environments. (c) 2021 Published by Elsevier B.V.

关键词： evolutionary computations Dynamic multi-objective optimization Different distributions Feature information prediction Joint distribution adaptation

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