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On the basis of giving a description of Shannon entropy and the improved genetic algorithm, this paper presents a combat information processing method which is based on Shannon entropy and the improved genetic algorithm. This method uses Shannon entropy to make uncertainty measurement for combat information and uses genetic algorithm to calculate, reduce and solve complex problems, which is very effective...
The goal of this research is to develop a new algorithm named social behavior algorithm (SBA) which is extended from genetic algorithm and adds some social value as rule for improving genetic algorithm. The social value include citizens can not marry relative, citizens prefer to marry spouses whose condition match themselves, citizens prefer to decide the volume of children based on their condition...
This paper proposes a different type of Genetic Network Programming (GNP) — Variable Size Genetic Network Programming (GNPvs) with Binomial Distribution. In contrast to the individuals with fixed size in Standard GNP, GNPvs will change the size of the individuals and obtain the optimal size of them during evolution. The proposed method defines a new type of crossover to implement the new feature of...
As stated in the building block hypothesis, we expect genetic algorithms (GAs) to create building blocks (BBs) and combine them appropriately in the evolutionary process. However, such BBs are often destroyed by unwanted crossovers, soon after they are created. Also, we may suffer from a “loose” encoding of chromosomes since BBs are in general unknown. In this paper, we propose a framework named GAP...
An action of genetic algorithm could be represented in the search space as a random Markovian process. The question concerning its asymptotic stability properties is discussed. Conditions under which genetic algorithm is convergent, are formulated. Then the existence of an operator to which infinite long iterations of the genetic algorithms tend, is shown. This operator describes optimal genetic algorithm...
Accurate time series forecasting are important for displaying the manner in which the past continues to affect the future and for planning our day to-day activities. In recent years, a large literature has evolved on the use of evolving artificial neural networks (EANNs) in many forecasting applications. Evolving neural networks are particularly appealing because of their ability to model an unspecified...
We present a non-parametric compact genetic algorithm (cGA) employing a new update strategy of the probability vector (PV) based on Bayesian networks. Since the cGAs use the PV of the current population to reproduce offsprings of the next generation instead of the genetic operators such as crossover and mutation, the cGA needs no parameter tuning. Besides, the cGA has some advantages that the cGA...
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