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Learning is the core of intelligence algorithm. Genetic algorithm (GA), as an intelligent algorithm, has its own learning mechanism. This paper focuses on the learning matrix of evolutionary operators in GA. From the viewpoint of solution generation, the learning mechanism in GA is studied and the matrix expression of recombination and mutation is given. A new insight of GA from learning viewpoint...
A portfolio selection problem is about finding an optimal scheme to allocate a fixed amount of capital to a set of available assets. The optimal scheme is very helpful for investors in making decisions. However, finding the optimal scheme is difficult and time-consuming especially when the number of assets is large and some actual investment constraints are considered. This paper proposes a new approach...
Decisions for admission scheduling in hospitals are a class of optimization problems constrained by many factors. Instead of scheduling the admission of patients directly, this paper proposes a genetic algorithm (GA) designed for the optimization of a long-term admission strategy for the ophthalmology department in hospitals. For the optimization of admission strategy, we devise a coding scheme of...
Maximizing the lifetime of a sensor network by scheduling operations of sensors is an effective way to construct energy efficient wireless sensor networks. After the random deployment of sensors in the target area, the problem of finding the largest number of disjoint sets of sensors, with every set being able to completely cover the target area, is nondeterministic polynomial-complete. This paper...
The SET k-cover problem is an NP-complete combinatorial optimization problem, which is derived from constructing energy efficient wireless sensor networks (WSNs). The goal of the problem is to find a way to divide sensors into disjoint cover sets, with every cover set being able to fully cover an area and the number of cover sets maximized. Instead of using deterministic algorithms or simple genetic...
In order to reduce the time of neural network self-learning, it proposes an algorithm which combines genetic algorithm (GA) and neural network prediction together. Genetic algorithm is used to search the optimal solution globally, and the data generated by GA during evolutionary process are used to train a predictive network. The predictive network establishes a mapping between parameters of operant...
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