Nonstationary function optimization using genetic algorithm with dominance and diploidy
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Coevolutionary search among adversaries
Coevolutionary search among adversaries
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Advances in genetic programming: volume 3
Advances in genetic programming: volume 3
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Operating System Concepts
Coevolution, memory and balance
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
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In dynamically changing environments, the performance of a standard evolutionary algorithm deteriorates. This is due to the fact that the population, which is considered to contain the history of the evolutionary process, does not contain enough information to allow the algorithm to react adequately to changes in the fitness landscape. Therefore, we added a simple, global case-based memory to the process to keep track of interesting historical events. Through the introduction of this memory and a storing and replacement scheme we were able to improve the reaction capabilities of an evolutionary algorithm with a periodically changing fitness function.