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Since the amount of spatial data grows rapidly during recent years, high dimensionality of the domain attributes presents a further obstacle for a number of rule-induction algorithms that would have the potential for automating knowledge acquisition. This paper attempts to tackle the problem by attribute reduction. Firstly, the problem of attribute reduction can be converted into a 0-1 combinatorial...
A multi-objective evolutionary algorithm with extended minimal generation gap (MGG) model and distance-based density measure is given, which we call DMOEA. DMOEA employs a new technique to estimate the distance between two individuals in the objective space, further, finds the K nearest neighbors on either side of certain individual along one focused objective, calculates the sum of the distances...
The application level multicast overlay network (ON) is composed by the hosts based on the physics unicast links between them. The initial ON is a completely connectivity network, which make the multicast tree construction and routing is lower efficiency. For improving the ON routing performance, the ON links should be selected. Based on a new presented ON link selection model with reliability and...
Niche genetic algorithm (NGA) is superior to genetic algorithm (GA) in multiple hump function optimization. NGA could search all global optimums of multiple hump function in a running. It is a class of parallel evolutionary method which suppresses genetic drift by forming stable subpopulations to maintain population diversity. To algorithm population diversity plays an important role to avoid trapping...
As an effective global optimization method, genetic algorithm has been used in real practice very widely. When it is used in real practice, its slow convergence and poor stability have become the main problems. In order to overcome these problems, from the creation of the initial population, immune selection operation, improved genetic operators, et al, an improved fast immunized genetic algorithm...
Permeability coefficients of dam foundation are inversed on the base of real water heads. Otherwise, simple genetic algorithm has the disadvantages in determining the optimal probability values of the crossover and mutation, computation quantity is great and premature phenomena easily appears. Adaptive genetic algorithm is proposed to overcome the disadvantages which simple genetic algorithm has....
In this paper we investigate the global optimization problems with bounded variables and linear equality constraints. We suggest an approach to drawing sample points randomly from the feasible region. A new population-based global optimization algorithm is proposed. We also show that the algorithm converges to the global optimal solution with probability one. The method is easily extended to global...
Adaptive inertia weight is proposed to rationally balance the global exploration and local exploitation abilities for particle swarm optimization. This paper describes an adaptive strategy for tuning the inertia weight parameter of the PSO algorithm - Exponential type adaptive inertia weighted Particle Swarm Optimization (EPSO). This adaptive tuning strategy is based on the inertia weight dynamic...
On the basis of the analysis of canonical genetic algorithm's shortage, a novel genetic algorithm based on dynastic changes mechanism of nation (DCGA) is proposed. In DCGA, populations one by one evolve according to the relay race mode, by which the DCGA holds a mechanism of transferring from a local optimal solution to another better one and finally finding the global optimal solution under the guidance...
By integrating the global searching advantage of Genetic Algorithm (GA) and the local searching ability of BP Artificial Neural Network (BP ANN), this paper proposes a new model of BP ANN based on GA (called GA-BP ANN). Firstly, it applies GA to optimize the initial interconnecting weights and thresholds of BP ANN. Then, it utilizes the BP algorithm to train the neural network more accurately. This...
Neural networks (NNs) have been widely used to predict financial distress because of their excellent performances of treating non-linear data with self-learning capability. However, common neural networks often suffer from long convergent processes and occasionally involve in a local optimal solution that more or less limited their applications in practice. To overcome the drawbacks of neural networks,...
The paper proposes the new model of the genetic algorithm and BP neural networks to predict the blood concentration of Cyclosporine. The BP model was optimized by genetic algorithm to overcome the slower convergence speed, and the best result was found in the particular condition with the strong search function of genetic algorithm. The prediction precision of average blood concentration of Cyclosporine...
Color management for the printer is one of the key techniques in the color image reproduction. A New color management model is presented based on analyzing the printer color rendering principle. First, the paper takes standard color target for experimental sample, and substitutes color blocks in color shade district for complete color space to decrease calculation and improve process speed. Second,...
This paper modified the structure of the original PSO algorithm. It proposes that the particles' position have relationship with the one particle's and the whole swarm's perceive extent in the processing of this time and last time, and presents the inertial weight based on simulated annealing temperature. So a new Particle Swarm Optimization algorithm (NPSO) is proposed. It can not improve the one...
To deal with the problem of premature convergence and slow search speed of PSO, inspired by the classical 5-nearest neighbor method, a reinforced self-escape discrete particle swarm optimization algorithm (RSEDPSO) is proposed in this paper. The modified method of selecting candidate edges can enhance the performance of RSEDPSO to explore the global minimum thoroughly. The 5-relative nearest neighbor...
The particle swarm optimization algorithm is a new kind of random optimization algorithm based on the swarm intelligence. It has been used in many field and it work very well. Many papers about the selection of the parameters have been written. This paper shows that the velocity vt of the PSO is cyclic when the parameters are constants. So It gives the reason for the randomicity in the PSO.
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