scholarly journals Improved Genetic Algorithm Based Classification

2012 ◽  
pp. 211-217
Author(s):  
Keshavamurthy B. N ◽  
Asad Mohammed Khan ◽  
Durga Toshniwal
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithms ◽  
Probabilistic Models ◽  
Fitness Function ◽  
Optimal Solution ◽  
Search Space ◽  
Improved Genetic Algorithm ◽  
Data Set ◽  
Useful Knowledge ◽  
Bayes Algorithm

Classification is the supervised learning technique of data mining which is used to extract hidden useful knowledge over a large volume of databases by predicting the class values based on the predicting attribute values. Of the various techniques, the most widely talked ones include decision tree, probabilistic model and evolutionary algorithms. Recently, the probabilistic and evolutionary techniques are most worked upon, because of the fact that probabilistic models yields high accuracy when there is no attribute dependency in the existing problem and evolutionary algorithms are used to obtain optimal solution over a large search space very quickly when there is less information known about a problem and problem posses attribute dependency. Though there are tradeoffs in each model still there are scopes to improve upon the existing. The proposed approach improves the evolutionary technique such as genetic algorithm by improving the fitness function parameters. Also, in this we compare the genetic algorithm results with Naïve Bayes algorithm results. For the experimental work we have used the nursery data set taken from the UCI Machine Learning Repository.

scholarly journals Application of an Improved Genetic Algorithm for Optimal Design of Planar Steel Frames

2018 ◽  
Author(s):  
Morteza Madhkhan ◽  
Mohammad Reza Baradaran
Keyword(s):  
Genetic Algorithm ◽  
Rate Of Convergence ◽  
Optimal Solution ◽  
Search Space ◽  
Initial Population ◽  
Generation Process ◽  
Improved Genetic Algorithm ◽  
Cross Sectional ◽  
Design Constraints ◽  
Matlab Programming

Genetic Algorithm (GA) is one of the most widely used optimization algorithms. This algorithm consists of five stages, namely population generation, crossover, mutation, evaluation, and selection. This study presents a modified version of GA called Improved Genetic Algorithm (IGA) for the optimization of steel frame designs. In the IGA, the rate of convergence to the optimal solution is increased by splitting the population generation process to two stages. In the first stage, the initial population is generated by random selection of members from among AISC W-shapes. The generated population is then evaluated in another stage, where the member that does not satisfy the design constraints are replaced with stronger members with larger cross sectional area. This process continues until all design constraints are satisfied. Through this process, the initial population will be improved intelligently so that the design constraints fall within the allowed range. For performance evaluation and comparison, the method was used to design and optimize 10-story and 24-story frames based on the LRFD method as per AISC regulations with the finite element method used for frame analysis. Structural analysis, design, and optimization were performed using a program written with MATLAB programming language. The results show that using the proposed method (IGA) for frame optimization reduces the volume of computations and increases the rate of convergence, thus allowing access to frame designs with near-optimal weights in only a few iterations. Using the IGA also limits the search space to the area of acceptable solutions.

scholarly journals Computer Mathematical Modeling Based on the Improved Genetic Algorithm and Mobile Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Huixian Wei ◽  
Jia Liu
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Mobile Computing ◽  
Optimal Solution ◽  
Least Square Method ◽  
Search Space ◽  
Least Square ◽  
Improved Genetic Algorithm ◽  
Operation Rate ◽  
The Individual

In order to change the problem of data redundancy in a genetic algorithm, this paper proposes a computer mathematical model based on the combination of an improved genetic algorithm and mobile computing. Combined with the least square method, MATLAB software is used to solve the equations, determine the range of parameters, and solve the estimation parameter range and identification problems. The improved genetic algorithm combined with mobile computing and least square method to establish a mathematical model greatly increased the individual search space and increased the operation rate of 90% compared to the basic genetic algorithm or mobile computing. The results show that the improved genetic algorithm and mobile computing have a certain ability to identify the optimal solution and greatly improve the work efficiency.

A Route Planning’s Method for Micro UAV Based on Improved Genetic Algorithm

2014 ◽  
Vol 602-605 ◽  
pp. 1348-1351 ◽  
Author(s):  
Ling Lu ◽  
Hua Zhang
Keyword(s):  
Genetic Algorithm ◽  
Environmental Impact ◽  
Penalty Function ◽  
Fitness Function ◽  
Search Space ◽  
Improved Genetic Algorithm ◽  
Path Constraints ◽  
Simulation Results ◽  
Return Path

According to micro UAV is susceptible to wind environmental impact during the flight, this paper puts forward a kind of ergodicity track strategy for return path re planning. By adding coverage flight and sacrificing path to achieve safe recovery of voyage. A genetic algorithm is used to solve the problem, and introducing dynamic penalty function to improve the fitness function, effectively reduce the search space. The simulation results show that the method can generate safely track, and satisfy the path constraints.

Research on Application of Intelligence Schedule of City Pubic Traffic Vehicles Based on Genetic Algorithm

2013 ◽  
Vol 706-708 ◽  
pp. 1902-1906
Author(s):  
Rui Chen ◽  
Liang Fang
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Search Space ◽  
Uniform Grid ◽  
Penalty Function Method ◽  
Improved Genetic Algorithm ◽  
Start Time ◽  
True Value ◽  
Coding Method ◽  
Research On Application

Giving attention to the benefits of the passengers and agency, this paper adopts the true value of the coding method using the start time as the variable and uses the penalty function method to add a variety of constraints to the objective function when constructing the fitness function,which simplifies the calculation. Finally, the simulation results are obtained by using the improved Genetic Algorithm for solving the non-uniform grid schedule. Results show that the improved Genetic Algorithm can find the approximate best result in the huge search space of optimization, and greatly increased the computational efficiency.

Xiangjiaba Reservoir Ecological Operation Preliminary Study Based on Genetic Algorithm

2014 ◽  
Vol 1010-1012 ◽  
pp. 1037-1041
Author(s):  
Min Qin ◽  
Yang Peng ◽  
Xian Liang Yu
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Optimal Solution ◽  
Threshold Value ◽  
Lower Threshold ◽  
Ecological Environment ◽  
Improved Genetic Algorithm ◽  
Environment Impact ◽  
Jinsha River ◽  
Adaptive Crossover

Traditional reservoir scheduling guarantees efficient utilization of water resources and maximizes hydropower station operation efficiency. However it doesn’t consider the surrounding ecological environment demand. Ecological operation which is based on Traditional reservoir scheduling makes the reservoir achieve long-term, sustainable operation and reduce adverse impacts on the ecological environment. Xiangjiaba reservoir is the ties of the downstream of jinsha river and the Yangtze river which is located in jinsha river basin ecological sensitive area. So the completion of the Xiangjiaba reservoir must result in unhealthy ecological environment impact. With traditional genetic algorithm (GA) encoding complexity and being easy to fall into local convergence limitation, put forward a kind of improved convergence of genetic algorithm, the improved genetic algorithm with real number coding and fitness function which is converted into a nonlinear trigonometric function selection operator. Select the adaptive crossover probability and mutation probability adjust, in order to improve the convergence of the algorithm. RVA method is used to calculate the IHA index upper and lower threshold value and the upper and lower threshold could value as the constraint conditions of ecological scheduling objective function model. The results show that the improved genetic algorithm converge makes the global optimal solution ability stronger and faster; RVA method is suitable for the jinshajiang river and results are more comprehensive and more reasonable.

An efficient intelligent decision method for bionic motion unmanned system

2021 ◽  
pp. 095965182110655
Author(s):  
Haipeng Chen ◽  
Wenxing Fu ◽  
Yuze Feng ◽  
Jia Long ◽  
Kang Chen
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithms ◽  
Path Planning ◽  
Motion Control ◽  
Control Method ◽  
Improved Genetic Algorithm ◽  
Decision Method ◽  
Multiple Traveling Salesman Problem ◽  
Intelligent Decision ◽  
Unmanned System

In this article, we propose an efficient intelligent decision method for a bionic motion unmanned system to simulate the formation change during the hunting process of the wolves. Path planning is a burning research focus for the unmanned system to realize the formation change, and some traditional techniques are designed to solve it. The intelligent decision based on evolutionary algorithms is one of the famous path planning approaches. However, time consumption remains to be a problem in the intelligent decisions of the unmanned system. To solve the time-consuming problem, we simplify the multi-objective optimization as the single-objective optimization, which was regarded as a multiple traveling salesman problem in the traditional methods. Besides, we present the improved genetic algorithm instead of evolutionary algorithms to solve the intelligent decision problem. As the unmanned system’s intelligent decision is solved, the bionic motion control, especially collision avoidance when the system moves, should be guaranteed. Accordingly, we project a novel unmanned system bionic motion control of complex nonlinear dynamics. The control method can effectively avoid collision in the process of system motion. Simulation results show that the proposed simplification, improved genetic algorithm, and bionic motion control method are stable and effective.

Solving vehicle routing problem by using improved genetic algorithm for optimal solution

2017 ◽  
Vol 21 ◽  
pp. 255-262 ◽  
Author(s):  
Mazin Abed Mohammed ◽  
Mohd Khanapi Abd Ghani ◽  
Raed Ibraheem Hamed ◽  
Salama A. Mostafa ◽  
Mohd Sharifuddin Ahmad ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Optimal Solution ◽  
Improved Genetic Algorithm ◽  
Routing Problem

Comparison of Multi-Objective Evolutionary Algorithms to Prioritize Regression Test Cases

2021 ◽  
Vol 9 (12) ◽  
pp. 1445-1454
Keyword(s):  
Fault Detection ◽  
Evolutionary Algorithms ◽  
Fitness Function ◽  
Optimal Solution ◽  
Test Suite ◽  
Test Case ◽  
Test Cases ◽  
Nsga Ii ◽  
Multi Objective ◽  
Regression Test

Regression testing is one of the most critical testing activities among software product verification activities. Nevertheless, resources and time constraints could inhibit the execution of a full regression test suite, hence leaving us in confusion on what test cases to run to preserve the high quality of software products. Different techniques can be applied to prioritize test cases in resource-constrained environments, such as manual selection, automated selection, or hybrid approaches. Different Multi-Objective Evolutionary Algorithms (MOEAs) have been used in this domain to find an optimal solution to minimize the cost of executing a regression test suite while obtaining maximum fault detection coverage as if the entire test suite was executed. MOEAs achieve this by selecting set of test cases and determining the order of their execution. In this paper, three Multi Objective Evolutionary Algorithms, namely, NSGA-II, IBEA and MoCell are used to solve test case prioritization problems using the fault detection rate and branch coverage of each test case. The paper intends to find out what’s the most effective algorithm to be used in test cases prioritization problems, and which algorithm is the most efficient one, and finally we examined if changing the fitness function would impose a change in results. Our experiment revealed that NSGA-II is the most effective and efficient MOEA; moreover, we found that changing the fitness function caused a significant reduction in evolution time, although it did not affect the coverage metric.

scholarly journals Prototype Filter Design Based on Channel Estimation for FBMC/OQAM Systems

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Yongjin Liu ◽  
Xihong Chen ◽  
Yu Zhao
Keyword(s):  
Genetic Algorithm ◽  
Channel Estimation ◽  
Optimization Problem ◽  
Filter Design ◽  
Stop Band ◽  
Optimal Solution ◽  
Improved Genetic Algorithm ◽  
Global Optimal Solution ◽  
Prototype Filter ◽  
Global Optimal

A prototype filter design for FBMC/OQAM systems is proposed in this study. The influence of both the channel estimation and the stop-band energy is taken into account in this method. An efficient preamble structure is proposed to improve the performance of channel estimation and save the frequency spectral efficiency. The reciprocal of the signal-to-interference plus noise ratio (RSINR) is derived to measure the influence of the prototype filter on channel estimation. After that, the process of prototype filter design is formulated as an optimization problem with constraint on the RSINR. To accelerate the convergence and obtain global optimal solution, an improved genetic algorithm is proposed. Especially, the History Network and pruning operator are adopted in this improved genetic algorithm. Simulation results demonstrate the validity and efficiency of the prototype filter designed in this study.

scholarly journals Multiobjective Synthesis of Linear Arrays by Using an Improved Genetic Algorithm

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Bo Yang
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Fitness Function ◽  
Particle Swarm ◽  
Weight Vector ◽  
Side Lobe Level ◽  
Improved Genetic Algorithm ◽  
Pattern Synthesis ◽  
Swarm Optimization ◽  
Dynamic Weight

In this paper, an improved genetic algorithm with dynamic weight vector (IGA-DWV) is proposed for the pattern synthesis of a linear array. To maintain the diversity of the selected solution in each generation, the objective function space is divided by the dynamic weight vector, which is uniformly distributed on the Pareto front (PF). The individuals closer to the dynamic weight vector can be chosen to the new population. Binary- and real-coded genetic algorithms (GAs) with a mapping method are implemented for different optimization problems. To reduce the computation complexity, the repeat calculation of the fitness function in each generation is replaced by a precomputed discrete cosine transform matrix. By transforming the array pattern synthesis into a multiobjective optimization problem, the conflict among the side lobe level (SLL), directivity, and nulls can be efficiently addressed. The proposed method is compared with real number particle swarm optimization (RNPSO) and quantized particle swarm optimization (QPSO) as applied in the pattern synthesis of a linear thinned array and a digital phased array. The numerical examples show that IGA-DWV can achieve a high performance with a lower SLL and more accurate nulls.

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