Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
Genetic Algorithms for Tracking Changing Environments
Proceedings of the 5th International Conference on Genetic Algorithms
Performance Measures for Dynamic Environments
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Measurement of Population Diversity
Selected Papers from the 5th European Conference on Artificial Evolution
Informative performance metrics for dynamic optimisation problems
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Solving dynamic constrained optimization problems with asynchronous change pattern
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part I
How long should we run in dynamic optimization?
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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Measuring the performance of algorithms over dynamic optimization problems (DOPs) presents some important differences when compared to static ones. One of the main problems is the loss of solution quality as the optimization process advances in time. The objective in DOPs is tracking the optima as the landscape changes; however it is possible that the algorithm fails to follow the optima after some changes happened. The main goal in this article is to introduce a new way of measuring how algorithms are able to maintain their performance during the dynamic optimization process. We propose a measure based on linear regression and study its behaviour. In order to do so, we propose a scenario based on the moving peaks benchmark and analyze our results using several metrics existing in the literature. We test our measure for degradation on the same scenario, applying it over accuracy values obtained for each period, and obtain results which help us to explain changes in algorithm performances.