Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Extending Population-Based Incremental Learning to Continuous Search Spaces
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
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AE '97 Selected Papers from the Third European Conference on Artificial Evolution
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GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
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Initial approaches to the application of islands-based parallel EDAs in continuous domains
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An estimation of distribution particle swarm optimization algorithm
ANTS'06 Proceedings of the 5th international conference on Ant Colony Optimization and Swarm Intelligence
Black-box optimization benchmarking for noiseless function testbed using an EDA and PSO hybrid
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A modified particle swarm optimizer with a novel operator
AICI'10 Proceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part II
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Particle Swarm Optimization (PSO) is a stochastic optimization approach that originated from earlier attempts to simulate the behavior of birds and was successfully applied in many applications as an optimization tool. Estimation of distributions algorithms (EDAs) are a class of evolutionary algorithms which build a probabilistic model capturing the search space properties and use this model to generate new individuals. One research trend that emerged in the past few years is the hybridization of PSO and EDA algorithms. In this work, we examine one of these hybrids attempts that uses a Gaussian model for capturing the search space characteristics. We compare two different approaches, previously introduced into EDAs to prevent premature convergence, when incorporated into this hybrid algorithm. The performance of the hybrid algorithm with and without these approaches is studied using a suite of well-known benchmark optimization functions.