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Dynamic optimization has attracted much attention for its wide applications in engineering problems. However, it is still a challenge for high nonlinear, multi-dimensional and multimodal problems. Estimation of Distribution Algorithm was proposed in which probabilistic models extracted relevant features of the complex search space and then generated new individuals during optimization. In order to...
Estimation of Distribution Algorithm (EDA) is a kind of new evolutionary algorithm which updates and samples from probabilistic model in evolutionary computation. Recently it is used to solve multi-objective problems. The key is how to construct probability model suitable for real distribution and how to keep diversity of solutions. In this paper a new multi-objective evolutionary of distribution...
Estimation of Distribution Algorithm is a new population based evolutionary optimization method and it generates new population from probability distribution model. Like most evolutionary algorithms, it is easy to trap into local optimums. In order to avoid this shortcoming, Gaussian and Cauchy probability density function are mixed as probability distribution model. For continuous problems, a new...
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