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Titlebook: Parallel Problem Solving from Nature, PPSN XI; 11th International C Robert Schaefer,Carlos Cotta,Günter Rudolph Conference proceedings 2010

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楼主: magnify
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Privacy-Preserving Multi-Objective Evolutionary Algorithmsy and security prevents their use for many security sensitive business optimization problems, such as our use case in collaborative supply chain management. We present a technique to construct privacy-preserving algorithms that address multi-objective problems and secure the entire algorithm includi
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Optimizing Delivery Time in Multi-Objective Vehicle Routing Problems with Time Windowsndows, using a homogeneous fleet of vehicles with limited capacity. In this paper, we propose and analyze the performance of an improved multi-objective evolutionary algorithm, that simultaneously minimizes the number of routes, the total travel distance, and the delivery time. Empirical results ind
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Speculative Evaluation in Particle Swarm Optimizatione objective function. However, some functions are more efficiently optimized with more iterations and fewer particles. Accordingly, we take inspiration from speculative execution performed in modern processors and propose speculative evaluation in PSO (SEPSO). Future positions of the particles are s
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Many-Objective Test Problems to Visually Examine the Behavior of Multiobjective Evolution in a Decision Spacepopulation are non-dominated with each other in many-objective EMO algorithms, we may need a different fitness evaluation scheme from the case of two and three objectives. One difficulty in the design of many-objective EMO algorithms is that we cannot visually observe the behavior of multiobjective
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GPGPU-Compatible Archive Based Stochastic Ranking Evolutionary Algorithm (G-ASREA) for Multi-Objective Optimizationposed, that ranks the population with respect to an archive of non-dominated solutions. It reduces the complexity of the deterministic ranking operator from .(..) to .(.) and further speeds up ranking on GPU..Experiments compare G-ASREA with a CPU version of ASREA and NSGA-II on ZDT test functions f
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Hybrid Directional-Biased Evolutionary Algorithm for Multi-Objective Optimization of the multi-objective 0/1 knapsack problem. Although the conventional Multi-objective Optimization Evolutionary Algorithms (MOEAs) regard the weights of all objective functions as equally, hIDEA biases the weights of the objective functions in order to search not only the center of true Pareto opt
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