Titlebook: Evolutionary Computation in Combinatorial Optimization; 15th European Confer Gabriela Ochoa,Francisco Chicano Conference proceedings 2015 S

突變

Runtime Analysis of , Evolutionary Algorithm Controlled with Q-learning Using Greedy Exploration St Previously it was shown that it runs in . on . when configured to use the randomized local search algorithm and the Q-learning algorithm with the greedy exploration strategy..We present the runtime analysis for the case when the .-EA algorithm is used. It is shown that the expected running time is at most ..

柔美流暢

Anemia

PHONE

A Variable Neighborhood Search Approach for the Interdependent Lock Scheduling Problem,-world ship trajectories. Notable improvements can be achieved. In addition, the number of (empty) lockages can be significantly reduced when taking them into account during optimization without loosing too much of quality in travel time optimization.

abysmal

Analysis of Solution Quality of a Multiobjective Optimization-Based Evolutionary Algorithm for Knapwo initialisation methods are considered in?the algorithm: local search initialisation and greedy search initialisation. Then the solution quality of the algorithm is analysed in terms of the approximation ratio.

Oversee

True Pareto Fronts for Multi-objective AI Planning Instances, obtained by varying the parameters of the generator. The experimental performances of an actual implementation of the exact solver are demonstrated, and some large instances with remarkable Pareto Front shapes are proposed, that will hopefully become standard benchmarks of the AI planning domain.

frozen-shoulder

牽索

https://doi.org/10.1007/978-3-319-54319-2 well as other methods for the standard set of medium-size problems taken from Beasley’s benchmark, but produces comparatively good results in terms of quality, runtime and memory footprint on our specific benchmark based on real Swedish data.

CLOT

Kapitel 7: Hauptergebnisse der Untersuchung,improved variant of GC-AIS is compared with a well known multi-objective evolutionary algorithm NSGA-II on the multi-objective knapsack problem. We show that our improved GC-AIS performs better than NSGA-II on the instances of the knapsack problem taken from [.] inheriting the same benefits of having to set fewer parameters manually.

Collision