Titlebook: Evolutionary Computation in Combinatorial Optimization; 16th European Confer Francisco Chicano,Bin Hu,Pablo García-Sánchez Conference proce

BARGE

Hyperplane Elimination for Quickly Enumerating Local Optima,or finding local optima are limited to very small problem sizes. We propose a method for exploiting problem structure to skip hyperplanes that cannot contain local optima, allowing runtime to scale with the number of local optima instead of with the landscape size. We prove optimality for linear fun

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Limits to Learning in Reinforcement Learning Hyper-heuristics, experimental studies have used . reinforcement learning mechanisms, however, these are inconclusive with regard to the performance of selection hyper-heuristics with these learning mechanisms. This paper points out limitations to learning with . reinforcement learning mechanisms. Our theoretical re

產(chǎn)生

bonnet

Particle Swarm Optimisation with Sequence-Like Indirect Representation for Web Service Composition, active research topic in the past years. The advantage of having an automated composition system is that it allows users to create new applications simply by providing the required parameters, instead of having to manually assemble the services. Existing approaches to automated composition rely on

laxative

Particle Swarm Optimization for Multi-Objective Web Service Location Allocation,st and network latency, are considered simultaneously. In order to solve this new multi-objective problem effectively, we adopted the framework of binary Particle Swarm Optimization (PSO) due to its efficacy that has been demonstrated in many optimization problems. Specifically, we developed two PSO

HARD

Sim-EDA: A Multipopulation Estimation of Distribution Algorithm Based on Problem Similarity,delling. The proposed approach is to tackle several similar instances of the same optimization problem at once. Each subpopulation is assigned to a different instance and a migration mechanism is used for transferring information between the subpopulations. The migration process can be performed usi

DIS

Solving the Quadratic Assignment Problem with Cooperative Parallel Extremal Optimization,cult, for many useful instances. We address this problem using a local search technique, based on Extremal Optimization and present experimental evidence that this approach is competitive. Moreover, cooperative parallel versions of our solver improve performance so much that large and hard instances

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W. Hempelmann,G. Hempelmann,S. Piepenbrockcult, for many useful instances. We address this problem using a local search technique, based on Extremal Optimization and present experimental evidence that this approach is competitive. Moreover, cooperative parallel versions of our solver improve performance so much that large and hard instances can be solved quickly.

tympanometry

Solving the Quadratic Assignment Problem with Cooperative Parallel Extremal Optimization,cult, for many useful instances. We address this problem using a local search technique, based on Extremal Optimization and present experimental evidence that this approach is competitive. Moreover, cooperative parallel versions of our solver improve performance so much that large and hard instances can be solved quickly.

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