The Experts below are selected from a list of 2178 Experts worldwide ranked by ideXlab platform

Ronald W Shonkwiler - One of the best experts on this subject based on the ideXlab platform.

  • geometric convergence of genetic algorithms under tempered Random Restart
    Journal of Applied Probability, 2009
    Co-Authors: Franklin Mendivil, Ronald W Shonkwiler, M C Spruill
    Abstract:

    Geometric convergence to 0 of the probability that the goal has not been encountered by the nth generation is established for a class of genetic algorithms. These algorithms employ a quickly decreasing mutation rate and a crossover which Restarts the algorithm in a controlled way depending on the current population and restricts execution of this crossover to occasions when progress of the algorithm is too slow. It is shown that without the crossover studied here, which amounts to a tempered Restart of the algorithm, the asserted geometric convergence need not hold.

  • analysis of Random Restart and iterated improvement for global optimization with application to the traveling salesman problem
    Journal of Optimization Theory and Applications, 2005
    Co-Authors: Franklin Mendivil, Ronald W Shonkwiler, M C Spruill
    Abstract:

    The optimization method employing iterated improvement with Random Restart (I2R2) is studied. Associated with each instance of an I2R2 search is a fundamental polynomial, \(f(x) - o_{0}x + p_{1}x^{2} + \cdots + p_{d}x^{d+1} - 1,\) in which the coefficient p k is the probability of starting a search k improvement steps from a local minimum. The positive root η of f can be used to calculate the convergence and speedup properties of that instance.

  • applications of Random Restart to genetic algorithms
    Information Sciences, 1996
    Co-Authors: Farzad Ghannadian, Cecil O Alford, Ronald W Shonkwiler
    Abstract:

    In this paper, a new genetic algorithm is introduced in which the mutation operation has been replaced with Random Restart. The new genetic algorithm is applied to the problem of scheduling a set of tasks onto a multiprocessor system. This problem is known to be NP-complete. Using the Markov chain method, the expected time for the algorithm to reach an optimal solution is derived analytically and computed for several different problem sizes. These results indicate that the expected time to reach the goal for the mapping problem increases sublogarithmically as the size of the search spaces grows exponentially. It is also shown that the expected time to reach an optimal solution is proportional to the time the algorithm spends among the transient states.

Todd R Golub - One of the best experts on this subject based on the ideXlab platform.

  • consensus clustering a resampling based method for class discovery and visualization of gene expression microarray data
    Machine Learning, 2003
    Co-Authors: Stefano Monti, Pablo Tamayo, Jill P Mesirov, Todd R Golub
    Abstract:

    In this paper we present a new methodology of class discovery and clustering validation tailored to the task of analyzing gene expression data. The method can best be thought of as an analysis approach, to guide and assist in the use of any of a wide range of available clustering algorithms. We call the new methodology consensus clustering, and in conjunction with resampling techniques, it provides for a method to represent the consensus across multiple runs of a clustering algorithm and to assess the stability of the discovered clusters. The method can also be used to represent the consensus over multiple runs of a clustering algorithm with Random Restart (such as K-means, model-based Bayesian clustering, SOM, etc.), so as to account for its sensitivity to the initial conditions. Finally, it provides for a visualization tool to inspect cluster number, membership, and boundaries. We present the results of our experiments on both simulated data and real gene expression data aimed at evaluating the effectiveness of the methodology in discovering biologically meaningful clusters.

  • a resampling based method for class discovery and visualization of gene expression microarray data
    2003
    Co-Authors: Stefano Monti, Pablo Tamayo, Jill P Mesirov, Todd R Golub
    Abstract:

    In this paper we present a new methodology of class discovery and clustering validation tailored to the task of analyzing gene expression data. The method can best be thought of as an analysis approach, to guide and assist in the use of any of a wide range of available clustering algorithms. We call the new methodology consensus clustering, and in conjunction with resampling techniques, it provides for a method to represent the consensus across multiple runs of a clustering algorithm and to assess the stability of the discovered clusters. The method can also be used to represent the consensus over multiple runs of a clustering algorithm with Random Restart (such as K-means, model-based Bayesian clustering, SOM, etc.), so as to account for its sensitivity to the initial conditions. Finally, it provides for a vi- sualization tool to inspect cluster number, membership, and boundaries. We present the results of our experiments on both simulated data and real gene expression data aimed at evaluating the eectiveness of the methodology in discovering biologically meaningful clusters.

M C Spruill - One of the best experts on this subject based on the ideXlab platform.

  • geometric convergence of genetic algorithms under tempered Random Restart
    Journal of Applied Probability, 2009
    Co-Authors: Franklin Mendivil, Ronald W Shonkwiler, M C Spruill
    Abstract:

    Geometric convergence to 0 of the probability that the goal has not been encountered by the nth generation is established for a class of genetic algorithms. These algorithms employ a quickly decreasing mutation rate and a crossover which Restarts the algorithm in a controlled way depending on the current population and restricts execution of this crossover to occasions when progress of the algorithm is too slow. It is shown that without the crossover studied here, which amounts to a tempered Restart of the algorithm, the asserted geometric convergence need not hold.

  • analysis of Random Restart and iterated improvement for global optimization with application to the traveling salesman problem
    Journal of Optimization Theory and Applications, 2005
    Co-Authors: Franklin Mendivil, Ronald W Shonkwiler, M C Spruill
    Abstract:

    The optimization method employing iterated improvement with Random Restart (I2R2) is studied. Associated with each instance of an I2R2 search is a fundamental polynomial, \(f(x) - o_{0}x + p_{1}x^{2} + \cdots + p_{d}x^{d+1} - 1,\) in which the coefficient p k is the probability of starting a search k improvement steps from a local minimum. The positive root η of f can be used to calculate the convergence and speedup properties of that instance.

Miguel J Puerta - One of the best experts on this subject based on the ideXlab platform.

  • an iterated local search algorithm for learning bayesian networks with Restarts based on conditional independence tests
    International Journal of Intelligent Systems, 2003
    Co-Authors: Luis M De Campos, Juan M Fernandezluna, Miguel J Puerta
    Abstract:

    A common approach for learning Bayesian networks (BNs) from data is based on the use of a scoring metric to evaluate the fitness of any given candidate network to the data and a method to explore the search space, which usually is the set of directed acyclic graphs (DAGs). The most efficient search methods used in this context are greedy hill climbing, either deterministic or stochastic. One of these methods that has been applied with some success is hill climbing with Random Restart. In this article we study a new algorithm of this type to Restart a local search when it is trapped at a local optimum. It uses problem-specific knowledge about BNs and the information provided by the database itself (by testing the conditional independencies, which are true in the current solution of the search process). We also study a new definition of neighborhood for the space of DAGs by using the classical operators of arc addition and arc deletion together with a new operator for arc reversal. The proposed methods are empirically tested using two different domains: ALARM and INSURANCE. © 2003 Wiley Periodicals, Inc.

Roberto Montemanni - One of the best experts on this subject based on the ideXlab platform.

  • a Random Restart local search matheuristic for the flying sidekick traveling salesman problem
    2021 The 8th International Conference on Industrial Engineering and Applications(Europe), 2021
    Co-Authors: Mauro Dellamico, Roberto Montemanni, Stefano Novellani
    Abstract:

    Drones and unmanned vehicles in general are gaining more and more interest in the logistic sector, due to the potential economic advantages they can provide. In this paper we focus on optimizing the use of a drone in conjunction with a truck for urban deliveries, dealing with what is called the flying sidekick traveling salesman problem. There is a set of customers that it is possible to serve either by a truck or by a drone. The target is to minimize the total time required to complete deliveries to all the customers. In this paper we show how an effective and simple Random Restart local search heuristic algorithm can be derived from a known mixed integer programming model for the problem.

  • re initialising solutions in a Random Restart local search for the probabilistic orienteering problem
    2021 The 8th International Conference on Industrial Engineering and Applications(Europe), 2021
    Co-Authors: Xiaochen Chou, Umberto Junior Mele, Luca Maria Gambardella, Roberto Montemanni
    Abstract:

    The Probabilistic Orienteering Problem is an optimization problem where a set of customers, each with an associated prize and probability of requiring a service, a time budget and travel times between customers are given. The objective is to select the subset of customers that maximize the expected total prize collected in the given time (taking into account of the total travel time spent visiting them). Random Restart Local Search is a heuristic method widely used to solve combinatorial optimization problems. In particular, it is used in conjunction with local search procedures to escape from local optima. The method works by Restarting the optimization search once no further improvement is possible by the embedded local search component. Each Restart is associated with a new initial solution for the optimization and selecting such Restart initial solutions play an important role in the success of the overall algorithm. In this work we propose a method to effectively selecting such solutions, and we present an empirical study to validate our ideas.

  • heuristics for the probabilistic traveling salesman problem with deadlines based on quasi parallel monte carlo sampling
    Computers & Operations Research, 2013
    Co-Authors: Dennis Weyland, Roberto Montemanni, Luca Maria Gambardella
    Abstract:

    The Probabilistic Traveling Salesman Problem with Deadlines (PTSPD) is a Stochastic Vehicle Routing Problem with a computationally demanding objective function. In this work we propose an approximation for that objective function based on Monte Carlo Sampling and using the novel approach of quasi-parallel evaluation of samples. We perform comprehensive computational studies that reveal the efficiency of this approximation. Additionally, we examine different Local Search Algorithms and present a Random Restart Local Search Algorithm for solving the PTSPD together with an extensive computational study on a large set of benchmark instances.