Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
On the Feasibility Problem of Penalty-Based Evolutionary Algorithms for Knapsack Problems
Proceedings of the EvoWorkshops on Applications of Evolutionary Computing
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Designing Evolutionary Algorithms for Dynamic Environments
Designing Evolutionary Algorithms for Dynamic Environments
Towards an analysis of dynamic environments
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
The role of representations in dynamic knapsack problems
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
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In the steady-state model for genetic algorithms (SSGA), the choice of a replacement strategy plays an important role in performance. Being able to handle changes is important for an optimization algorithm since many real-world problems are dynamic in nature. The main aim of this study is to experimentally compare different variations for basic replacement strategies in a dynamic environment. To cope with changes, a very simple mechanism of duplicate elimination is used. As an example of a dynamic problem, a dynamic version of the multi-dimensional knapsack problem is chosen. The results obtained here are in keeping with previous studies while some further interesting results are also obtained due to the special landscape features of the chosen problem.