A weight-coded genetic algorithm for the multiple container packing problem
Proceedings of the 1999 ACM symposium on Applied computing
The multiple container packing problem: a genetic algorithm approach with weighted codings
ACM SIGAPP Applied Computing Review
A Genetic Algorithm for the Multidimensional Knapsack Problem
Journal of Heuristics
Genetic Algorithms for the Multiple Container Packing Problem
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
'Adaptive Link Adjustment' Applied to the Fixed Charge Transportation Problem
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
The edge-window-decoder representation for tree-based problems
IEEE Transactions on Evolutionary Computation
A memetic algorithm for the quadratic multiple container packing problem
Applied Intelligence
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In this paper, we propose a new version of Adaptive Link Adjustment Evolutionary Algorithm (ALA-EA) for the network optimization problems, and apply it to the multiple container packing problem (MCPP). Because the proposed algorithm uses a different encoding method from that of the original ALA-EA, we also need different decoding methods for the new algorithm. In addition, to improve the performance of the proposed algorithm, we incorporate heuristic local improvement approaches into it. To verify the effectiveness of the proposed algorithm we compare it with the existing evolutionary approaches for several instances, which are known to be extremely difficult to them. Computational tests show that the algorithm is superior to the existing evolutionary approaches and the original ALA-EA in both of the solution quality and the computational time. Moreover, the performance seems to be not affected by an instance property.