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An evolutionary approach for inferring the model parameters of the hierarchical ELECTRE III method

INFORMATION SCIENCES 2022年 607卷 705-726页

作者： Lopez, Juan Carlos Leyva Solares, Efrain Figueira, Jose Rui Univ Autonoma Occidente Dept Econ & Management Sci Los Mochis Mexico Univ Autonoma Coahuila Fac Accounting & Adm Saltillo Mexico Univ Lisbon CEGIST Inst Super Tecn Lisbon Portugal

Given a finite set of alternatives, the ranking problem statement builds a preference preorder (partial or complete) on this set. In this paper, we are interested in multiple criteria ranking problems with a hierarchical structure of criteria;more precisely, we are interested in the existing hierarchical ELECTRE III method. This method requires eliciting several preference parameters (namely, the weights and the veto thresholds). A direct elicitation of such parameters can be cognitively very demanding;thus, it is adequate to define the parameters in a way that requires much less cognitive effort from the decision-maker. The model parameters can be indirectly elicited by using holistic information provided by the decision-maker;this information can be given in the form of a ranking on a set of reference alternatives and some additional preference information. This paper proposes an aggregation-disaggregation approach for inferring the model parameters of the hierarchical ELECTRE III based on an evolutionary algorithm. To verify the applicability and validity of the proposed preference disaggregation methodology, an illustrative example is addressed regarding the ranking of a set of universities. (c) 2022 Elsevier Inc. All rights reserved.

关键词： Multiple criteria decision analysis E LECTRE III method Multiple criteria hierarchy process evolutionary algorithms Preference disaggregation

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EvoGAN: An evolutionary computation assisted GAN

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NEUROCOMPUTING 2022年 469卷 81-90页

作者： Liu, Feng Wang, Hanyang Zhang, Jiahao Fu, Ziwang Zhou, Aimin Qi, Jiayin Li, Zhibin East China Normal Univ Sch Comp Sci & Technol Shanghai Peoples R China Univ Int Business & Econ Inst Artificial Intelligence & Change Management Shanghai Peoples R China Beijing Univ Posts & Telecommun Sch Comp Sci Beijing Peoples R China East China Normal Univ Inst AI Educ Shanghai Peoples R China East China Normal Univ Sch Psychol & Cognit Sci Shanghai Key Lab Mental Hlth & Psychol Crisis Int Shanghai Peoples R China

The image synthesis technique is relatively well established which can generate facial images that are indistinguishable even by human beings. However, all of these approaches uses gradients to condition the output, resulting in the outputting the same image with the same input. Also, they can only generate images with basic expression or mimic an expression instead of generating compound expression. In real life, however, human expressions are of great diversity and complexity. In this paper, we propose an evo-lutionary algorithm (EA) assisted GAN, named EvoGAN, to generate various compound expressions with any accurate target compound expression. EvoGAN uses an EA to search target results in the data distri-bution learned by GAN. Specifically, we use the Facial Action Coding System (FACS) as the encoding of an EA and use a pre-trained GAN to generate human facial images, and then use a pre-trained classifier to recognize the expression composition of the synthesized images as the fitness function to guide the search of the EA. Combined random searching algorithm, various images with the target expression can be easily sythesized. Quantitative and Qualitative results are presented on several compound expres-sions, and the experimental results demonstrate the feasibility and the potential of EvoGAN. The source code is available at https://***/ECNU-Cross-Innovation-Lab/EvoGAN. (c) 2021 Elsevier B.V. All rights reserved.

关键词： evolutionary algorithms GAN Facial expression synthesis Computational Affection

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Integrated Whale Swarm and Neuro-evolutionary Computing for Large-Scale Sparse Optimization Problems 2nd

Integrated Whale Swarm and Neuro-Evolutionary Computing for ...

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2nd International Conference on Advances in Data-driven Computing and Intelligent Systems, ADCIS 2023

作者： Balakrishnan, Biju Shanthini, B. Amudha, R. St. Peter’s Institute of Higher Education and Research Avadi Tamilnadu Chennai India Hindusthan College of Engineering and Technology Tamilnadu Coimbatore India

ISBN: (纸本)9789819995233

Sparse optimization problems at a large scale present considerable difficulties in diverse fields, such as machine learning, data mining, and signal processing. The aim is to identify the most efficient solutions within expansive search spaces, while constraining the number of non-zero variables. The present study aims to tackle this obstacle by introducing an innovative methodology that integrates whale swarm optimization (WSO) with neuro-evolutionary computing (NEC). The WSO algorithm draws inspiration from the social behavior of humpback whales and presents a proficient approach for investigating and utilizing the search space. The integration of evolutionary algorithms with neural networks by NEC has demonstrated potential in effectively addressing intricate problems. Through the simultaneous evolution of neural networks and optimization processes, the algorithm is capable of adapting and enhancing its search strategy progressively. The study aims to improve the efficiency of WSO in addressing optimization problems characterized by sparsity at a significant scale by incorporating techniques from neuro-evolutionary computing. The method under consideration entails the integration of neural networks into the WSO algorithm with the aim of directing and augmenting the exploration procedure. The study evaluates the efficacy of the hybrid algorithm in relation to established methodologies based on criteria such as the quality of solutions, rate of convergence, and computational efficiency. The results obtained from the experiment provide evidence to support the efficacy of the proposed approach. The hybrid algorithm effectively utilizes the sparsity structure inherent in the problems, resulting in expedited convergence and enhanced precision of the solutions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： evolutionary algorithms

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Design of an automatic Demand-Side Management system based on evolutionary algorithms 14

Design of an automatic Demand-Side Management system based o...

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29th Annual ACM Symposium on Applied Computing, SAC 2014

作者： Galván-López, Edgar Taylor, Adam Clarke, Siobhán Cahill, Vinny Distributed Systems Group School of Computer Science and Statistics Trinity College Dublin Ireland

ISBN: (纸本)9781450324694

Demand-Side Management (DSM) refers to programs that aim to control the energy consumption at the customer side of the meter. Different techniques have been proposed to achieve this. Perhaps the most popular techniques are those based on smart pricing (e.g., critical-peak pricing, real-time pricing). The idea, in a nutshell, is to encourage end users to shift their load consumption based on the price at a particular time (e.g., the higher the price, the less number of electric appliances are expected to be turned on). Motivated by these techniques (e.g., a strong positive correlation between the number of appliances being used and the electricity cost), we propose the use of an stochastic evolutionary-based optimisation technique, evolutionary algorithms, to automatically generate optimal, or nearly optimal, solutions that represent schedules to charge a number of electric vehicles (EVs) with two goals: (a) that each EV is as fully charged as possible at time of departure, and (b) to avoid charging them at the same time, whenever possible (e.g., load reduction at the transformer level). Instead of using a price signal to shift load consumption, we achieve this by considering what all the EVs might do at a particular time, rather than considering an interaction between an utility company and its user, as normally adopted in DSM programs. We argue that exploiting the interaction of these EVs is crucial at achieving excellent results because it carries the notion of smart pricing (e.g., balance energy usage), which is highly popular in DSM systems. Thus, the main contribution of this work is the notion of load shifting, borrowed from smart pricing methods, implemented in an evolutionary-based algorithm to automatically generate optimal solutions. To test our proposed approach, we used a dynamic scenario, where the state of charge of each EV is different for every day of our 28 days testing period. The results obtained by our proposed approach are highly encouraging in

关键词： evolutionary algorithms

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Knowledge Transfer for Dynamic Multi-Objective Optimization With a Changing Number of Objectives

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2024年第6期8卷 4210-4224页

作者： Ruan, Gan Minku, Leandro L. Menzel, Stefan Sendhoff, Bernhard Yao, Xin Univ Birmingham CERCIA Sch Comp Sci Birmingham B15 2SQ W Midlands England Honda Res Inst Europe GmbH D-63073 Offenbach Germany Lingnan Univ Dept Comp & Decis Sci Hong Kong Peoples R China

Different from most other dynamic multi-objective optimization problems (DMOPs), DMOPs with a changing number of objectives usually result in expansion or contraction of the Pareto front or Pareto set manifold. Knowledge transfer has been used for solving DMOPs, since it can transfer useful information from solving one problem instance to solve another related problem instance. However, we show that the state-of-the-art transfer algorithm for DMOPs with a changing number of objectives lacks sufficient diversity when the fitness landscape and Pareto front shape present nonseparability, deceptiveness or other challenging features. Therefore, we propose a knowledge transfer dynamic multi-objective evolutionary algorithm (KTDMOEA) to enhance population diversity after changes by expanding/contracting the Pareto set in response to an increase/decrease in the number of objectives. This enables a solution set with good convergence and diversity to be obtained after optimization. Comprehensive studies using 13 DMOP benchmarks with a changing number of objectives demonstrate that our proposed KTDMOEA is successful in enhancing population diversity compared to state-of-the-art algorithms, improving optimization especially in fast changing environments.

关键词： Dynamic optimization changing objectives knowledge transfer evolutionary algorithms multi-objective optimization

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A multiobjective evolutionary algorithm for achieving energy efficiency in production environments integrated with multiple automated guided vehicles

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KNOWLEDGE-BASED SYSTEMS 2022年 243卷 108315-108315页

作者： He, Lijun Chiong, Raymond Li, Wenfeng Budhi, Gregorius Satia Zhang, Yu Wuhan Univ Technol Sch Transportat & Logist Engn Wuhan 430063 Hubei Peoples R China Univ Newcastle Sch Informat & Phys Sci Callaghan NSW 2308 Australia Petra Christian Univ Informat Dept Surabaya 60236 Indonesia

Increasing energy shortages and environmental pollution have made energy efficiency an urgent concern in manufacturing plants. Most studies looking into sustainable production in general and energy-efficient production scheduling in particular, however, have not paid much attention to logistical factors (e.g., transport and setup). This study integrates multiple automated guided vehicles (AGVs) into a job-shop environment. We propose a multiobjective scheduling model that considers machine processing, sequence-dependent setup and AGV transport, aiming to simultaneously minimize the makespan, total idle time of machines and total energy consumption of both machines and AGVs. To solve this problem, an effective multiobjective evolutionary algorithm (EMOEA) is developed. Within the EMOEA, an efficient encoding/decoding method is designed to represent and decode each solution. A new crossover operator is proposed for AGV assignment and AGV speed sequences. To balance the exploration and exploitation ability of the EMOEA, an opposition-based learning strategy is incorporated. A total of 75 benchmark instances and a real-world case are used for our experimental study. Taguchi analysis is applied to determine the best combination of key parameters for the EMOEA. Extensive computational experiments show that properly increasing the number of AGVs can shorten the waiting time of machines and achieve a balance between economic and environmental objectives for production systems. The experimental results confirm that the proposed EMOEA is significantly better at solving the problem than three other well-known algorithms. Our findings here have significant managerial implications for real-world manufacturing environments integrated with AGVs. (C) 2022 Elsevier B.V. All rights reserved.

关键词： Energy efficiency Automated guided vehicles Job-shop scheduling evolutionary algorithms Multiobjective optimization Opposition-based learning

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Model-based evolutionary algorithms 14

Model-based evolutionary algorithms

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16th Genetic and evolutionary Computation Conference Companion, GECCO 2014 Companion

作者： Thierens, Dirk Bosman, Peter A. N. Utrecht University Department of Information and Computing Sciences Utrecht Netherlands Centrum Wiskunde and Informatica - CWI Centre for Mathematics and Computer Science Amsterdam Netherlands

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Automated evolutionary approach for the design of composite machine learning pipelines

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2022年 127卷 109-125页

作者： Nikitin, Nikolay O. Vychuzhanin, Pavel Sarafanov, Mikhail Polonskaia, Iana S. Revin, Ilia V. Barabanova, Irina Maximov, Gleb Kalyuzhnaya, Anna, V Boukhanovsky, Alexander ITMO Univ St Petersburg Russia

The effectiveness of the machine learning methods for real-world tasks depends on the proper structure of the modeling pipeline. The proposed approach is aimed to automate the design of composite machine learning pipelines, which is equivalent to computation workflows that consist of models and data operations. The approach combines key ideas of both automated machine learning and workflow management systems. It designs the pipelines with a customizable graph-based structure, analyzes the obtained results, and reproduces them. The evolutionary approach is used for the flexible identification of pipeline structure. The additional algorithms for sensitivity analysis, atomization, and hyperparameter tuning are implemented to improve the effectiveness of the approach. Also, the software implementation on this approach is presented as an open-source framework. The set of experiments is conducted for the different datasets and tasks (classification, regression, time series forecasting). The obtained results confirm the correctness and effectiveness of the proposed approach in the comparison with the state-of-the-art competitors and baseline solutions. (C) 2021 Elsevier B.V. All rights reserved.

关键词： AutoML Workflow Composite pipeline Machine learning evolutionary algorithms WMS

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Explaining evolutionary Agent-Based Models via Principled Simplification

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ARTIFICIAL LIFE 2022年第3-4期27卷 143-163页

作者： Barnes, Chloe M. Ghouri, Abida Lewis, Peter R. Aston Univ Birmingham W Midlands England Ontario Tech Univ Oshawa ON Canada

Understanding how evolutionary agents behave in complex environments is a challenging problem. Agents can be faced with complex fitness landscapes derived from multi-stage tasks, interaction with others, and limited environmental feedback. Agents that evolve to overcome these can sometimes access greater fitness, as a result of factors such as cooperation and tool use. However, it is often difficult to explain why evolutionary agents behave in certain ways, and what specific elements of the environment or task may influence the ability of evolution to find goal-achieving behaviours;even seemingly simple environments or tasks may contain features that affect agent evolution in unexpected ways. We explore principled simplification of evolutionary agent-based models, as a possible route to aiding their explainability. Using the River Crossing Task (RCT) as a case study, we draw on analysis in the Minimal River Crossing (RC-) Task testbed, which was designed to simplify the original task while keeping its key features. Using this method, we present new analysis concerning when agents evolve to successfully complete the RCT. We demonstrate that the RC- environment can be used to understand the effect that a cost to movement has on agent evolution, and that these findings can be generalised back to the original RCT. Then, we present new insight into the use of principled simplification in understanding evolutionary agents. We find evidence that behaviour dependent on features that survive simplification, such as problem structure, are amenable to prediction;while predicting behaviour dependent on features that are typically reduced in simplification, such as scale, can be invalid.

关键词： Principled simplification evolutionary algorithms evolutionary agents explainability river crossing neuroevolution

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A novel differential evolutionary algorithm based on population feedback information 4

A novel differential evolutionary algorithm based on populat...

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4th International Conference on Advanced algorithms and Neural Networks, AANN 2024

作者： Yuan, Dongfeng An, Qixun School of Digital Media and Art Design Nanyang Institute of Technology Changjiang Road Henan Nanyang473004 China Zhengzhou Foreign Language School Henan Zhengzhou450001 China

ISBN: (纸本)9781510686106

The differential evolution algorithm has rich, successful experience in parameter settings. How to reasonably control strategies and parameters and effectively utilize feedback information from individuals in the population has become a hot topic of concern. In this paper, an adaptive differential evolution algorithm based on population feedback information (TtDE) is proposed. It uses multi-subpopulation selection methods to guide the direction of evolution. TtDE adopts a framework that combines multi-strategies and multi-parameter sets. One set of fixed strategies and parameter selections is the DE/Best/2 and adaptive parameters, and this method can utilize feedback information from individuals in the population effectively. In addition, during population iteration, the mutant subpopulation may contain better information about individuals than the test subpopulation, and the population diversity reasonably avoids early population convergence or stagnation. The proposed TtDE was evaluated on 23 testing functions of the CEC2005 benchmark suite, and the results showed that it is more competitive than multiple classical DE variants. © 2024 SPIE.

关键词： evolutionary algorithms Genetic algorithms Adaptive control evolutionary optimization Data transmission Fuzzy logic Intelligence systems Lithium Statistical analysis

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