Multiple Adaptive Agents for Tactical Driving
Applied Intelligence
Bayesian Evolutionary Optimization Using Helmholtz Machines
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Model-Based Search for Combinatorial Optimization: A Comparative Study
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
A unified Bayesian framework for evolutionary learning and optimization
Advances in evolutionary computing
Intelligent data analysis
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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
Using cluster computing to solve a real-world FAP problem
PDCN '08 Proceedings of the IASTED International Conference on Parallel and Distributed Computing and Networks
Binary rule encoding schemes: a study using the compact classifier system
IWLCS'03-05 Proceedings of the 2003-2005 international conference on Learning classifier systems
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A memory efficient and continuous-valued compact EDA for large scale problems
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part I
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Fundamenta Informaticae
An extension of hill-climbing with learning applied to a symbolic regression of boolean functions
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Goldenberry: EDA visual programming in orange
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Order statistics and region-based evolutionary computation
Journal of Global Optimization
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We present an abstraction of the genetic algorithm (GA), termed population-based incremental learning (PBIL), that explicitly maintains the statistics contained in a GA''s population, but which abstracts away the crossover operator and redefines the role of the population. This results in PBIL being simpler, both computationally and theoretically, than the GA. Empirical results reported elsewhere show that PBIL is faster and more effective than the GA on a large set of commonly used benchmark problems. Here we present results on a problem custom designed to benefit both from the GA''s crossover operator and from its use of a population. The results show that PBIL performs as well as, or better than, GAs carefully tuned to do well on this problem. This suggests that even on problems custom designed for GAs, much of the power of the GA may derive from the statistics maintained implicitly in its population, and not from the population itself nor from the crossover operator.