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The current algorithms of learning the structure of dynamic Bayesian networks attempt to find single "best" model. However, this approach ignores the uncertainty in model selection and is prone to overfitting and local optimal problem. Markov chain Monte Carlo algorithm based on Bayesian model averaging can provide a way for accounting for this model uncertainty, but the convergence is too...
Clustering ensembles have emerged as a powerful method for improving both the robustness and the stability of unsupervised classification solutions. However, finding a consensus clustering from multiple clusterings is a difficult problem that can be approached from graph-based, combinatorial or statistical perspectives. A consensus scheme via the genetic algorithm based on information theory is proposed...
This paper describes two approaches based on evolutionary algorithms for determining Bayesian networks structures from a database of cases. One major difficulty when tackling the problem of structure learning with evolutionary strategies is to avoid the premature convergence of the population to a local optimum. In this paper, we propose two methods in order to overcome this obstacle. The first method...
The logistic distribution has the characteristic of dispersive customer positions, little batches and many repeated routes under common distribution. Therefore, according to the particularity of logistic distribution, the improved cluster first/route second algorithm is adopted to get solutions. Namely, the customer group can be divided into several regions using k-means algorithm in first phase....
It is a key issue that constructing a successful knowledge base to satisfy an efficient adaptive scheduling for the complex manufacturing system. So a hybrid backpropagation (BP)-based scheduling knowledge acquisition algorithm is presented in this paper. We combined genetic algorithm (GA) with simulated annealing (SA) to develop a hybrid optimization method, in which GA was introduced to present...
This paper presents a new method to label parts of human body automatically based on the joint probability density function (PDF). To adapt to different motion for different articulation, the probabilistic models of each triangle different number of mixture components with MML are adopted. To solve the computation load problem of genetic algorithm (GA), a constraint-based genetic algorithm (CBGA)...
As a fundamental operator in genetic algorithms (GAs), crossover may not only make an existing schema survived, but also construct a new one from other existing schemata. Unfortunately, the traditional schema theorem (Holland 1975) does not take into account the positive effects of a schema construction through crossover operation. Thus, they can not well characterize the evolution of a schema. In...
Conceptual innovation in mechanical engineering design has been extremely challenging compared to the wide applications of automated design systems in digital circuits. This paper presents an automated methodology for open-ended synthesis of mechanical vibration absorbers based on genetic programming and bond graphs. It is shown that our automated design system can automatically evolve passive vibration...
This paper investigates the representation of program for program reuse. A new gene structure is proposed: head + body + tail, which allows the program with necessary complexity and putting some learning mechanism into the search process. A new homeotic gene structure is proposed, it not only can call for subroutines easily, but also can automatically perform programming. The concept of different...
For the capacitated minimum spanning tree problem (CMST), there are still no effective algorithms to solve this problem up to now. In this paper, we present a completely new approach by using the genetic algorithms (GAs). For the adaptation to the evolutionary process, we developed a tree-based genetic representation to code the candidate solution of the CMST problem. Numerical analysis shows the...
In this paper, a fast computational method for a class of nonlinear bilevel programming problems is proposed. In these problems, the lower-level problem can be decomposed into some paratactic and independent sub-problems. First, by Karush-Kuhn-Tucker optimality, the stationary-points of these sub-problems corresponding to the upper-level variables can be determined. As a result, this kind of nonlinear...
A novel algorithm called seeker optimization algorithm (SOA) for the real-parameter optimization is proposed in this paper. SOA is based on the concept of simulating the act of human randomized search. In the SOA, after given center point, search direction, search radius, and trust degree, every seeker moves to a new position (next solution) from his current position based on his historical and social...
The satellite module layout problem belongs to the constrained multi-objective optimization problem. Taking the layout design of an international commercial communication satellite as a background, a human-guided genetic algorithm for the satellite module layout problem is proposed, in which a new selection strategy is adopted based on the Deb's constrained-domination principle and the engineering...
In this paper, the adaptive simulated annealing genetic algorithm (ASAGA) is presented by integrating the advantages of adaptive mechanics, simulated annealing algorithm and simple genetic algorithm (SGA). More successful result is got in manipulator trajectory planning using ASAGA compared with simple genetic algorithm. The experiment results show that the method can be used in manipulator trajectory...
Gaussian-Hopfield neural networks (GHNNs) are widely used in identifying nonlinear systems, however, the delta-learning rule is easy to encounter the local minima problem. In this paper, genetic algorithms are adopted to overcome the problem. The proposed method is used to improve the speed of searching for a set of optimal parameters for the GHNNs. To verify the validity of the proposed method, simulation...
This paper presents a co-evolutionary game optimization algorithm (CEGA) to explore the layout design of a simplified international commercial communication satellite module. The method is inspired by game theory and co-evolutionary optimization algorithm. Some comparisons are offered with other optimization algorithms in terms of both solution quality and efficiency of computation. These contrast...
Sefrioui introduced the Nash genetic algorithm in 1998. This approach combines genetic algorithms with Nash's idea. Another central achievement of game theory is the introduction of an evolutionary stable strategy, introduced by Maynard Smith in 1982. In this paper, we will try to find ESS as a solution of MOPs using our game model based co-evolutionary algorithm. We present A Game model based co-evolutionary...
This paper presents a genetic algorithm (GA) for generating efficient rules for cost-sensitive misuse detection in intrusion detection systems. The GA employs only the five most relevant features for each attack category for rule generation. Furthermore, it incorporates the different costs of misclassifying attacks in its fitness function to yield rules that are cost sensitive. The generated rules...
Short-term traffic flow prediction is complex but important to handle urban traffic congestion. To acquire accurate traffic flow information beforehand, a prediction model based on the sufficient fusion of GA with NN is developed in this study. In this paper, the detailed formulation of the genetic structure is given and procedures for coding the NN architecture, the number of hidden nodes, activation...
In this paper, a QoS (quality of service) multicast routing scheme in NGI (next generation Internet) is proposed based on genetic engineering and microeconomics. It can not only deal with network status inaccuracy, but also help prevent network overload and meet with intra-group fairness, trying to find a multicast routing tree with bandwidth, delay, delay jitter and error rate satisfaction degree,...
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