Constructing Attractors via the Improved Eugenics Genetic Algorithm

2014 ◽  
Vol 989-994 ◽  
pp. 1786-1789
Author(s):  
Li Ming Du ◽  
Feng Ying Wang ◽  
Zi Yang Han
Keyword(s):  
Genetic Algorithm ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Genetic Drift ◽  
Chaotic Attractors ◽  
Parameter Vector ◽  
Distance Limit

The paper introduces Monte Carlo method and Eugenics genetic algorithm, which be used to generate a great diversity of chaotic attractors firstly. By an analysis of their algorithms, a improved eugenics genetic algorithm is presented to avoid the "genetic drift" phenomenon in attractor graphics. A parameter vector distance limit is adopted to solve the problem and lots of experiments applying equivalent mappings of frieze group are finished to validate effectiveness for algorithm.

Calibration of Modular Reconfigurable Robots Based on a Hybrid Search Method

2010 ◽  
Vol 132 (6) ◽  
Author(s):  
Yu Lin ◽  
Fengfeng Xi ◽  
Richard Phillip Mohamed ◽  
Xiao-wei Tu
Keyword(s):  
Genetic Algorithm ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Configuration Space ◽  
Global Solutions ◽  
Search Method ◽  
Hybrid Search ◽  
Reconfigurable Robots ◽  
Genetic Algorithm Method

Developed in this paper is a hybrid method for calibration of modular reconfigurable robots (MRRs). The underlying problem under study is unique to MRRs, that is, how to calibrate a set of MRR’s geometric parameters that are applicable to all feasible configurations. For this reason, a hybrid search method is developed to ensure a global search over the MRRs’ workspace for each feasible configuration. By combining a genetic algorithm method with a Monte Carlo method, this method includes three levels of search, namely, pose, workspace, and configuration-space. The final set of global solutions is generated progressively from the results of these three levels of search. The effectiveness of this method is demonstrated through a case study.

scholarly journals Comparation of nonuniform and uniform Monte - Carlo Searching

2018 ◽  
Vol 244 ◽  
pp. 01016
Author(s):  
Marián Handrik ◽  
Jana Handriková ◽  
Milan Vaško ◽  
Filip Dorčiak
Keyword(s):  
Genetic Algorithm ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Random Number ◽  
Statistical Evaluation ◽  
Random Number Generation ◽  
Number Generation ◽  
Function Mapping

Nonuniform Monte-Carlo method is often used for optimization and solution of function mapping. This method has some disadvantages. New genetic algorithm, based on uniform Monte-Carlo is proposed by authors reduce disadvantage of nonuniform Monte- Carlo method. Both of these methods are based on random number generation and therefore the solutions are approximate. Statistical evaluation of solutions is used for comparison.

scholarly journals Sustainable Water Resource Management of Regulated Rivers under Uncertain Inflow Conditions Using a Noisy Genetic Algorithm

2019 ◽  
Vol 16 (5) ◽  
pp. 868 ◽  
Author(s):  
Chunxue Yu ◽  
Xinan Yin ◽  
Zhifeng Yang ◽  
Zhi Dang
Keyword(s):  
Genetic Algorithm ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Reservoir Operation ◽  
Water Resource Management ◽  
Fitness Function ◽  
Regulated Rivers ◽  
The Monte Carlo Method ◽  
Sustainable Water Resource ◽  
Inflow Conditions

Ecofriendly reservoir operation is an important tool for sustainable water resource management in regulated rivers. Optimization of reservoir operation is potentially affected by the stochastic characteristics of inflows. However, inflow stochastics are not widely incorporated in ecofriendly reservoir operation optimization. The reasons might be that computational cost and unsatisfactory performance are two key issues for reservoir operation under uncertainty inflows, since traditional simulation methods are usually needed to evaluate over many realizations and the results vary between different realizations. To solve this problem, a noisy genetic algorithm (NGA) is adopted in this study. The NGA uses an improved type of fitness function called sampling fitness function to reduce the noise of fitness assessment. Meanwhile, the Monte Carlo method, which is a commonly used approach to handle the stochastic problem, is also adopted here to compare the effectiveness of the NGA. Degree of hydrologic alteration and water supply reliability, are used to indicate satisfaction of environmental flow requirements and human needs. Using the Tanghe Reservoir in China as an example, the results of this study showed that the NGA can be a useful tool for ecofriendly reservoir operation under stochastic inflow conditions. Compared with the Monte Carlo method, the NGA reduces ~90% of the computational time and obtains higher water supply reliability in the optimization.

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