Towards an analysis of dynamic environments
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Soft Computing - A Fusion of Foundations, Methodologies and Applications
The role of representations in dynamic knapsack problems
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
The memory indexing evolutionary algorithm for dynamic environments
EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing
Population-Based Incremental Learning With Associative Memory for Dynamic Environments
IEEE Transactions on Evolutionary Computation
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The dynamic, multi-dimensional knapsack problem is an important benchmark for evaluating the performance of evolutionary algorithms in changing environments, especially because it has many real-world applications. In order to analyze the performance of an evolutionary algorithm according to this benchmark, one needs to be able to change the current problem in a controlled manner. Several methods have been proposed to achieve this goal. In this paper, we briefly outline the proposed methods, discuss their shortcomings and propose a new method that can generate changes for a given severity level more reliably. We then present the experimental setup and results for the new method and compare it with existing methods. The current results are promising and promote further study.