Improving Memory-based Evolutionary Algorithms in Changing Environments



When using Evolutionary Algorithms (EAs) in no stationary problems some extensions have been introduced in order to avoid the convergence of the population towards a point of the search space. One of these improvements consists in the use of explicit memory responsible for storing good individuals from the search population. When the environment is cyclic and previous environments reappear later, memory should allow continuous progression of the EA's performance with the least decline of the individuals' fitness. But in most situations this purpose is not achieved, and the typical behavior of an EA when a change happens is the best-fitness decrease and some time is necessary to readapting to the new conditions. The key problem when using explicit memory is the size's restrictions usually imposed. So, when it is necessary to store a new individual and memory is full, it is necessary to replace individuals. This replacement can lead to the destruction of information that might be useful in the future. In this work we are interested in the enhancement of memory's usage and we propose two new replacing methods to apply when memory is full. The investigated methods are tested in several memory-based EAs and the obtained results show that memory can be used in a more effective way such that the algorithms' performance is strongly improved.


Evolutionary Algorithms, Dynamic Environments, Memory, Diversity, Replacing Strategies


Evolutionary Optimization

TechReport Number

TR 2007/004

Cited by

Year 2009 : 1 citations

 J. Tim Hendtlass, Irene Moser, Marcus Randal (2009). Dynamic Problems and Nature Inspired Meta-heuristics. Biologically-Inspired Optimisation Methods , Series Studies in Computational Intelligence, Volume 210, pp. 79-109, Springer 2009.