Cognitive radio resource allocation based on coupled chaotic genetic algorithm

A coupled chaotic genetic algorithm for cognitive radio resource allocation which is based on genetic algorithm and coupled Logistic map is proposed. A fitness function for cognitive radio resource allocation is provided. Simulations are conducted for cognitive radio resource allocation by using the coupled chaotic genetic algorithm, simple genetic algorithm and dynamic allocation algorithm respectively. The simulation results show that, compared with simple genetic and dynamic allocation algorithm, coupled chaotic genetic algorithm reduces the total transmission power and bit error rate in cognitive radio system, and has faster convergence speed.

Chaotic Genetic Algorithm-Based Forest Harvest Adjustment

Forest harvesting adjustment is a decision-making,large and complex system. In this paper,we analysis the shortcomings of the traditional harvest adjustment problems,and establish the model of multi-target harvest adjustment. As intelligent optimization,chaotic genetic algorithm has the parallel mechanism and the inherent global optimization characteristics which are suitable for multi-objective planning the settlement of the issue,specially in complex occasions where there are many objective functions and optimize variables. In order to solve the problem of forest harvesting adjustment,this paper introduces a genetic algorithm to the Forest Farm of Qiujia Liancheng Longyan for forest harvesting adjustment firstly. And the experimental result shows that the method is feasible and effective,and it can provide satisfactory solution for policy makers.

A self-adaptive stochastic resonance system design and study in chaotic interference

The us of stochastic resonance （SR） can effectively achieve the detection of weak signal in white noise and colored noise. However, SR in chaotic interference is seldom involved. In view of the requirements for the detection of weak signal in the actual project and the relationship between the signal, chaotic interference, and nonlinear system in the bistable system, a self-adaptive SR system based on genetic algorithm is designed in this paper. It regards the output signal-to-noise ratio （SNR） as a fitness function and the system parameters are jointly encoded to gain optimal bistable system parameters, then the input signal is processed in the SR system with the optimal system parameters. Experimental results show that the system can keep the best state of SR under the condition of low input SNR, which ensures the effective detection and process of weak signal in low input SNR.

Current hypotheses to explain genetic chaos under the sea

Chaotic genetic patchiness （CGP） refers to surprising patterns of spatial and temporal genetic structure observed in some marine species at a scale where genetic variation should be efficiently homogenized by gene flow via larval dispersal. Here we review and discuss 4 mechanisms that could generate such unexpected patterns： selection, sweepstakes reproductive success, collective dispersal, and temporal shifts in local population dynamics. First, we review examples where genetic differentiation at specific loci was driven by diversifying selection, which was historically the first process invoked to explain CGP. Second, we turn to neutral demographic processes that may drive genome-wide effects, and whose effects on CGP may be enhanced when they act together. We discuss how sweepstakes reproductive success accelerates genetic drift and can thus generate genetic structure, provided that gene flow is not too strong. Collective dispersal is another mechanism whereby genetic structure can be maintained regardless of dispersal intensity, because it may prevent larval cohorts from becoming entirely mixed. Theoretical analyses of both the sweepstakes and the collective dispersal ideas are presented. Finally, we discuss an idea that has received less attention than the other ones just mentioned, namely temporal shifts in local population dynamics.