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Aurora Pozo - One of the best experts on this subject based on the ideXlab platform.

  • product selection based on upper Confidence Bound moea d dra for testing software product lines
    Congress on Evolutionary Computation, 2016
    Co-Authors: Thiago Do Nascimento Ferreira, Aurora Pozo, Josiel Neumann Kuk, Silvia Regina Vergilio
    Abstract:

    The selection of products for testing Software Product Lines (SPLs) is an optimization problem. The goal is to select a possible minimum set of products that satisfies testing criteria, such as, pairwise and mutation testing. Multi-objective Evolutionary Algorithms (MOEAs) have been successfully used to solve this problem and other ones related to software development. However, the use of MOEAs demands setting a number of control parameters and selection of genetic operators, to which the algorithm performance is often very sensitive. Adaptive Operator Selection (AOS) methods, such as Upper Confidence Bound (UCB) based ones can help in this task. UCB methods used with Multi-objective Evolutionary Algorithm Based on Decomposition with Dynamical Resource Allocation (MOEA/D-DRA) have presented promising results, but they are underexplored in the Search Based Software Engineering (SBSE) field. To contribute to this research area and to solve efficiently the product selection problem, this paper investigates the use of different AOS UCB-based methods with MOEA/D-DRA. The idea is to reduce effort spent by the tester. Some parameters and evolutionary operators can be automatically set. The approach is empirical evaluated using four instances and three UCB methods. The UCB methods present similar results and outperform the canonical version of MOEA/D-DRA.

  • CEC - Product selection based on upper Confidence Bound MOEA/D-DRA for testing software product lines
    2016 IEEE Congress on Evolutionary Computation (CEC), 2016
    Co-Authors: Thiago Do Nascimento Ferreira, Aurora Pozo, Josiel Neumann Kuk, Silvia Regina Vergilio
    Abstract:

    The selection of products for testing Software Product Lines (SPLs) is an optimization problem. The goal is to select a possible minimum set of products that satisfies testing criteria, such as, pairwise and mutation testing. Multi-objective Evolutionary Algorithms (MOEAs) have been successfully used to solve this problem and other ones related to software development. However, the use of MOEAs demands setting a number of control parameters and selection of genetic operators, to which the algorithm performance is often very sensitive. Adaptive Operator Selection (AOS) methods, such as Upper Confidence Bound (UCB) based ones can help in this task. UCB methods used with Multi-objective Evolutionary Algorithm Based on Decomposition with Dynamical Resource Allocation (MOEA/D-DRA) have presented promising results, but they are underexplored in the Search Based Software Engineering (SBSE) field. To contribute to this research area and to solve efficiently the product selection problem, this paper investigates the use of different AOS UCB-based methods with MOEA/D-DRA. The idea is to reduce effort spent by the tester. Some parameters and evolutionary operators can be automatically set. The approach is empirical evaluated using four instances and three UCB methods. The UCB methods present similar results and outperform the canonical version of MOEA/D-DRA.

  • upper Confidence Bound ucb algorithms for adaptive operator selection in moea d
    International Conference on Evolutionary Multi-criterion Optimization, 2015
    Co-Authors: Richard A Goncalves, Carolina P De Almeida, Aurora Pozo
    Abstract:

    Adaptive Operator Selection (AOS) is a method used to dynamically determine which operator should be applied in an optimization algorithm based on its performance history. Recently, Upper Confidence Bound (UCB) algorithms have been successfully applied for this task. UCB algorithms have special features to tackle the Exploration versus Exploitation (EvE) dilemma presented on the AOS problem. However, it is important to note that the use of UCB algorithms for AOS is still incipient on Multiobjective Evolutionary Algorithms (MOEAs) and many contributions can be made. The aim of this paper is to extend the study of UCB based AOS methods. Two methods are proposed: MOEA/D-UCB-Tuned and MOEA/D-UCB-V, both use the variance of the operators’ rewards in order to obtain a better EvE tradeoff. In these proposals the UCB-Tuned and UCB-V algorithms from the multiarmed bandit (MAB) literature are combined with MOEA/D (MOEA based on decomposition), one of the most successful MOEAs. Experimental results demonstrate that MOEA/D-UCB-Tuned can be favorably compared with state-of-the-art adaptive operator selection MOEA/D variants based on probability (ENS-MOEA/D and ADEMO/D) and multi-armed bandits (MOEA/D-FRRMAB) methods.

  • EMO (1) - Upper Confidence Bound (UCB) Algorithms for Adaptive Operator Selection in MOEA/D
    Lecture Notes in Computer Science, 2015
    Co-Authors: Richard A Goncalves, Carolina P De Almeida, Aurora Pozo
    Abstract:

    Adaptive Operator Selection (AOS) is a method used to dynamically determine which operator should be applied in an optimization algorithm based on its performance history. Recently, Upper Confidence Bound (UCB) algorithms have been successfully applied for this task. UCB algorithms have special features to tackle the Exploration versus Exploitation (EvE) dilemma presented on the AOS problem. However, it is important to note that the use of UCB algorithms for AOS is still incipient on Multiobjective Evolutionary Algorithms (MOEAs) and many contributions can be made. The aim of this paper is to extend the study of UCB based AOS methods. Two methods are proposed: MOEA/D-UCB-Tuned and MOEA/D-UCB-V, both use the variance of the operators’ rewards in order to obtain a better EvE tradeoff. In these proposals the UCB-Tuned and UCB-V algorithms from the multiarmed bandit (MAB) literature are combined with MOEA/D (MOEA based on decomposition), one of the most successful MOEAs. Experimental results demonstrate that MOEA/D-UCB-Tuned can be favorably compared with state-of-the-art adaptive operator selection MOEA/D variants based on probability (ENS-MOEA/D and ADEMO/D) and multi-armed bandits (MOEA/D-FRRMAB) methods.

Silvia Regina Vergilio - One of the best experts on this subject based on the ideXlab platform.

  • product selection based on upper Confidence Bound moea d dra for testing software product lines
    Congress on Evolutionary Computation, 2016
    Co-Authors: Thiago Do Nascimento Ferreira, Aurora Pozo, Josiel Neumann Kuk, Silvia Regina Vergilio
    Abstract:

    The selection of products for testing Software Product Lines (SPLs) is an optimization problem. The goal is to select a possible minimum set of products that satisfies testing criteria, such as, pairwise and mutation testing. Multi-objective Evolutionary Algorithms (MOEAs) have been successfully used to solve this problem and other ones related to software development. However, the use of MOEAs demands setting a number of control parameters and selection of genetic operators, to which the algorithm performance is often very sensitive. Adaptive Operator Selection (AOS) methods, such as Upper Confidence Bound (UCB) based ones can help in this task. UCB methods used with Multi-objective Evolutionary Algorithm Based on Decomposition with Dynamical Resource Allocation (MOEA/D-DRA) have presented promising results, but they are underexplored in the Search Based Software Engineering (SBSE) field. To contribute to this research area and to solve efficiently the product selection problem, this paper investigates the use of different AOS UCB-based methods with MOEA/D-DRA. The idea is to reduce effort spent by the tester. Some parameters and evolutionary operators can be automatically set. The approach is empirical evaluated using four instances and three UCB methods. The UCB methods present similar results and outperform the canonical version of MOEA/D-DRA.

  • CEC - Product selection based on upper Confidence Bound MOEA/D-DRA for testing software product lines
    2016 IEEE Congress on Evolutionary Computation (CEC), 2016
    Co-Authors: Thiago Do Nascimento Ferreira, Aurora Pozo, Josiel Neumann Kuk, Silvia Regina Vergilio
    Abstract:

    The selection of products for testing Software Product Lines (SPLs) is an optimization problem. The goal is to select a possible minimum set of products that satisfies testing criteria, such as, pairwise and mutation testing. Multi-objective Evolutionary Algorithms (MOEAs) have been successfully used to solve this problem and other ones related to software development. However, the use of MOEAs demands setting a number of control parameters and selection of genetic operators, to which the algorithm performance is often very sensitive. Adaptive Operator Selection (AOS) methods, such as Upper Confidence Bound (UCB) based ones can help in this task. UCB methods used with Multi-objective Evolutionary Algorithm Based on Decomposition with Dynamical Resource Allocation (MOEA/D-DRA) have presented promising results, but they are underexplored in the Search Based Software Engineering (SBSE) field. To contribute to this research area and to solve efficiently the product selection problem, this paper investigates the use of different AOS UCB-based methods with MOEA/D-DRA. The idea is to reduce effort spent by the tester. Some parameters and evolutionary operators can be automatically set. The approach is empirical evaluated using four instances and three UCB methods. The UCB methods present similar results and outperform the canonical version of MOEA/D-DRA.

Richard A Goncalves - One of the best experts on this subject based on the ideXlab platform.

  • a hyper heuristic in moea d dra using the upper Confidence Bound technique
    Brazilian Conference on Intelligent Systems, 2017
    Co-Authors: Lucas Prestes, Myriam Regattieri Delgado, Richard A Goncalves, Carolina P De Almeida, Aurora Trinidad Pozo
    Abstract:

    The Multi-Objective Evolutionary Algorithm based on Decomposition with Dynamical Resource Allocation (MOEA/D-DRA) has obtained very good results on various multi-objective optimization problems in the past few years. This paper focuses on an attempt to improve even more its performance by introducing a hyper-heuristic mechanism to select the best set of its operators and parameters. In this paper we use Upper Confidence Bound (UCB) as the basis of the hyper-heuristic, and test three versions of the proposed approach. Four well known benchmarks (CEC 2009, WFG, DTLZ and ZDT) and a quality indicator (hypervolume) are used to analyze the performance of the three variants. The proposed approach is compared with the original MOEA/D-DRA and the results show that tuning the parameters via UCB is an interesting alternative for a hyper-heuristic based version of MOEA/D-DRA on the addressed problems.

  • BRACIS - A Hyper-Heuristic in MOEA/D-DRA Using the Upper Confidence Bound Technique
    2017 Brazilian Conference on Intelligent Systems (BRACIS), 2017
    Co-Authors: Lucas Prestes, Myriam Regattieri Delgado, Richard A Goncalves, Carolina P De Almeida, Aurora Trinidad Pozo
    Abstract:

    The Multi-Objective Evolutionary Algorithm based on Decomposition with Dynamical Resource Allocation (MOEA/D-DRA) has obtained very good results on various multi-objective optimization problems in the past few years. This paper focuses on an attempt to improve even more its performance by introducing a hyper-heuristic mechanism to select the best set of its operators and parameters. In this paper we use Upper Confidence Bound (UCB) as the basis of the hyper-heuristic, and test three versions of the proposed approach. Four well known benchmarks (CEC 2009, WFG, DTLZ and ZDT) and a quality indicator (hypervolume) are used to analyze the performance of the three variants. The proposed approach is compared with the original MOEA/D-DRA and the results show that tuning the parameters via UCB is an interesting alternative for a hyper-heuristic based version of MOEA/D-DRA on the addressed problems.

  • upper Confidence Bound ucb algorithms for adaptive operator selection in moea d
    International Conference on Evolutionary Multi-criterion Optimization, 2015
    Co-Authors: Richard A Goncalves, Carolina P De Almeida, Aurora Pozo
    Abstract:

    Adaptive Operator Selection (AOS) is a method used to dynamically determine which operator should be applied in an optimization algorithm based on its performance history. Recently, Upper Confidence Bound (UCB) algorithms have been successfully applied for this task. UCB algorithms have special features to tackle the Exploration versus Exploitation (EvE) dilemma presented on the AOS problem. However, it is important to note that the use of UCB algorithms for AOS is still incipient on Multiobjective Evolutionary Algorithms (MOEAs) and many contributions can be made. The aim of this paper is to extend the study of UCB based AOS methods. Two methods are proposed: MOEA/D-UCB-Tuned and MOEA/D-UCB-V, both use the variance of the operators’ rewards in order to obtain a better EvE tradeoff. In these proposals the UCB-Tuned and UCB-V algorithms from the multiarmed bandit (MAB) literature are combined with MOEA/D (MOEA based on decomposition), one of the most successful MOEAs. Experimental results demonstrate that MOEA/D-UCB-Tuned can be favorably compared with state-of-the-art adaptive operator selection MOEA/D variants based on probability (ENS-MOEA/D and ADEMO/D) and multi-armed bandits (MOEA/D-FRRMAB) methods.

  • EMO (1) - Upper Confidence Bound (UCB) Algorithms for Adaptive Operator Selection in MOEA/D
    Lecture Notes in Computer Science, 2015
    Co-Authors: Richard A Goncalves, Carolina P De Almeida, Aurora Pozo
    Abstract:

    Adaptive Operator Selection (AOS) is a method used to dynamically determine which operator should be applied in an optimization algorithm based on its performance history. Recently, Upper Confidence Bound (UCB) algorithms have been successfully applied for this task. UCB algorithms have special features to tackle the Exploration versus Exploitation (EvE) dilemma presented on the AOS problem. However, it is important to note that the use of UCB algorithms for AOS is still incipient on Multiobjective Evolutionary Algorithms (MOEAs) and many contributions can be made. The aim of this paper is to extend the study of UCB based AOS methods. Two methods are proposed: MOEA/D-UCB-Tuned and MOEA/D-UCB-V, both use the variance of the operators’ rewards in order to obtain a better EvE tradeoff. In these proposals the UCB-Tuned and UCB-V algorithms from the multiarmed bandit (MAB) literature are combined with MOEA/D (MOEA based on decomposition), one of the most successful MOEAs. Experimental results demonstrate that MOEA/D-UCB-Tuned can be favorably compared with state-of-the-art adaptive operator selection MOEA/D variants based on probability (ENS-MOEA/D and ADEMO/D) and multi-armed bandits (MOEA/D-FRRMAB) methods.

Thiago Do Nascimento Ferreira - One of the best experts on this subject based on the ideXlab platform.

  • product selection based on upper Confidence Bound moea d dra for testing software product lines
    Congress on Evolutionary Computation, 2016
    Co-Authors: Thiago Do Nascimento Ferreira, Aurora Pozo, Josiel Neumann Kuk, Silvia Regina Vergilio
    Abstract:

    The selection of products for testing Software Product Lines (SPLs) is an optimization problem. The goal is to select a possible minimum set of products that satisfies testing criteria, such as, pairwise and mutation testing. Multi-objective Evolutionary Algorithms (MOEAs) have been successfully used to solve this problem and other ones related to software development. However, the use of MOEAs demands setting a number of control parameters and selection of genetic operators, to which the algorithm performance is often very sensitive. Adaptive Operator Selection (AOS) methods, such as Upper Confidence Bound (UCB) based ones can help in this task. UCB methods used with Multi-objective Evolutionary Algorithm Based on Decomposition with Dynamical Resource Allocation (MOEA/D-DRA) have presented promising results, but they are underexplored in the Search Based Software Engineering (SBSE) field. To contribute to this research area and to solve efficiently the product selection problem, this paper investigates the use of different AOS UCB-based methods with MOEA/D-DRA. The idea is to reduce effort spent by the tester. Some parameters and evolutionary operators can be automatically set. The approach is empirical evaluated using four instances and three UCB methods. The UCB methods present similar results and outperform the canonical version of MOEA/D-DRA.

  • CEC - Product selection based on upper Confidence Bound MOEA/D-DRA for testing software product lines
    2016 IEEE Congress on Evolutionary Computation (CEC), 2016
    Co-Authors: Thiago Do Nascimento Ferreira, Aurora Pozo, Josiel Neumann Kuk, Silvia Regina Vergilio
    Abstract:

    The selection of products for testing Software Product Lines (SPLs) is an optimization problem. The goal is to select a possible minimum set of products that satisfies testing criteria, such as, pairwise and mutation testing. Multi-objective Evolutionary Algorithms (MOEAs) have been successfully used to solve this problem and other ones related to software development. However, the use of MOEAs demands setting a number of control parameters and selection of genetic operators, to which the algorithm performance is often very sensitive. Adaptive Operator Selection (AOS) methods, such as Upper Confidence Bound (UCB) based ones can help in this task. UCB methods used with Multi-objective Evolutionary Algorithm Based on Decomposition with Dynamical Resource Allocation (MOEA/D-DRA) have presented promising results, but they are underexplored in the Search Based Software Engineering (SBSE) field. To contribute to this research area and to solve efficiently the product selection problem, this paper investigates the use of different AOS UCB-based methods with MOEA/D-DRA. The idea is to reduce effort spent by the tester. Some parameters and evolutionary operators can be automatically set. The approach is empirical evaluated using four instances and three UCB methods. The UCB methods present similar results and outperform the canonical version of MOEA/D-DRA.

Eric Moulines - One of the best experts on this subject based on the ideXlab platform.

  • on upper Confidence Bound policies for switching bandit problems
    Algorithmic Learning Theory, 2011
    Co-Authors: Aurélien Garivier, Eric Moulines
    Abstract:

    Many problems, such as cognitive radio, parameter control of a scanning tunnelling microscope or internet advertisement, can be modelled as non-stationary bandit problems where the distributions of rewards changes abruptly at unknown time instants. In this paper, we analyze two algorithms designed for solving this issue: discounted UCB (D-UCB) and sliding-window UCB (SW-UCB). We establish an upperBound for the expected regret by upper-Bounding the expectation of the number of times suboptimal arms are played. The proof relies on an interesting Hoeffding type inequality for self normalized deviations with a random number of summands. We establish a lower-Bound for the regret in presence of abrupt changes in the arms reward distributions. We show that the discounted UCB and the sliding-window UCB both match the lower-Bound up to a logarithmic factor. Numerical simulations show that D-UCB and SW-UCB perform significantly better than existing soft-max methods like EXP3.S.

  • ALT - On upper-Confidence Bound policies for switching bandit problems
    Lecture Notes in Computer Science, 2011
    Co-Authors: Aurélien Garivier, Eric Moulines
    Abstract:

    Many problems, such as cognitive radio, parameter control of a scanning tunnelling microscope or internet advertisement, can be modelled as non-stationary bandit problems where the distributions of rewards changes abruptly at unknown time instants. In this paper, we analyze two algorithms designed for solving this issue: discounted UCB (D-UCB) and sliding-window UCB (SW-UCB). We establish an upperBound for the expected regret by upper-Bounding the expectation of the number of times suboptimal arms are played. The proof relies on an interesting Hoeffding type inequality for self normalized deviations with a random number of summands. We establish a lower-Bound for the regret in presence of abrupt changes in the arms reward distributions. We show that the discounted UCB and the sliding-window UCB both match the lower-Bound up to a logarithmic factor. Numerical simulations show that D-UCB and SW-UCB perform significantly better than existing soft-max methods like EXP3.S.

  • On Upper-Confidence Bound Policies for Non-Stationary Bandit Problems
    arXiv: Statistics Theory, 2008
    Co-Authors: Aurélien Garivier, Eric Moulines
    Abstract:

    Multi-armed bandit problems are considered as a paradigm of the trade-off between exploring the environment to find profitable actions and exploiting what is already known. In the stationary case, the distributions of the rewards do not change in time, Upper-Confidence Bound (UCB) policies have been shown to be rate optimal. A challenging variant of the MABP is the non-stationary bandit problem where the gambler must decide which arm to play while facing the possibility of a changing environment. In this paper, we consider the situation where the distributions of rewards remain constant over epochs and change at unknown time instants. We analyze two algorithms: the discounted UCB and the sliding-window UCB. We establish for these two algorithms an upper-Bound for the expected regret by upper-Bounding the expectation of the number of times a suboptimal arm is played. For that purpose, we derive a Hoeffding type inequality for self normalized deviations with a random number of summands. We establish a lower-Bound for the regret in presence of abrupt changes in the arms reward distributions. We show that the discounted UCB and the sliding-window UCB both match the lower-Bound up to a logarithmic factor.