scholarly journals Design and Application of Model Based on Bayesian Network

2021 ◽  
Vol 2074 (1) ◽  
pp. 012037
Author(s):  
Ying Shi
Keyword(s):  
Bayesian Network ◽  
Fitness Function ◽  
Parameter Learning ◽  
Error Feedback ◽  
Accuracy Rate ◽  
Network Learning ◽  
Knowledge Reasoning ◽  
Mutation Operators ◽  
Coding Method ◽  
Crossover And Mutation

Abstract At present, Bayesian networks lack consistent algorithms that support structure establishment, parameter learning, and knowledge reasoning, making it impossible to connect knowledge establishment and application processes. In view of this situation, by designing a genetic algorithm coding method suitable for Bayesian network learning, crossover and mutation operators with adjustment strategies, the fitness function for reasoning error feedback can be carried out. Experimental results show that the new algorithm not only simultaneously optimizes the network structure and parameters, but also can adaptively learn and correct inference errors, and has a more satisfactory knowledge inference accuracy rate.

Evolutionary Nonlinear Multimodel Partitioning Filters

2001 ◽  
Vol 5 (1) ◽  
pp. 8-14 ◽  
Author(s):  
G. N. Befigiannis ◽  
◽  
E. N. Demiris ◽  
S. D. Likothanassis ◽  
◽  
...  
Keyword(s):  
Adaptive Filters ◽  
Initial Conditions ◽  
Fitness Function ◽  
Specific Model ◽  
Posteriori Probability ◽  
Extended Kalman Filters ◽  
A Posteriori Probability ◽  
Mutation Operators ◽  
Conditional Models ◽  
Crossover And Mutation

The problem of designing adaptive filters for nonlinear systems is faced in this work. The proposed evolution program combines the effectiveness of multimodel adaptive filters and the robustness of genetic algorithms (GAs). Specifically, a bank of different extended Kalman filters is implemented. Then, the a posteriori probability that a specific model of the bank of conditional models is the true one can be used as a GA fitness function. The superiority of the algorithm is that it evolves concurrently the models’ population with initial conditions. Thus, this procedure alleviates extended Kalman filter sensitivity in initial conditions, by estimating the best values. In addition to this, adaptive implementation is proposed that relieves the disadvantage of time-consuming GA implementation. Finally, a variety of defined crossover and mutation operators is investigated in order to accelerate the algorithm’s convergence.

scholarly journals An Island Grouping Genetic Algorithm for Fuzzy Partitioning Problems

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
S. Salcedo-Sanz ◽  
J. Del Ser ◽  
Z. W. Geem
Keyword(s):  
Fuzzy Clustering ◽  
Fitness Function ◽  
Distance Measures ◽  
Mutation Operators ◽  
Fuzzy Partitioning ◽  
Grouping Genetic Algorithm ◽  
Partitioning Problems ◽  
Overall Performance ◽  
Clustering Problems ◽  
Crossover And Mutation

This paper presents a novel fuzzy clustering technique based on grouping genetic algorithms (GGAs), which are a class of evolutionary algorithms especially modified to tackle grouping problems. Our approach hinges on a GGA devised for fuzzy clustering by means of a novel encoding of individuals (containing elements and clusters sections), a new fitness function (a superior modification of the Davies Bouldin index), specially tailored crossover and mutation operators, and the use of a scheme based on a local search and a parallelization process, inspired from an island-based model of evolution. The overall performance of our approach has been assessed over a number of synthetic and real fuzzy clustering problems with different objective functions and distance measures, from which it is concluded that the proposed approach shows excellent performance in all cases.

Data Mining of Bayesian Network Structure Using a Semantic Genetic Algorithm-Based Approach

2007 ◽  
pp. 42-53 ◽  
Author(s):  
Sachin Shetty ◽  
Min Song ◽  
Mansoor Alam
Keyword(s):  
Data Mining ◽  
Genetic Algorithm ◽  
Bayesian Network ◽  
Network Structure ◽  
Initial Population ◽  
Network Structures ◽  
Bayesian Network Structure ◽  
Mutation Operators ◽  
Crossover And Mutation ◽  
Intuitive Interpretation

A Bayesian network model is a popular formalism for data mining due to its intuitive interpretation. This chapter presents a semantic genetic algorithm (SGA) to learn the best Bayesian network structure from a database. SGA builds on recent advances in the field and focuses on the generation of initial population, crossover, and mutation operators. In SGA, we introduce semantic crossover and mutation operators to aid in obtaining accurate solutions. The crossover and mutation operators incorporate the semantic of Bayesian network structures to learn the structure with very minimal errors. SGA has been proven to discover Bayesian networks with greater accuracy than existing classical genetic algorithms. We present empirical results to prove the accuracy of SGA in predicting the Bayesian network structures.

Data Mining of Bayesian Network Structure Using a Semantic Genetic Algorithm-Based Approach

2008 ◽  
pp. 1081-1090
Author(s):  
Sachin Shetty ◽  
Min Song ◽  
Mansoor Alam
Keyword(s):  
Data Mining ◽  
Genetic Algorithm ◽  
Bayesian Network ◽  
Network Structure ◽  
Initial Population ◽  
Network Structures ◽  
Bayesian Network Structure ◽  
Mutation Operators ◽  
Crossover And Mutation ◽  
Intuitive Interpretation

A Bayesian network model is a popular formalism for data mining due to its intuitive interpretation. This chapter presents a semantic genetic algorithm (SGA) to learn the best Bayesian network structure from a database. SGA builds on recent advances in the field and focuses on the generation of initial population, crossover, and mutation operators. In SGA, we introduce semantic crossover and mutation operators to aid in obtaining accurate solutions. The crossover and mutation operators incorporate the semantic of Bayesian network structures to learn the structure with very minimal errors. SGA has been proven to discover Bayesian networks with greater accuracy than existing classical genetic algorithms. We present empirical results to prove the accuracy of SGA in predicting the Bayesian network structures.

Finding Community of Brain Networks Based on Neighbor Index and DPSO with Dynamic Crossover

2020 ◽  
Vol 15 (4) ◽  
pp. 287-299
Author(s):  
Jie Zhang ◽  
Junhong Feng ◽  
Fang-Xiang Wu
Keyword(s):  
Brain Function ◽  
Brain Networks ◽  
Discrete Particle Swarm Optimization ◽  
Mri Brain ◽  
Mutation Operators ◽  
Neural Unit ◽  
Crossover And Mutation ◽  
The Brain ◽  
Index Table ◽  
Dynamic Crossover

Background: : The brain networks can provide us an effective way to analyze brain function and brain disease detection. In brain networks, there exist some import neural unit modules, which contain meaningful biological insights. Objective:: Therefore, we need to find the optimal neural unit modules effectively and efficiently. Method:: In this study, we propose a novel algorithm to find community modules of brain networks by combining Neighbor Index and Discrete Particle Swarm Optimization (DPSO) with dynamic crossover, abbreviated as NIDPSO. The differences between this study and the existing ones lie in that NIDPSO is proposed first to find community modules of brain networks, and dose not need to predefine and preestimate the number of communities in advance. Results: : We generate a neighbor index table to alleviate and eliminate ineffective searches and design a novel coding by which we can determine the community without computing the distances amongst vertices in brain networks. Furthermore, dynamic crossover and mutation operators are designed to modify NIDPSO so as to alleviate the drawback of premature convergence in DPSO. Conclusion: The numerical results performing on several resting-state functional MRI brain networks demonstrate that NIDPSO outperforms or is comparable with other competing methods in terms of modularity, coverage and conductance metrics.

TABU PROGRAMMING: A NEW PROBLEM SOLVER THROUGH ADAPTIVE MEMORY PROGRAMMING OVER TREE DATA STRUCTURES

2011 ◽  
Vol 10 (02) ◽  
pp. 373-406 ◽  
Author(s):  
ABDEL-RAHMAN HEDAR ◽  
EMAD MABROUK ◽  
MASAO FUKUSHIMA
Keyword(s):  
Search Algorithm ◽  
Benchmark Problems ◽  
Problem Solver ◽  
Learning Tools ◽  
Mutation Operators ◽  
Tree Data ◽  
Tree Data Structure ◽  
Comprehensive Framework ◽  
Rate Of Success ◽  
Crossover And Mutation

Since the first appearance of the Genetic Programming (GP) algorithm, extensive theoretical and application studies on it have been conducted. Nowadays, the GP algorithm is considered one of the most important tools in Artificial Intelligence (AI). Nevertheless, several questions have been raised about the complexity of the GP algorithm and the disruption effect of the crossover and mutation operators. In this paper, the Tabu Programming (TP) algorithm is proposed to employ the search strategy of the classical Tabu Search algorithm with the tree data structure. Moreover, the TP algorithm exploits a set of local search procedures over a tree space in order to mitigate the drawbacks of the crossover and mutation operators. Extensive numerical experiments are performed to study the performance of the proposed algorithm for a set of benchmark problems. The results of those experiments show that the TP algorithm compares favorably to recent versions of the GP algorithm in terms of computational efforts and the rate of success. Finally, we present a comprehensive framework called Meta-Heuristics Programming (MHP) as general machine learning tools.

Simulation of the Pressure-Bearing Capacity Test Model in Architectural Decoration Design

2014 ◽  
Vol 716-717 ◽  
pp. 391-394
Author(s):  
Li Mei Guo ◽  
Ai Min Xiao
Keyword(s):  
Genetic Algorithm ◽  
Bearing Capacity ◽  
Test Method ◽  
Test Model ◽  
Improved Genetic Algorithm ◽  
Good Convergence ◽  
Mutation Operators ◽  
Process Pressure ◽  
Calculation Process ◽  
Crossover And Mutation

in architectural decoration process, pressure-bearing capacity test is the foundation of design, and is very important. To this end, a pressure-bearing capacity test method in architectural decoration design is proposed based on improved genetic algorithm. The selection, crossover and mutation operators in genetic algorithm are improved respectively. Using its fast convergence characteristics eliminate the pressure movement in the calculation process. The abnormal area of pressure-bearing existed in buildings which can ensure to be tested is added, to obtain accurate distribution information of the abnormal area of pressure-bearing. Simulation results show that the improved genetic algorithm has good convergence, can accurately test the pressure-bearing capacity in architectural decoration.

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