The Experts below are selected from a list of 67743 Experts worldwide ranked by ideXlab platform
Andrzej Jaszkiewicz - One of the best experts on this subject based on the ideXlab platform.
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on the Computational efficiency of multiple objective metaheuristics the knapsack problem case study
European Journal of Operational Research, 2004Co-Authors: Andrzej JaszkiewiczAbstract:Abstract The paper describes a Computational Experiment which goal is to evaluate Computational efficiency of three multiple objective evolutionary metaheuristics on the multiple objective multiple constraints knapsack problem. The relative efficiency of the multiple objective algorithms is evaluated with respect to a single objective evolutionary algorithm (EA). We use a methodology that allows consistent evaluation of the quality of approximately Pareto-optimal solutions generated by both multiple and single objective metaheuristics. Then, we compare Computational efforts needed to generate solutions of approximately the same quality by the two kinds of methods. The results indicate that Computational efficiency of multiple objective EAs deteriorates with the growth of the number of objectives. Furthermore, significant differences in the performance of the three algorithms are observed.
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a comparative study of multiple objective metaheuristics on the bi objective set covering problem and the pareto memetic algorithm
Annals of Operations Research, 2004Co-Authors: Andrzej JaszkiewiczAbstract:The paper describes a comparative study of multiple-objective metaheuristics on the bi-objective set covering problem. Ten representative methods based on genetic algorithms, multiple start local search, hybrid genetic algorithms and simulated annealing are evaluated in the Computational Experiment. Nine of the methods are well known from the literature. The paper introduces also a new hybrid genetic algorithm called Pareto memetic algorithm. The results of the Experiment indicate very good performance of hybrid genetic algorithms, however, no algorithm was able to outperform all other methods on all instances. Furthermore, the results indicate that the performance of multiple-objective metaheuristics may differ radically even if the methods are based on the same single objective algorithm and use exactly the same problem-specific operators. Copyright Kluwer Academic Publishers 2004
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a comparative study of multiple objective metaheuristics on the bi objective set covering problem and the pareto memetic algorithm
Annals of Operations Research, 2004Co-Authors: Andrzej JaszkiewiczAbstract:The paper describes a comparative study of multiple-objective metaheuristics on the bi-objective set covering problem. Ten representative methods based on genetic algorithms, multiple start local search, hybrid genetic algorithms and simulated annealing are evaluated in the Computational Experiment. Nine of the methods are well known from the literature. The paper introduces also a new hybrid genetic algorithm called Pareto memetic algorithm. The results of the Experiment indicate very good performance of hybrid genetic algorithms, however, no algorithm was able to outperform all other methods on all instances. Furthermore, the results indicate that the performance of multiple-objective metaheuristics may differ radically even if the methods are based on the same single objective algorithm and use exactly the same problem-specific operators.
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do multiple objective metaheuristics deliver on their promises a Computational Experiment on the set covering problem
IEEE Transactions on Evolutionary Computation, 2003Co-Authors: Andrzej JaszkiewiczAbstract:In this paper, we compare the Computational efficiency of three state-of-the-art multiobjective metaheuristics (MOMHs) and their single-objective counterparts on the multiple-objective set-covering problem (MOSCP). We use a methodology that allows consistent evaluation of the quality of approximately Pareto-optimal solutions generated by of both MOMHs and single-objective metaheuristics (SOMHs). Specifically, we use the average value of the scalarization functions over a representative sample of weight vectors. Then, we compare Computational efforts needed to generate solutions of approximately the same quality by the two kinds of methods. In the Computational Experiment, we use two SOHMs - the evolutionary algorithm (EA) and memetic algorithm (MA), and three MOMH-controlled elitist nondominated sorting genetic algorithm, the strength Pareto EA, and the Pareto MA. The methods are compared on instances of the MOSCP with 2, 3, and 4 objectives, 20, 40, 80 and 200 rows, and 200, 400, 800 and 1000 columns. The results of the Experiment indicate good Computational efficiency of the multiple-objective metaheuristics in comparison to their single-objective counterparts.
Willy Herroelen - One of the best experts on this subject based on the ideXlab platform.
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proactive policies for the stochastic resource constrained project scheduling problem
European Journal of Operational Research, 2011Co-Authors: Filip Deblaere, Erik Demeulemeester, Willy HerroelenAbstract:The resource-constrained project scheduling problem involves the determination of a schedule of the project activities, satisfying the precedence and resource constraints while minimizing the project duration. In practice, activity durations may be subject to variability. We propose a stochastic methodology for the determination of a project execution policy and a vector of predictive activity starting times with the objective of minimizing a cost function that consists of the weighted expected activity starting time deviations and the penalties or bonuses associated with late or early project completion. In a Computational Experiment, we show that our procedure greatly outperforms existing algorithms described in the literature.
Matej Crepinsek - One of the best experts on this subject based on the ideXlab platform.
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a chess rating system for evolutionary algorithms a new method for the comparison and ranking of evolutionary algorithms
Information Sciences, 2014Co-Authors: Niki Vecek, Marjan Mernik, Matej CrepinsekAbstract:Abstract The Null Hypothesis Significance Testing (NHST) is of utmost importance for comparing evolutionary algorithms as the performance of one algorithm over another can be scientifically proven. However, NHST is often misused, improperly applied and misinterpreted. In order to avoid the pitfalls of NHST usage this paper proposes a new method, a Chess Rating System for Evolutionary Algorithms (CRS4EAs) for the comparison and ranking of evolutionary algorithms. A Computational Experiment in CRS4EAs is conducted in the form of a tournament where the evolutionary algorithms are treated as chess players and a comparison between the solutions of two algorithms on the objective function is treated as one game outcome. The rating system used in CRS4EAs was inspired by the Glicko-2 rating system, based on the Bradley–Terry model for dynamic pairwise comparisons, where each algorithm is represented by rating, rating deviation, a rating/confidence interval, and rating volatility. The CRS4EAs was empirically compared to NHST within a Computational Experiment conducted on 16 evolutionary algorithms and a benchmark suite of 20 numerical minimisation problems. The analysis of the results shows that the CRS4EAs is comparable with NHST but may also have many additional benefits. The computations in CRS4EAs are less complicated and sensitive than those in statistical significance tests, the method is less sensitive to outliers, reliable ratings can be obtained over a small number of runs, and the conservativity/liberality of CRS4EAs is easier to control.
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a chess rating system for evolutionary algorithms a new method for the comparison and ranking of evolutionary algorithms
Information Sciences, 2014Co-Authors: Niki Vecek, Marjan Mernik, Matej CrepinsekAbstract:Abstract The Null Hypothesis Significance Testing (NHST) is of utmost importance for comparing evolutionary algorithms as the performance of one algorithm over another can be scientifically proven. However, NHST is often misused, improperly applied and misinterpreted. In order to avoid the pitfalls of NHST usage this paper proposes a new method, a Chess Rating System for Evolutionary Algorithms (CRS4EAs) for the comparison and ranking of evolutionary algorithms. A Computational Experiment in CRS4EAs is conducted in the form of a tournament where the evolutionary algorithms are treated as chess players and a comparison between the solutions of two algorithms on the objective function is treated as one game outcome. The rating system used in CRS4EAs was inspired by the Glicko-2 rating system, based on the Bradley–Terry model for dynamic pairwise comparisons, where each algorithm is represented by rating, rating deviation, a rating/confidence interval, and rating volatility. The CRS4EAs was empirically compared to NHST within a Computational Experiment conducted on 16 evolutionary algorithms and a benchmark suite of 20 numerical minimisation problems. The analysis of the results shows that the CRS4EAs is comparable with NHST but may also have many additional benefits. The computations in CRS4EAs are less complicated and sensitive than those in statistical significance tests, the method is less sensitive to outliers, reliable ratings can be obtained over a small number of runs, and the conservativity/liberality of CRS4EAs is easier to control.
Erik Demeulemeester - One of the best experts on this subject based on the ideXlab platform.
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a purely proactive scheduling procedure for the resource constrained project scheduling problem with stochastic activity durations
Journal of Scheduling, 2016Co-Authors: Patricio Lamas, Erik DemeulemeesterAbstract:The purpose of this research is to develop a new procedure for generating a proactive baseline schedule for the resource-constrained project scheduling problem. The main advantage of this new procedure is that it is completely independent of the reactive policy applied. This contrasts with the traditional methods that assume a predefined reactive policy. First, we define a new robustness measure, then we introduce a branch-and-cut method for solving a sample average approximation of our original problem. In a Computational Experiment, we show that our procedure outperforms two other published methods, assuming different robustness measures.
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proactive policies for the stochastic resource constrained project scheduling problem
European Journal of Operational Research, 2011Co-Authors: Filip Deblaere, Erik Demeulemeester, Willy HerroelenAbstract:The resource-constrained project scheduling problem involves the determination of a schedule of the project activities, satisfying the precedence and resource constraints while minimizing the project duration. In practice, activity durations may be subject to variability. We propose a stochastic methodology for the determination of a project execution policy and a vector of predictive activity starting times with the objective of minimizing a cost function that consists of the weighted expected activity starting time deviations and the penalties or bonuses associated with late or early project completion. In a Computational Experiment, we show that our procedure greatly outperforms existing algorithms described in the literature.
Filip Deblaere - One of the best experts on this subject based on the ideXlab platform.
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proactive policies for the stochastic resource constrained project scheduling problem
European Journal of Operational Research, 2011Co-Authors: Filip Deblaere, Erik Demeulemeester, Willy HerroelenAbstract:The resource-constrained project scheduling problem involves the determination of a schedule of the project activities, satisfying the precedence and resource constraints while minimizing the project duration. In practice, activity durations may be subject to variability. We propose a stochastic methodology for the determination of a project execution policy and a vector of predictive activity starting times with the objective of minimizing a cost function that consists of the weighted expected activity starting time deviations and the penalties or bonuses associated with late or early project completion. In a Computational Experiment, we show that our procedure greatly outperforms existing algorithms described in the literature.
