The Experts below are selected from a list of 258 Experts worldwide ranked by ideXlab platform

Shigeyoshi Tsutsui - One of the best experts on this subject based on the ideXlab platform.

  • parallelizing a Genetic Operator for gpus
    Congress on Evolutionary Computation, 2013
    Co-Authors: Noriyuki Fujimoto, Shigeyoshi Tsutsui
    Abstract:

    Genetic algorithms (GAs) have parallelism among applications of Genetic Operators to individuals, but in order to extract high performance of a GPU, parallelizing each Genetic Operator is desirable. This paper presents parallelization of the OX (order crossover) Operator and experimentally show that our parallelized OX is effective on a GPU based on the CUDA architecture. The experiments with an NVIDIA GeForce GTX580 GPU show that our GPU program for the traveling salesman problem (TSP) is about up to 101.3 times faster than the corresponding CPU program on a single core of 2.67 GHz Intel Xeon X5550.

  • IEEE Congress on Evolutionary Computation - Parallelizing a Genetic Operator for GPUs
    2013 IEEE Congress on Evolutionary Computation, 2013
    Co-Authors: Noriyuki Fujimoto, Shigeyoshi Tsutsui
    Abstract:

    Genetic algorithms (GAs) have parallelism among applications of Genetic Operators to individuals, but in order to extract high performance of a GPU, parallelizing each Genetic Operator is desirable. This paper presents parallelization of the OX (order crossover) Operator and experimentally show that our parallelized OX is effective on a GPU based on the CUDA architecture. The experiments with an NVIDIA GeForce GTX580 GPU show that our GPU program for the traveling salesman problem (TSP) is about up to 101.3 times faster than the corresponding CPU program on a single core of 2.67 GHz Intel Xeon X5550.

Peter C. Nelson - One of the best experts on this subject based on the ideXlab platform.

  • controlling Genetic Operator rates in evolutionary algorithms
    2011
    Co-Authors: Peter C. Nelson, Fatemeh Vafaee
    Abstract:

    The notion of parameterized evolutionary algorithms—EAs in which the behavior of the process is subject to change due to the variation of one or more of the parameters—has been introduced to the field of the Evolutionary Computation from its inception, back to 1970s. It has been realized from the beginning that suitable choices of EA parameters greatly affect the behavior of the algorithm on a particular fitness landscape. Furthermore, an appropriate parameter setting for a given problem domain may be inappropriate for another. Among different parameters, the rates of Genetic Operators (i.e., mutation and crossover) are known to have a considerable effect on the performance of evolutionary algorithms. Due to the intrinsically dynamic nature of evolutionary processes, the time and the state of the evolution should be inevitably considered in determining the rate of Genetic Operators. This intuition has led to the emergence of the class of parameter control techniques in which the parameter values are subject to change during an EA run. Motivated by the necessity of controlling Genetic Operator rates in evolutionary algorithms, the research covered in this work is concerned with introducing a series of new ideas to the field of parameter control techniques. In other words, it aims to take advantage of some new methods, frameworks, or models (e.g., Markov chain model of GA and statistical models of DNA evolution) which to our knowledge are barely used in former parameter control schemes.

  • optimizing Genetic Operator rates using a markov chain model of Genetic algorithms
    Genetic and Evolutionary Computation Conference, 2010
    Co-Authors: Fatemeh Vafaee, Gyorgy Turan, Peter C. Nelson
    Abstract:

    This work is concerned with proposing a robust framework for optimizing Operator rates of simple Genetic Algorithms (GAs) during a GA run. The suggested framework is built upon a formerly proposed GA Markov chain model to estimate the optimal values of the Operator rates based on the time and the current state of the evolution. Though the proposed framework has been formalized for optimizing both mutation and crossover rates, in the current paper, we only implemented it as the mutation rate optimizer and kept the crossover rate constant. To demonstrate the efficacy of the proposed algorithm, the method is evaluated using a set of benchmark problems and the outcome is compared with a series of well-known relevant algorithms. The results demonstrate that the newly suggested algorithm significantly outperforms its rivals.

  • SASO - Self-Adaptation of Genetic Operator Probabilities Using Differential Evolution
    2009 Third IEEE International Conference on Self-Adaptive and Self-Organizing Systems, 2009
    Co-Authors: Fatemeh Vafaee, Peter C. Nelson
    Abstract:

    In this work a novel approach is proposed to adaptively adjust Genetic Operator probabilities through the adoption of a robust, real-valued optimization algorithm known as Differential Evolution (DE). We set up a series of experiments on a wide array of symbolic regression problems. The experimental results demonstrate the supremacy of our proposed method over the compared rivals both in the accuracy and reliability of the final solutions.

  • ICMLA - Adaptively Evolving Probabilities of Genetic Operators
    2008 Seventh International Conference on Machine Learning and Applications, 2008
    Co-Authors: Fatemeh Vafaee, Peter C. Nelson, Weimin Xiao, Chi Zhou
    Abstract:

    This work is concerned with proposing an adaptive method to dynamically adjust Genetic Operator probabilities throughout the evolutionary process. The proposed method relies on the individual preferences of each chromosome, rather than the global behavior of the whole population. Hence, each individual carries its own set of parameters, including the probabilities of the Genetic Operators. The carried parameters undergo the same evolutionary process as the carriers--the chromosomes - do. We call this method Evolved Evolutionary Algorithm (E2A) as it has an additional evolutionary process to evolve control parameters. Furthermore, E2A employs a supplementary mutation Operator (DE-mutation) which utilizes the previously overlooked numerical optimization model known as the Differential Evolution to expedite the optimization rate of the Genetic parameters. To leverage our previous work, we used Gene Expression Programming (GEP) as a benchmark to determine the performance of our proposed method. Nevertheless, E2A can be easily extended to other Genetic programming variants. As the experimental results on a wide array of regression problems demonstrate, the E2A method reveals a faster rate of convergence and provides fitter ultimate solutions. However, to further expose the power of the E2A method, we compared it to related methods using self-adaptation previously applied to Genetic Algorithms. Our benchmarking on the same set of regression problems proves the supremacy of our proposed method both in the accuracy and simplicity of the final solutions.

Noriyuki Fujimoto - One of the best experts on this subject based on the ideXlab platform.

  • parallelizing a Genetic Operator for gpus
    Congress on Evolutionary Computation, 2013
    Co-Authors: Noriyuki Fujimoto, Shigeyoshi Tsutsui
    Abstract:

    Genetic algorithms (GAs) have parallelism among applications of Genetic Operators to individuals, but in order to extract high performance of a GPU, parallelizing each Genetic Operator is desirable. This paper presents parallelization of the OX (order crossover) Operator and experimentally show that our parallelized OX is effective on a GPU based on the CUDA architecture. The experiments with an NVIDIA GeForce GTX580 GPU show that our GPU program for the traveling salesman problem (TSP) is about up to 101.3 times faster than the corresponding CPU program on a single core of 2.67 GHz Intel Xeon X5550.

  • IEEE Congress on Evolutionary Computation - Parallelizing a Genetic Operator for GPUs
    2013 IEEE Congress on Evolutionary Computation, 2013
    Co-Authors: Noriyuki Fujimoto, Shigeyoshi Tsutsui
    Abstract:

    Genetic algorithms (GAs) have parallelism among applications of Genetic Operators to individuals, but in order to extract high performance of a GPU, parallelizing each Genetic Operator is desirable. This paper presents parallelization of the OX (order crossover) Operator and experimentally show that our parallelized OX is effective on a GPU based on the CUDA architecture. The experiments with an NVIDIA GeForce GTX580 GPU show that our GPU program for the traveling salesman problem (TSP) is about up to 101.3 times faster than the corresponding CPU program on a single core of 2.67 GHz Intel Xeon X5550.

H. Ren - One of the best experts on this subject based on the ideXlab platform.

  • A Genetic algorithm for multiprocessor scheduling
    IEEE Transactions on Parallel and Distributed Systems, 1994
    Co-Authors: E.s.h. Hou, H. Ren
    Abstract:

    The problem of multiprocessor scheduling can he stated as finding a schedule for a general task graph to be executed on a multiprocessor system so that the schedule length can he minimized. This scheduling problem is known to be NP- hard, and methods based on heuristic search have been proposed to obtain optimal and suboptimal solutions. Genetic algorithms have recently received much attention as a class of robust sto- chastic search algorithms for various optimization problems. In this paper, an efficient method based on Genetic algorithms is developed to solve the multiprocessor scheduling problem. The representation of the search node is based on the order of the tasks being executed in each individual processor. The Genetic Operator proposed is based on the precedence relations between the tasks in the task graph. Simulation results comparing the proposed Genetic algorithm, the list scheduling algorithm, and the optimal schedule using random task graphs, and a robot inverse dynamics computational task graph for various are presented.

Tania Pencheva - One of the best experts on this subject based on the ideXlab platform.

  • LSSC - Genetic Operators Significance Assessment in Simple Genetic Algorithm
    Large-Scale Scientific Computing, 2014
    Co-Authors: Maria Angelova, Tania Pencheva
    Abstract:

    Genetic algorithms, proved as successful alternative to conventional optimization methods for the purposes of parameter identification of fermentation process models, search for a global optimal solution via three main Genetic Operators, namely selection, crossover, and mutation. In order to determine their importance for finding the solution, a procedure for significance assessment of Genetic algorithms Operators has been developed. The workability of newly elaborated procedure has been tested when simple Genetic algorithm is applied to parameter identification of S. cerevisiae fed-batch cultivation. According to obtained results the most significant Genetic Operator has been distinguished and its influence for finding the global optimal solution has been evaluated.

  • Genetic Operators' significance assessment in multi-population Genetic algorithms
    International Journal of Metaheuristics, 2014
    Co-Authors: Maria Angelova, Tania Pencheva
    Abstract:

    Genetic algorithms are widely applied bioinspired optimisation technique that search for a global optimal solution via three main Genetic Operators, namely selection, crossover, and mutation. In order to determine the Operators importance when multi-population Genetic algorithm is applied for parameter identification of S. cerevisiae fed-batch cultivation, recently presented procedure for significance assessment has been implemented. Based on obtained results the most significant Genetic Operator has been distinguished and its influence on finding the global optimal solution has been evaluated as well.

  • generalized net model of selection Operator of Genetic algorithms
    IEEE International Conference on Intelligent Systems, 2010
    Co-Authors: Olympia Roeva, Tania Pencheva, Krassimir T Atanassov, A G Shannon
    Abstract:

    The apparatus of Generalized Nets is applied here to a description of a selection Operator, which is one of the basic Genetic algorithm Operators. This Genetic Operator performs a probabilistic selection based upon the individual's fittness such that the better individuals have an increased chance of being selected for the next generation. The resulting GN model could be considered as a separate module, but can also be accumulated into a GN model to describe a whole Genetic algorithm.