Proceedings of the third international conference on Genetic algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Using Optimal Dependency-Trees for Combinational Optimization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Finding Multimodal Solutions Using Restricted Tournament Selection
Proceedings of the 6th International Conference on Genetic Algorithms
Modeling Building-Block Interdependency
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Combinatonal Optimization by Learning and Simulation of Bayesian Networks
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Core Problems in Knapsack Algorithms
Operations Research
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Replacement strategies to preserve useful diversity in steady-state genetic algorithms
Information Sciences: an International Journal
An evolutionary algorithm with guided mutation for the maximum clique problem
IEEE Transactions on Evolutionary Computation
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This work studies the problem of premature convergence due to the lack of diversity in Estimation of Distributions Algorithms. This problem is quite important for these kind of algorithms since, even when using very complex probabilistic models, they can not solve certain optimization problems such as some deceptive, hierarchical or multimodal ones. There are several works in literature which propose different techniques to deal with premature convergence. In most cases, they arise as an adaptation of the techniques used with genetic algorithms, and use randomness to generate individuals. In our work, we study a new scheme which tries to preserve the population diversity. Instead of generating individuals randomly, it uses the information contained in the probability distribution learned from the population. In particular, a new probability distribution is obtained as a variation of the learned one so as to generate individuals with less probability to appear on the evolutionary process. This proposal has been validated experimentally with success with a set of different test functions.