Population initialization is a crucial task in evolutionary algorithms because it can affect the convergence speed and also the quality of the final solution. If no information about the solution is available, then random initialization is the most commonly used method to generate candidate solutions (initial population). This paper proposes a novel initialization approach which employs opposit...

In this paper, we experimentally show that the initialization process is very important for Grammar Guided Genetic Programming (GGGP). In particular, using different initialization trategies (algorithms) can lead to very different overall results with GGGP. We also show that on the problems tried, the initialization algorithm from Tree Adjoining Grammar Guided Genetic Programming (TAG3P) helps ...

Leader election and initialization are two fundamental problems in mobile ad hoc networks (MANET s). The leader can serve as a coordinator in the MANET s and the initialization protocol can assign each host a unique and short id. As we know that none of the research on initialization for IEEE 802.11-basedMANET s has been done. Here, we proposed two contention-based leader election and initializ...

In this paper, a new re-initialization method for the conservative level-set function is put forward. First, it has been shown that the re-initialization and advection equations of the conservative level-set function are mathematically equivalent to the re-initialization and advection equations of the localized signed distance function. Next, a new discretization for the spatial derivatives of ...

The multiple active contours (snakes) are widely used in image processing to extract the feature of interest. However, initialization of the active contours is still an open problem. This paper proposes a new completely automatic initialization of snakes based on a combination of Phrase Portrait Analysis (PPA), K-means clustering and multi-stage evolution of the active contours. The proposed me...

Convergence rate of training algorithms for neural networks is heavily affected by initialization of weights. In this paper, an original algorithm for initialization of weights in backpropagation neural net is presented with application to character recognition. The initialization method is mainly based on a customization of the Kalman filter, translating it into Bayesian statistics terms. A me...

The k-means algorithm is widely used in many applications due to its simplicity and fast speed. However, its result is very sensitive to the initialization step: choosing initial cluster centers. Different initialization algorithms may lead to different clustering results and may also affect the convergence of the method. In this paper, we propose a new algorithm for improving the initializatio...

Numerical difficulty arises when DAEs are solved with inconsistent initial values of dependent variables. It can cause the solution failures of many popular DAEs solvers. An initialization subroutine, DAEIS (Differential Algebraic Equations Initialization Subroutine), has been developed to handle this issue. In DAEIS, consistent initial values of dependent variables for index-1 DAEs are obtaine...