scholarly journals An Efficient Genetic Algorithm for Small Search Range Problems and Its Applications

2006 ◽  
pp. 253-280 ◽  
Author(s):  
Ja-Ling Wu ◽  
Chun-Hung Lin ◽  
Chun-Hsiang Huang
Keyword(s):  
Genetic Algorithm ◽  
Search Range

Based on GA Mixed with Trust Region Method Solving Nonlinear Least Square Problems

2011 ◽  
Vol 141 ◽  
pp. 92-97
Author(s):  
Miao Hu ◽  
Tai Yong Wang ◽  
Bo Geng ◽  
Qi Chen Wang ◽  
Dian Peng Li
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Trust Region Method ◽  
Trust Region ◽  
Least Square ◽  
Search Range ◽  
Genetic Search ◽  
Region Method ◽  
Unconstrained Optimization Problems ◽  
Least Square Problems

Nonlinear least square is one of the unconstrained optimization problems. In order to solve the least square trust region sub-problem, a genetic algorithm (GA) of global convergence was applied, and the premature convergence of genetic algorithms was also overcome through optimizing the search range of GA with trust region method (TRM), and the convergence rate of genetic algorithm was increased by the randomness of the genetic search. Finally, an example of banana function was established to verify the GA, and the results show the practicability and precision of this algorithm.

scholarly journals Improving of Data Processing Effectiveness for Pavement Structural Evaluation Using Subsurface Radar Probing

2013 ◽  
Vol 14 (2) ◽  
pp. 143-154
Author(s):  
Alexander Krainyukov ◽  
Valery Kutev
Keyword(s):  
Genetic Algorithm ◽  
Inverse Problem ◽  
Data Processing ◽  
Frequency Domain ◽  
Iterative Procedure ◽  
Search Range ◽  
Structural Evaluation ◽  
Modified Genetic Algorithm ◽  
Pavement Structure ◽  
Structure Evaluation

Problems of the data processing improving for pavement structure evaluation with help of subsurface radar probing are discussed. Iterative procedure to solve the inverse problem in frequency domain is used on the base of the genetic algorithm. For improving of data processing effectiveness it is proposed to use a modified genetic algorithm with adaptation of search range of pavement parameters. The results of reconstruction of electro-physical characteristics for model of five-layered pavement structure are presented.

Improved Multiple Point Non-Linear Genetic Algorithm Based Performance Adaptation Using Least Square Method

2011 ◽  
Author(s):  
Y. G. Li ◽  
M. F. Abdul Ghafir ◽  
L. Wang ◽  
R. Singh ◽  
K. Huang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Engine Performance ◽  
Least Square Method ◽  
Scaling Factor ◽  
Least Square ◽  
Multiple Point ◽  
Search Range ◽  
Non Linear ◽  
Performance Adaptation ◽  
Adaptation Method

At off-design conditions, engine performance model prediction accuracy depends largely on its component characteristic maps. With the absence of actual characteristic maps, performance adaptation needs to be done for good imitations of actual engine performance. A non-linear multiple point Genetic Algorithm based performance adaptation developed earlier by the authors using a set of non-linear scaling factor functions has been proven capable of making accurate performance prediction over a wide range of operating conditions. However, the success depends on searching the right range of scaling factor coefficients heuristically, in order to obtain optimum scaling factor functions. Such search ranges may be difficult to obtain and in many off-design adaption cases, it may be very time consuming due to the nature of trial and error process. In this paper, an improvement on the present adaptation method is presented using a Least Square method where the search range can be selected deterministically. In the new method, off-design adaptation is applied to individual off-design point first to obtain individual off-design point scaling factors. Then plots of the scaling factors against the off-design conditions are generated. Using the Least Square method, the relationship between each scaling factor and the off-design operating condition is generated. The regression coefficients are then used to determine the search range of the scaling factor coefficients before multiple off-design points performance adaptation is finally applied. The developed adaptation approach has been applied to a model single-spool turboshaft engine and demonstrated a simpler and faster way of obtaining the optimal scaling factor coefficients compared with the original off-design adaptation method.

An Improved Genetic Algorithm of Web Services Composition with QOS

2012 ◽  
Vol 532-533 ◽  
pp. 1836-1840 ◽  
Author(s):  
Yan Yan Zhang ◽  
Hai Ling Xiong ◽  
Yong Chun Zhang
Keyword(s):  
Genetic Algorithm ◽  
Web Services ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Experimental Result ◽  
Web Services Composition ◽  
Improved Genetic Algorithm ◽  
Search Range ◽  
Services Composition ◽  
Fitness Value

A web service composition method based on the adaptive genetic operator was proposed to deal with the issues of the lack of adaptability and the easy-premature phenomena in web services composition genetic algorithm. Adaptive crossover and mutation operator were designed according to the individual adaptability and evolution stage for enlarging local search range and increasing convergent speed. Moreover, use for reference the idea of taboo table in taboo search algorithm, we can inhibit the algorithm from converging to false optimal solution untimely; meanwhile, an evolution strategy was adopted to prevent the loss of composite service with high fitness value. The experimental result shows that better composite services can be gotten through the improved algorithm; moreover the convergence speed has also been improved.

Improved Multiple Point Nonlinear Genetic Algorithm Based Performance Adaptation Using Least Square Method

2011 ◽  
Vol 134 (3) ◽  
Author(s):  
Y.G. Li ◽  
M. F. Abdul Ghafir ◽  
L. Wang ◽  
R. Singh ◽  
K. Huang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Engine Performance ◽  
Least Square Method ◽  
Scaling Factor ◽  
Least Square ◽  
Multiple Point ◽  
Search Range ◽  
Design Point ◽  
Performance Adaptation ◽  
Adaptation Method

At off-design conditions, engine performance model prediction accuracy depends largely on its component characteristic maps. With the absence of actual characteristic maps, performance adaptation needs to be done for good imitations of actual engine performance. A nonlinear multiple point genetic algorithm based performance adaptation developed earlier by the authors using a set of nonlinear scaling factor functions has been proven capable of making accurate performance predictions over a wide range of operating conditions. However, the success depends on searching the right range of scaling factor coefficients heuristically, in order to obtain the optimum scaling factor functions. Such search ranges may be difficult to obtain and in many off-design adaption cases, it may be very time consuming due to the nature of the trial and error process. In this paper, an improvement on the present adaptation method is presented using a least square method where the search range can be selected deterministically. In the new method, off-design adaptation is applied to individual off-design point first to obtain individual off-design point scaling factors. Then plots of the scaling factors against the off-design conditions are generated. Using the least square method, the relationship between each scaling factor and the off-design operating condition is generated. The regression coefficients are then used to determine the search range of the scaling factor coefficients before multiple off-design points performance adaptation is finally applied. The developed adaptation approach has been applied to a model single-spool turboshaft engine and demonstrated a simpler and faster way of obtaining the optimal scaling factor coefficients compared with the original off-design adaptation method.

The Application of an Improved Genetic Algorithm to Signal Acquisition

2014 ◽  
Vol 945-949 ◽  
pp. 2319-2322
Author(s):  
Min Shi ◽  
Cong Cong Tian ◽  
Qing Ming Yi ◽  
Song Li
Keyword(s):  
Genetic Algorithm ◽  
Initial Population ◽  
Signal Acquisition ◽  
Improved Genetic Algorithm ◽  
Search Range ◽  
Signal Parameters ◽  
Crossover Probability ◽  
Dynamic Search ◽  
Gps Signal Acquisition ◽  
Adaptive Crossover

The paper proposes an improved genetic algorithm which is applied to the problem of GPS signal acquisition.The improved genetic algorithm uses the small section method of the initial population, dynamic search range of parameter, and the adaptive crossover probability and mutation probability to search GPS signal parameters. Simulations and experiment results show that the improved genetic algorithm can search the signal parameters rapidly and precisely. The GPS signal acquisition performance is improved.

GPS Signal Acquisition Based on an Improved Genetic Algorithm

2013 ◽  
Vol 291-294 ◽  
pp. 2909-2912
Author(s):  
Min Shi ◽  
Cong Cong Tian ◽  
Qing Ming Yi
Keyword(s):  
Genetic Algorithm ◽  
Initial Population ◽  
Signal Acquisition ◽  
Improved Genetic Algorithm ◽  
Search Range ◽  
Acquisition Performance ◽  
Signal Parameters ◽  
Dynamic Search ◽  
Gps Signal Acquisition ◽  
Crossover And Mutation

In order to solve the problem of GPS signal acquisition, an acquisition algorithm based on an improved genetic algorithm (IGA) is proposed in this paper. The IGA employs the technologies including the dynamic search range of parameter, the adaptive probabilities of crossover and mutation, and the small section method for generation of an initial population to search Doppler shift and code phase. Simulations and experiment results show that the proposed method can acquire the signal parameters precisely and rapidly. Consequently, the acquisition performance is improved.

scholarly journals Counterexample Generation for Probabilistic Model Checking Micro-Scale Cyber-Physical Systems

Micromachines ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 1059
Author(s):  
Yang Liu ◽  
Yan Ma ◽  
Yongsheng Yang ◽  
Tingting Zheng
Keyword(s):  
Genetic Algorithm ◽  
Model Checking ◽  
Probabilistic Model ◽  
Cyber Physical Systems ◽  
Probabilistic Model Checking ◽  
Dynamic Power Management ◽  
Search Range ◽  
Physical Systems ◽  
Micro Scale ◽  
Markov Decision

Micro-scale Cyber-Physical Systems (MCPSs) can be automatically and formally estimated by probabilistic model checking, on the level of system model MDPs (Markov Decision Processes) against desired requirements in PCTL (Probabilistic Computation Tree Logic). The counterexamples in probabilistic model checking are witnesses of requirements violation, which can provide the meaningful information for debugging, control, and synthesis of MCPSs. Solving the smallest counterexample for probabilistic model checking MDP has been proven to be an NPC (Non-deterministic Polynomial complete) problem. Although some heuristic methods are designed for this, it is usually difficult to fix the heuristic functions. In this paper, the Genetic algorithm optimized with heuristic, i.e., the heuristic Genetic algorithm, is firstly proposed to generate a counterexample for the probabilistic model checking MDP model of MCPSs. The diagnostic subgraph serves as a compact counterexample, and diagnostic paths of MDP constitute an AND/OR tree for constructing a diagnostic subgraph. Indirect path coding of the Genetic algorithm is used to extend the search range of the state space, and a heuristic crossover operator is used to generate more effective diagnostic paths. A prototype tool based on the probabilistic model checker PAT is developed, and some cases (dynamic power management and some communication protocols) are used to illustrate its feasibility and efficiency.

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