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Hybridizing evolutionary computation and reinforcement learning for the design of almost universal controllers for autonomous robots

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NEUROCOMPUTING 2009年第4-6期72卷 887-894页

作者： Maravall, Dario de Lope, Javier Martin H., Jose Antonio Univ Politecn Madrid Dept Appl Intelligent Syst E-28040 Madrid Spain Univ Politecn Madrid Dept Artificial Intelligence E-28040 Madrid Spain Univ Complutense Madrid Dept Sistemas Informat Computac E-28040 Madrid Spain

In this paper a hybrid approach to the autonomous motion control of robots in cluttered environments with unknown obstacles is introduced. It is shown the efficiency of a hybrid solution by combining the optimization power of evolutionary algorithms and at the same time the efficiency of reinforcement learning in real-time and on-line situations. Experimental results concerning the navigation of a L-shaped robot in a cluttered environment with unknown obstacles are also presented. In such environments there appear real-time and on-line constraints well-suited to RL algorithms and, at the same time, there exists an extremely high dimension of the state space usually unpractical for RL algorithms but well-suited to evolutionary algorithms. The experimental results confirm the validity of the hybrid approach to solve hard real-time, on-line and high dimensional robot motion planning and control problems, where the RL approach shows some difficulties. (C) 2008 Elsevier B.V. All rights reserved.

关键词： evolutionary computation Reinforcement learning State-space granulation Autonomous robot motion control Sensory-motor coordination

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An analog behavioral equivalence boundary search methodology for simulink models and circuit level designs utilizing evolutionary computation

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INTEGRATION-THE VLSI JOURNAL 2016年 55卷 366-375页

作者： Saglamdemir, Muharrem Orkun Berkol, Gonenc Dundar, Gunhan Sen, Alper Bogazici Univ Dept Elect & Elect Engn Istanbul Turkey Bogazici Univ Dept Comp Engn Istanbul Turkey

We propose a simulation-based analog equivalence boundary search methodology for high level Simulink models and their low level HSpice counterparts. The equivalence of high and low level designs is determined by comparing a set of predefined performance parameters measured during the simulation of both models. Our methodology investigates the search space to obtain boundary of input parameters, where both models have equivalent performance parameters. We build an optimization problem, where the error percentage between the performance parameters of both models being less than a specified threshold is defined as success criteria. In this problem, input parameters are determined by utilizing evolutionary computation. At the end of the optimization, the border of equivalence for the models is found for input parameters satisfying the success criteria. We demonstrate the validity of our approach on three designs, an inverter, an operational amplifier, and a buck converter, where our approach proves to be an efficient tool in finding an equivalence boundary of analog circuits and models. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Behavioral equivalence checking Model based design evolutionary computation Simulink Boundary search methodology

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Automatic Program Repair with evolutionary computation

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COMMUNICATIONS OF THE ACM 2010年第5期53卷 109-116页

作者： Weimer, Westley Forrest, Stephanie Le Goues, Claire Nguyen, ThanhVu Univ Virginia Charlottesville VA 22903 USA Univ New Mexico Albuquerque NM 87131 USA

There are many methods for detecting and mitigating software errors but few generic methods for automatically repairing errors once they are discovered. This paper highlights recent work combining program analysis methods with-evolutionary computation to automatically repair bugs in off-the-shelf legacy C programs. The method takes as input the buggy C source code, a failed test case that demonstrates the bug, and a small number of other test cases that encode the required functionality of the program. The repair procedure does not rely on formal specifications, making it applicable to a wide range of extant software for which formal specifications rarely exist.

关键词： evolutionary computation DEBUGGING in computer science LEGACY systems COMPUTER software GENETIC programming (Computer science) SOURCE code (Computer science)

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Surrogate-assisted evolutionary computation: Recent advances and future challenges

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SWARM AND evolutionary computation 2011年第2期1卷 61-70页

作者： Jin, Yaochu Univ Surrey Dept Comp Guildford GU2 7XH Surrey England

Surrogate-assisted, or meta-model based evolutionary computation uses efficient computational models, often known as surrogates or meta-models, for approximating the fitness function in evolutionary algorithms. Research on surrogate-assisted evolutionary computation began over a decade ago and has received considerably increasing interest in recent years. Very interestingly, surrogate-assisted evolutionary computation has found successful applications not only in solving computationally expensive single- or multi-objective optimization problems, but also in addressing dynamic optimization problems, constrained optimization problems and multi-modal optimization problems. This paper provides a concise overview of the history and recent developments in surrogate-assisted evolutionary computation and suggests a few future trends in this research area. (C) 2011 Elsevier B.V. All rights reserved.

关键词： evolutionary computation Surrogates Meta-models Machine learning Expensive optimization problems Model management

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A Hybrid evolutionary computation Approach to Inducing Transfer Classifiers for Domain Adaptation

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IEEE TRANSACTIONS ON CYBERNETICS 2021年第12期51卷 6319-6332页

作者： Nguyen, Bach Hoai Xue, Bing Andreae, Peter Zhang, Mengjie Victoria Univ Wellington Evolutionary Computat Res Grp Wellington 6140 New Zealand

Domain adaptation utilizes learned knowledge from an existing domain (source domain) to improve the classification performance of another related, but not identical, domain (target domain). Most existing domain adaptation methods first perform domain alignment, then apply standard classification algorithms. Transfer classifier induction is an emerging domain adaptation approach that incorporates the domain alignment into the process of building an adaptive classifier instead of using a standard classifier. Although transfer classifier induction approaches have achieved promising performance, they are mainly gradient-based approaches which can be trapped at local optima. In this article, we propose a transfer classifier induction algorithm based on evolutionary computation to address the above limitation. Specifically, a novel representation of the transfer classifier is proposed which has much lower dimensionality than the standard representation in existing transfer classifier induction approaches. We also propose a hybrid process to optimize two essential objectives in domain adaptation: 1) the manifold consistency and 2) the domain difference. Particularly, the manifold consistency is used in the main fitness function of the evolutionary search to preserve the intrinsic manifold structure of the data. The domain difference is reduced via a gradient-based local search applied to the top individuals generated by the evolutionary search. The experimental results show that the proposed algorithm can achieve better performance than seven state-of-the-art traditional domain adaptation algorithms and four state-of-the-art deep domain adaptation algorithms.

关键词： Standards Task analysis evolutionary computation Manifolds Cybernetics Optimization Genetics Classification domain adaptation evolutionary computation (EC) transfer learning

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What is evolutionary computation?

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IEEE SPECTRUM 2000年第2期37卷 26-32页

作者： Fogel, DB Nat Select Inc La Jolla CA USA

Taking a page from Darwin's 'On the origin of the species',computer scientists have found ways to evolve solutions to complexproblems. Harnessing the evolutionary process within a computer providesa means for addressing complex engineering problems-ones involvingchaotic disturbances, randomness, and complex nonlinear dynamics-thattraditional algorithms have been unable to conquer. Indeed, the field ofevolutionary computation is one of the fastest growing areas of computerscience and engineering for just this reason; it is addressing manyproblems that were previously beyond reach, such as rapid design ofmedicines, flexible solutions to supply-chain management problems, andrapid analysis of battlefield tactics for defense. Potentially, thefield may fulfil the dream of artificial intelligence: a computer thatcan learn on its own and become an expert in any chosen area

关键词： evolutionary computation artificial intelligence battlefield tactics chaotic disturbances complex engineering problems complex nonlinear dynamics complex problems solutions evolution randomness supply-chain management Random Number Generators Dreams Supply chain management Artificial Intelligence evolutionary algorithm Nonlinear Dynamics

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An evolutionary computation Framework for Task Off-and-Downloading Scheduling in Mobile Edge Computing

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IEEE INTERNET OF THINGS JOURNAL 2024年第12期11卷 22399-22412页

作者： Yin, Lu Sun, Jin Wu, Zebin Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Jiangsu Peoples R China

When designing task scheduling algorithms in mobile edge computing (MEC), the mobile device (MD)'s mobility becomes an important concern, since the change in MD's location would affect the data transmission rate, leading to fluctuations in task transmission duration and completion time. In this article, we study a mobility-aware task off-and-downloading scheduling problem in MEC, considering both the communication delay and energy consumption caused by the data offloading and the result downloading. We first formulate a mathematical optimization model of the studied problem and prove its NP-hardness. To explore high-quality task scheduling decisions, we propose a swarm intelligence (SI) algorithm-based evolutionary computation (START) framework. The main technical innovations of START include a solution representation of off-and-downloading sequence, an exponential probability model-based mapping operator, and a task dispatching heuristic. Specifically, the solution representation makes START applicable to a wide range of SI algorithms. The mapping operator establishes the link between individual space and solution space, in which the critical parameter is determined by a rigorous theoretical analysis. The task dispatching strategy is the only component of the START framework that is relevant to the particular problem, providing the extensibility of applying START to solving other problems. In experiments, we create a real-world MD trajectory data set MDT-NJUST, and integrate several representative SI algorithms to justify the performance of START in solving the scheduling problem. Experimental results also verify the conclusion drawn from the theoretical analysis on critical parameter determination.

关键词： evolutionary computation mobile edge computing (MEC) swarm intelligence (SI) algorithm task scheduling user mobility

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Parameter optimisation of sliding window algorithm based on ensemble multi-objective evolutionary computation

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INTERNATIONAL JOURNAL OF BIO-INSPIRED computation 2022年第4期19卷 228-237页

作者： Li, Guang Wang, Jie Liang, Jing Yue, Caitong Lou, Tai-shan Zhengzhou Univ Sch Elect Engn Zhengzhou 450001 Peoples R China Henan Inst Technol Sch Elect Engn & Automat Xinxiang 453003 Henan Peoples R China Zhengzhou Univ Light Ind Sch Elect & Informat Engn Zhengzhou 450002 Peoples R China

The parameters of sliding window algorithm are difficult to determine. Therefore, a sliding window-based method for parameter optimisation of data stream trend anomaly detection algorithm is proposed in this study. This method regards the data stream anomaly detection as a two-objective optimisation problem. Three optimisation algorithms and ensemble strategies were used to obtain the optimal parameter settings of the algorithm. With this strategy, it is no longer difficult to determine the parameters of the data stream trend anomaly detection algorithm based on the sliding window. Through verification of multiple real parameter data in Tarim Oilfield, it could be known that this method could realise the optimal parameter settings, which provides a reference for the parameter setting of the data stream trend anomaly detection algorithm based on sliding window.

关键词： sliding window parameter optimisation anomaly detection evolutionary computation ensemble strategy

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Line Topology Identification Using Multiobjective evolutionary computation

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2010年第3期59卷 715-729页

作者： Sales, Claudomiro Rodrigues, Roberto M. Lindqvist, Fredrik Costa, Joao Klautau, Aldebaro Ericson, Klas Rius i Riu, Jaume Borjesson, Per Ola Fed Univ Para Inst Technol Elect Engn Grad Program BR-66075110 Belem Para Brazil Lund Univ Elect & Informat Technol Dept S-22100 Lund Sweden Ericsson AB Broadband Technol Lab Dept S-16480 Stockholm Sweden

The broadband capacity of the twisted-pair lines strongly varies within the copper access network. It is therefore important to assess the ability of a digital subscriber line (DSL) to support the DSL services prior to deployment. This task is handled by the line qualification procedures, where the identification of the line topology is an important part. This paper presents a new method, denoted topology identification via model-based evolutionary computation (TIMEC), for line topology identification, where either one-port measurements or both one-and two-port measurements are utilized. The measurements are input to a model-based multiobjective criterion that is minimized by a genetic algorithm to provide an estimate of the line topology. The inherent flexibility of TIMEC enables the incorporation of a priori information, e. g., the total line length. The performance of TIMEC is evaluated by computer simulations with varying degrees of information. Comparison with a state-of-art method indicates that TIMEC achieves better results for all the tested lines when only one-port measurements are used. The results are improved when employing both one-and two-port measurements. If a rough estimate of the total length is also used, near-perfect estimation is obtained for all the tested lines.

关键词： Digital subscriber line (DSL) double-ended line testing (DELT) evolutionary computation line qualification (LQ) line topology identification multiobjective optimization single-ended line testing (SELT)

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Multiobjectivization of Single-Objective Optimization in evolutionary computation: A Survey

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IEEE TRANSACTIONS ON CYBERNETICS 2023年第6期53卷 3702-3715页

作者： Ma, Xiaoliang Huang, Zhitao Li, Xiaodong Qi, Yutao Wang, Lei Zhu, Zexuan Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China RMIT Univ Sch Sci Comp Sci & Software Engn Melbourne Vic 3001 Australia Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China Shenzhen Pengcheng Lab Shenzhen 518055 Peoples R China Southern Univ Sci & Technol Guangdong Prov Key Lab Brain InspiredIntelligent C Shenzhen 518055 Peoples R China

Multiobjectivization has emerged as a new promising paradigm to solve single-objective optimization problems (SOPs) in evolutionary computation, where an SOP is transformed into a multiobjective optimization problem (MOP) and solved by an evolutionary algorithm to find the optimal solutions of the original SOP. The transformation of an SOP into an MOP can be done by adding helper-objective(s) into the original objective, decomposing the original objective into multiple subobjectives, or aggregating subobjectives of the original objective into multiple scalar objectives. Multiobjectivization bridges the gap between SOPs and MOPs by transforming an SOP into the counterpart MOP, through which multiobjective optimization methods manage to attain superior solutions of the original SOP. Particularly, using multiobjectivization to solve SOPs can reduce the number of local optima, create new search paths from local optima to global optima, attain more incomparability solutions, and/or improve solution diversity. Since the term "multiobjectivization" was coined by Knowles et al. in 2001, this subject has accumulated plenty of works in the last two decades, yet there is a lack of systematic and comprehensive survey of these efforts. This article presents a comprehensive multifacet survey of the state-of-the-art multiobjectivization methods. Particularly, a new taxonomy of the methods is provided in this article and the advantages, limitations, challenges, theoretical analyses, benchmarks, applications, as well as future directions of the multiobjectivization methods are discussed.

关键词： Optimization evolutionary computation Computer science Transforms Software engineering Taxonomy Systematics Multiobjectivization via decomposition (MVD) multiobjectivization via helper-objectives (MHOs) multiobjectivization via scalarizing functions (MSFs) multiobjectivization

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