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Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
Removing the Genetics from the Standard Genetic Algorithm
Removing the Genetics from the Standard Genetic Algorithm
Effects of learning rate on the performance of the population based incremental learning algorithm
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Bi-objective multipopulation genetic algorithm for multimodal function optimization
IEEE Transactions on Evolutionary Computation
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Population-Based Incremental Learning (PBIL) is a relatively new class of Evolutionary Algorithms (EA) that has been recently applied to a range of optimization problems in engineering with promising results. PBIL combines aspects of Genetic Algorithm with competitive learning. The learning rate in the standard PBIL is generally fixed which makes it difficult for the algorithm to explore the search space effectively. In this paper, a PBIL with Adapting learning rate is proposed. The Adaptive PBIL (APBIL) is able to thoroughly explore the search space at the start of the run and maintain the diversity longer than the standard PBIL. To show its effectiveness, the proposed algorithm is applied to the problem of optimizing the parameters of a power system controller. Simulation results show that APBIL based controller performs better than the standard PBIL based controller.