Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
Tracking the Red Queen: Measurements of Adaptive Progress in Co-Evolutionary Simulations
Proceedings of the Third European Conference on Advances in Artificial Life
Improving the Performance of Genetic Algorithms in Classifier Systems
Proceedings of the 1st International Conference on Genetic Algorithms
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Increasing Population Diversity Through Cultural Learning
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Diversity in genetic programming: an analysis of measures and correlation with fitness
IEEE Transactions on Evolutionary Computation
Stable cooperation in changing environments
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3
Analysis of the effects of lifetime learning on population fitness using vose model
Proceedings of the 12th annual conference on Genetic and evolutionary computation
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This paper examines the effects of lifetime learning on populations evolving genetically in a series of changing environmets. The analysis of both fitness and diversity of the populations provides an insight into the improved performance provided by lifetime learning. The NK fitness landscape model is employed as the problem task, which has the advantage of being able to generate a variety of fitness landscapes of varying difficulty. Experiments observe the response of populations in an environment where problem difficulty increases and decreases with varying frequency. Results show that lifetime learning is capable of overall higher fitness levels and, in addition, that lifetime learning stimulates the diversity of the population. This increased diversity allows lifetime learning a greater level of recovery and stability than evolutionary learning alone.