The Experts below are selected from a list of 4818 Experts worldwide ranked by ideXlab platform

Francisco Herrera - One of the best experts on this subject based on the ideXlab platform.

  • Studying the behavior of a Multiobjective Genetic Algorithm to design fuzzy rule-based classification systems for imbalanced data-sets
    2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011), 2011
    Co-Authors: P. Villar, Alberto Fernández, Francisco Herrera
    Abstract:

    This paper studies the behavior of a Multiobjective Genetic Algorithm for jointly performing a feature selection and granularity learning for Fuzzy Rule-Based Classification Systems in the scenario of imbalanced data-sets. We refer to imbalanced data-sets when the class distribution is not uniform, a situation that it is present in many real application areas. We consider two different measures, one for the precision of the model and other for its complexity as the two objectives to optimize. In one previous approach, we aggregate these two measures in a single-objective Genetic Algorithm, and thus, a Multiobjective approach of that Genetic Algorithm would yield a set of models with different trade-off between high accuracy and low complexity rather than a unique model, provided by the single-objective Genetic Algorithm. The experimental analysis, carried out over a wide range of imbalanced data-sets, shows that our approach is able to obtain a set of models with good trade-off between the two objectives considered but it is an open problem how to select the solution with best prediction ability from the whole set of solutions obtained.

  • Multiobjective Genetic Algorithm for Extracting Subgroup Discovery Fuzzy Rules
    2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making, 2007
    Co-Authors: Maria Jose Del Jesus, Pedro Gonzalez, Francisco Herrera
    Abstract:

    This paper presents a Multiobjective Genetic Algorithm for obtaining fuzzy rules for subgroup discovery. This kind of fuzzy rules lets us represent knowledge about patterns of interest in an explanatory and understandable form which can be used by the expert. The Multiobjective Algorithm proposed in this paper defines three objectives. One of them is used as a restriction on the rules in order to obtain a Pareto front composed of a set of quite different rules with a high degree of coverage over the examples. The other two objectives take into account the support and the confidence of the rules. The use of the mentioned objective as restriction allows us the extraction of a set of rules which describe more complete information on most of the examples. Experimental evaluation of the Algorithm, applying it to a market problem shows the validity of the proposal obtaining novel and valuable knowledge for the experts

  • MCDM - Multiobjective Genetic Algorithm for Extracting Subgroup Discovery Fuzzy Rules
    2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making, 2007
    Co-Authors: M.j. Del Jesus, Pedro Gonzalez, Francisco Herrera
    Abstract:

    This paper presents a Multiobjective Genetic Algorithm for obtaining fuzzy rules for subgroup discovery. This kind of fuzzy rules lets us represent knowledge about patterns of interest in an explanatory and understandable form which can be used by the expert. The Multiobjective Algorithm proposed in this paper defines three objectives. One of them is used as a restriction on the rules in order to obtain a Pareto front composed of a set of quite different rules with a high degree of coverage over the examples. The other two objectives take into account the support and the confidence of the rules. The use of the mentioned objective as restriction allows us the extraction of a set of rules which describe more complete information on most of the examples. Experimental evaluation of the Algorithm, applying it to a market problem shows the validity of the proposal obtaining novel and valuable knowledge for the experts

P. Villar - One of the best experts on this subject based on the ideXlab platform.

  • Studying the behavior of a Multiobjective Genetic Algorithm to design fuzzy rule-based classification systems for imbalanced data-sets
    2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011), 2011
    Co-Authors: P. Villar, Alberto Fernández, Francisco Herrera
    Abstract:

    This paper studies the behavior of a Multiobjective Genetic Algorithm for jointly performing a feature selection and granularity learning for Fuzzy Rule-Based Classification Systems in the scenario of imbalanced data-sets. We refer to imbalanced data-sets when the class distribution is not uniform, a situation that it is present in many real application areas. We consider two different measures, one for the precision of the model and other for its complexity as the two objectives to optimize. In one previous approach, we aggregate these two measures in a single-objective Genetic Algorithm, and thus, a Multiobjective approach of that Genetic Algorithm would yield a set of models with different trade-off between high accuracy and low complexity rather than a unique model, provided by the single-objective Genetic Algorithm. The experimental analysis, carried out over a wide range of imbalanced data-sets, shows that our approach is able to obtain a set of models with good trade-off between the two objectives considered but it is an open problem how to select the solution with best prediction ability from the whole set of solutions obtained.

  • A Multiobjective Genetic Algorithm for feature selection and granularity learning in fuzzy-rule based classification systems
    Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569), 2001
    Co-Authors: O. Cordon, F. Herrera, M.j. Del Jesus, P. Villar
    Abstract:

    We propose a new method to automatically learn the knowledge base of a fuzzy rule-based classification system (FRBCS) by selecting an adequate set of features and by finding an appropiate granularity for them. This process uses a Multiobjective Genetic Algorithm and considers a simple generation method to derive the fuzzy classification rules.

Mietek A. Brdys - One of the best experts on this subject based on the ideXlab platform.

  • Grid Implementation of a Parallel Multiobjective Genetic Algorithm for Optimized Allocation of Chlorination Stations in Drinking Water Distribution Systems: Chojnice Case Study
    IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews), 2008
    Co-Authors: Grzegorz Ewald, Wojciech Kurek, Mietek A. Brdys
    Abstract:

    Solving Multiobjective optimization problems requires suitable Algorithms to find a satisfactory approximation of a globally optimal Pareto front. Furthermore, it is a computationally demanding task. In this paper, the grid implementation of a distributed Multiobjective Genetic Algorithm is presented. The distributed version of the Algorithm is based on the island Algorithm with forgetting island elitism used instead of a Genetic data exchange. The Algorithm is applied to the allocation of booster stations in a drinking water distribution system. First, a Multiobjective formulation of the allocation problem is further enhanced in order to handle multiple water demand scenarios and to integrate controller design into the allocation problem formulation. Next, the new grid-based Algorithm is applied to a case study system. The results are compared with a nondistributed version of the Algorithm.

Chiu-hung Chen - One of the best experts on this subject based on the ideXlab platform.

  • Optimization of short-haul aircraft schedule recovery problems using a hybrid Multiobjective Genetic Algorithm
    Expert Systems With Applications, 2010
    Co-Authors: Chiu-hung Chen, Jyh-horng Chou
    Abstract:

    A hybrid Multiobjective Genetic Algorithm is presented in this paper to find an efficient solution for the daily short-haul aircraft schedule recovery problems which usually happen due to some disturbance events and require a time-sensitive solution to meet various hard constraints and soft objectives. The proposed Algorithm employs an adaptive evaluated vector (AEV) to guide the solution search and uses the method of inequality-based Multiobjective Genetic Algorithm to provide the Multiobjective solution. A simulated disturbance experiment, temporal airport closure, is made and shown that the hybrid method can provide a very efficient short-haul schedule recovery solution under various performance indices.

  • Dynamic size-based Multiobjective Genetic Algorithm to solve the crew pairing problem
    Proceedings of SICE Annual Conference 2010, 2010
    Co-Authors: Ta-yuan Chou, Chiu-hung Chen, Fu-sheng Chang
    Abstract:

    This paper presents a dynamic size-based Multiobjective Genetic Algorithm (DSMGA) to solve the crew pairing problem in airline companies. The proposed DSMGA has several features, such as 1) A permutation-based model is proposed rather than the 0-1 set partition model. 2) Instead of pre-assigning a fixed group number of crewmembers, the proposed method can determine it by performing the evolutionary process. 3) The crossover and mutation operators are enhanced so that the flying time and the flight duty period can be integrated and considered during the evolutionary process. Experiments show that the proposed DSMGA can find out optimal solution with exact group number of crewmembers instead of pre-assigning it so that the effective and efficient crew pairing can be yielded.

  • method of inequalities based Multiobjective Genetic Algorithm for optimizing a cart double pendulum system
    International Journal of Automation and Computing, 2009
    Co-Authors: Chiu-hung Chen, Zushu Li, Jyh-horng Chou
    Abstract:

    This article presents a Multiobjective approach to the design of the controller for the swing-up and handstand control of a general cart-double-pendulum system (CDPS). The designed controller, which is based on the human-simulated intelligent control (HSIC) method, builds up different control modes to monitor and control the CDPS during four kinetic phases consisting of an initial oscillation phase, a swing-up phase, a posture adjustment phase, and a balance control phase. For the approach, the original method of inequalities-based (MoI) Multiobjective Genetic Algorithm (MMGA) is extended and applied to the case study which uses a set of performance indices that includes the cart displacement over the rail boundary, the number of swings, the settling time, the overshoot of the total energy, and the control effort. The simulation results show good responses of the CDPS with the controllers obtained by the proposed approach.

  • SMC - A hybrid Multiobjective Genetic Algorithm on optimizing aircraft schedule recovery problems under short-time response
    2008 IEEE International Conference on Systems Man and Cybernetics, 2008
    Co-Authors: Chiu-hung Chen, Jyh-horng Chou, Jinn-tsong Tsai, Wen-hsien Ho
    Abstract:

    This article presents a hybrid Multiobjective Genetic Algorithm to aid the tracking of the daily aircraft schedule recovery problem under disturbance events such as severe weather and mechanical problems. The proposed Algorithm extends from the original method of inequality-based Multiobjective Genetic Algorithm (MMGA) and utilizes an adaptive evaluated vector (AEV) to co-work with MMGA efficiently when maintaining the Pareto set of recovered schedules in the evolutionary population. Two main goals would be presented: One is to provide a multi-objective solution to the recovery problem and the other is to address the performance requirement on the recovery approach. A simulated disturbance experiment on the practical aircraft schedule is made to validate the recovery results under the expected short-time period.

  • A hybrid Multiobjective Genetic Algorithm on optimizing aircraft schedule recovery problems under short-time response
    2008 IEEE International Conference on Systems Man and Cybernetics, 2008
    Co-Authors: Chiu-hung Chen, Jyh-horng Chou, Jinn-tsong Tsai, Wen-hsien Ho
    Abstract:

    This article presents a hybrid Multiobjective Genetic Algorithm to aid the tracking of the daily aircraft schedule recovery problem under disturbance events such as severe weather and mechanical problems. The proposed Algorithm extends from the original method of inequality-based Multiobjective Genetic Algorithm (MMGA) and utilizes an adaptive evaluated vector (AEV) to co-work with MMGA efficiently when maintaining the Pareto set of recovered schedules in the evolutionary population. Two main goals would be presented: One is to provide a multi-objective solution to the recovery problem and the other is to address the performance requirement on the recovery approach. A simulated disturbance experiment on the practical aircraft schedule is made to validate the recovery results under the expected short-time period.

Jyh-horng Chou - One of the best experts on this subject based on the ideXlab platform.

  • Optimization of short-haul aircraft schedule recovery problems using a hybrid Multiobjective Genetic Algorithm
    Expert Systems With Applications, 2010
    Co-Authors: Chiu-hung Chen, Jyh-horng Chou
    Abstract:

    A hybrid Multiobjective Genetic Algorithm is presented in this paper to find an efficient solution for the daily short-haul aircraft schedule recovery problems which usually happen due to some disturbance events and require a time-sensitive solution to meet various hard constraints and soft objectives. The proposed Algorithm employs an adaptive evaluated vector (AEV) to guide the solution search and uses the method of inequality-based Multiobjective Genetic Algorithm to provide the Multiobjective solution. A simulated disturbance experiment, temporal airport closure, is made and shown that the hybrid method can provide a very efficient short-haul schedule recovery solution under various performance indices.

  • method of inequalities based Multiobjective Genetic Algorithm for optimizing a cart double pendulum system
    International Journal of Automation and Computing, 2009
    Co-Authors: Chiu-hung Chen, Zushu Li, Jyh-horng Chou
    Abstract:

    This article presents a Multiobjective approach to the design of the controller for the swing-up and handstand control of a general cart-double-pendulum system (CDPS). The designed controller, which is based on the human-simulated intelligent control (HSIC) method, builds up different control modes to monitor and control the CDPS during four kinetic phases consisting of an initial oscillation phase, a swing-up phase, a posture adjustment phase, and a balance control phase. For the approach, the original method of inequalities-based (MoI) Multiobjective Genetic Algorithm (MMGA) is extended and applied to the case study which uses a set of performance indices that includes the cart displacement over the rail boundary, the number of swings, the settling time, the overshoot of the total energy, and the control effort. The simulation results show good responses of the CDPS with the controllers obtained by the proposed approach.

  • SMC - A hybrid Multiobjective Genetic Algorithm on optimizing aircraft schedule recovery problems under short-time response
    2008 IEEE International Conference on Systems Man and Cybernetics, 2008
    Co-Authors: Chiu-hung Chen, Jyh-horng Chou, Jinn-tsong Tsai, Wen-hsien Ho
    Abstract:

    This article presents a hybrid Multiobjective Genetic Algorithm to aid the tracking of the daily aircraft schedule recovery problem under disturbance events such as severe weather and mechanical problems. The proposed Algorithm extends from the original method of inequality-based Multiobjective Genetic Algorithm (MMGA) and utilizes an adaptive evaluated vector (AEV) to co-work with MMGA efficiently when maintaining the Pareto set of recovered schedules in the evolutionary population. Two main goals would be presented: One is to provide a multi-objective solution to the recovery problem and the other is to address the performance requirement on the recovery approach. A simulated disturbance experiment on the practical aircraft schedule is made to validate the recovery results under the expected short-time period.

  • A hybrid Multiobjective Genetic Algorithm on optimizing aircraft schedule recovery problems under short-time response
    2008 IEEE International Conference on Systems Man and Cybernetics, 2008
    Co-Authors: Chiu-hung Chen, Jyh-horng Chou, Jinn-tsong Tsai, Wen-hsien Ho
    Abstract:

    This article presents a hybrid Multiobjective Genetic Algorithm to aid the tracking of the daily aircraft schedule recovery problem under disturbance events such as severe weather and mechanical problems. The proposed Algorithm extends from the original method of inequality-based Multiobjective Genetic Algorithm (MMGA) and utilizes an adaptive evaluated vector (AEV) to co-work with MMGA efficiently when maintaining the Pareto set of recovered schedules in the evolutionary population. Two main goals would be presented: One is to provide a multi-objective solution to the recovery problem and the other is to address the performance requirement on the recovery approach. A simulated disturbance experiment on the practical aircraft schedule is made to validate the recovery results under the expected short-time period.