The Experts below are selected from a list of 54567 Experts worldwide ranked by ideXlab platform

Thomas Bousonville - One of the best experts on this subject based on the ideXlab platform.

  • Artificial Evolution - The Two Stage Continuous Parallel Flow Shop Problem with Limited Storage: Modeling and Algorithms
    Lecture Notes in Computer Science, 2002
    Co-Authors: Thomas Bousonville
    Abstract:

    Two stage continuous parallel flow shops with limited intermediate storage are common in process industry. Because of its computational complexity and continuous nature only special cases of the general problem have been solved to optimality so far. The focal point of this paper is the examination of appropriate indirect discrete representations that allow the application of evolutionary methods combined with local search. The results give insight into the most appropriate neighborhood structure and the usefulness of Heuristic Information for the guidance of the search process. In particular it is shown that the conceived Memetic Algorithm when submitted to a rigid time limit yields better results by using additional Heuristic Information.

  • The two stage continuous parallel flow shop problem with limited storage: Modeling and algorithms
    Lecture Notes in Computer Science, 2002
    Co-Authors: Thomas Bousonville
    Abstract:

    Two stage continuous parallel flow shops with limited intermediate storage are common in process industry. Because of its computational complexity and continuous nature only special cases of the general problem have been solved to optimality so far. The focal point of this paper is the examination of appropriate indirect discrete representations that allow the application of evolutionary methods combined with local search. The results give insight into the most appropriate neighborhood structure and the usefulness of Heuristic Information for the guidance of the search process. In particular it is shown that the conceived Memetic Algorithm when submitted to a rigid time limit yields better results by using additional Heuristic Information.

Cong Li - One of the best experts on this subject based on the ideXlab platform.

  • fast robot motor skill acquisition based on bayesian inspired policy improvement
    International Conference on Intelligent Robotics and Applications, 2019
    Co-Authors: Jian Fu, Siyuan Shen, Cong Li
    Abstract:

    Learning from demonstration with the reinforcement learning (LfDRL) framework has been successfully applied to acquire the skill of robot movement. However, the optimization process of LfDRL usually converges slowly on the condition that new task is considerable different from imitation task. We in this paper proposes a ProMPs-Bayesian-PI\(^2\) algorithms to expedite the transfer process. The main ideas is adding new Heuristic Information to guide optimization search other than random search from the stats of imitation learning. Specifically, we use the result of Bayesian estimation as the Heuristic Information to guide the PI\(^2\) when it random search. Finally, we verify this method by UR5 and compare it with the traditional method of ProMPs-PI\(^2\). The experimental results show that this method is feasible and effective.

  • the partition Heuristic Information extraction algorithm of unstructured data
    International Conference on Cloud Computing, 2013
    Co-Authors: Cong Li, Luo Zhong
    Abstract:

    In this paper, we propose a method that extracts attributes of given entity from unstructured data for the field of logistics by using the idea of divide and conquer as to the characters of logistics Information. After the full study of logistics Information, we make a statistical analysis for the text logistics Information and summarize the common attributes of text Information entity. According to the different attributes and attribute values, we divided text Information entity by the idea of divide and conquer. As to the entity we get from last step we make an internal processing based on segmentation method of tagging and graph. We extracted valuable attributes and attribute values from the unstructured data. Experimental results show that this method is valid for the logistics Information which we achieve from a well-known logistics system.

Karen M. Feigh - One of the best experts on this subject based on the ideXlab platform.

  • Heuristic Information Acquisition and Restriction Rules for Decision Support
    IEEE Transactions on Human-Machine Systems, 2017
    Co-Authors: Marc C. Canellas, Karen M. Feigh
    Abstract:

    The research question addressed by this study was: What Information should be presented to or hidden from decision makers in order to facilitate high performance in decision tasks? Previous research on Information search is limited because of its focus on analytic Information acquisition methods; analytic because of the focus on maximizing expected utility; acquisition because of the focus on what Information should be added or searched for. Implementing these methods requires reliable assessments of probabilities, cue weights, and cue values and does not provide suggestions on how to restrict or remove Information. In this work, we present four Heuristics, or simple rules, for acquiring and restricting Information that only require an understanding of the distribution of known and unknown Information (Information imbalance and complete attribute pairs). The rules were tested on a range of analytic and Heuristic decision strategies within two-option decision tasks across 15 real-world environments. Though the rules are transparent and easy to communicate (create a balance of Information between options and within cues) and require little Information to perform, the simulation results show that the rules were generally effective across all environments. For almost every combination of rule and strategy, the Heuristic restriction rules were shown to be more likely to increase rather than decrease accuracy. In every combination, the Heuristic acquisition rules were shown to increase accuracy more than acquiring Information that did not adhere to the rules. Further statistical and mathematical analysis showed that rules are mediated by strategies’ full Information accuracy and estimates of missing Information.

Tan Yi-ming - One of the best experts on this subject based on the ideXlab platform.

  • Extracting symbolic rules from support vector machines based on the Heuristic Information
    Journal of Computer Applications, 2008
    Co-Authors: Tan Yi-ming
    Abstract:

    A new approach for symbolic rules extraction from support vector machines based on Heuristic Information was proposed,which solved the attribute selection and the division of attribute space.The position and shape characteristics of the classification hypersurface of Support Vector Regression(SVR)were used as Heuristic Information to direct the attribute selection and the division of attribute space.Then,the algorithm was given.Experiment results show that the proposed approach can improve the validity of the extracted rules remarkably compared to other rule extracting approaches,especially for complicated classification problems.

Marc C. Canellas - One of the best experts on this subject based on the ideXlab platform.

  • Heuristic Information Acquisition and Restriction Rules for Decision Support
    IEEE Transactions on Human-Machine Systems, 2017
    Co-Authors: Marc C. Canellas, Karen M. Feigh
    Abstract:

    The research question addressed by this study was: What Information should be presented to or hidden from decision makers in order to facilitate high performance in decision tasks? Previous research on Information search is limited because of its focus on analytic Information acquisition methods; analytic because of the focus on maximizing expected utility; acquisition because of the focus on what Information should be added or searched for. Implementing these methods requires reliable assessments of probabilities, cue weights, and cue values and does not provide suggestions on how to restrict or remove Information. In this work, we present four Heuristics, or simple rules, for acquiring and restricting Information that only require an understanding of the distribution of known and unknown Information (Information imbalance and complete attribute pairs). The rules were tested on a range of analytic and Heuristic decision strategies within two-option decision tasks across 15 real-world environments. Though the rules are transparent and easy to communicate (create a balance of Information between options and within cues) and require little Information to perform, the simulation results show that the rules were generally effective across all environments. For almost every combination of rule and strategy, the Heuristic restriction rules were shown to be more likely to increase rather than decrease accuracy. In every combination, the Heuristic acquisition rules were shown to increase accuracy more than acquiring Information that did not adhere to the rules. Further statistical and mathematical analysis showed that rules are mediated by strategies’ full Information accuracy and estimates of missing Information.