Dissertation > Excellent graduate degree dissertation topics show

The Improvement of Genetic Algorithm and It’s Application on Knapsack Problem and Function Optimization

Author: XieLong
Tutor: MaFengNing
School: Tianjin University
Course: Information Management and Information Systems
Keywords: Genetic Algorithms Gene property reserved Simple groups Knapsack problem Function optimization
CLC: TP18
Type: Master's thesis
Year: 2010
Downloads: 124
Quote: 0
Read: Download Dissertation

Abstract


Genetic algorithm is an effective way to solve the discrete and continuous problem , effective in the study of several genetic algorithm for knapsack problem and function optimization problems based on gene attributes preserved genetic algorithm Arga ( Attribute Gene - reserved Algorithm ) property differences of every gene in the different generations of genetic be retained elitist method , a good solution to advance convergence , GA deceptive problem . Finally, and through a lot of the classic test case to verify the efficiency of the algorithm . The methods and conclusions of this study are as follows : 1, the proposed concept of genes property reserved , the use of the genetic properties retention policy attribute differences of every gene in the different generations of genetic be retained , and its essence is a correction to the chromosome coding process that aims to to avoid by chromosome evolution , due to the lack of difference can not continue to evolve , so that a good solution to GA deceive . 2, the concept of simple groups , able to make such groups remain non- redundant , and thus a better solution of the GA premature . 3 , the improved elite retention policy , which not only makes the best individual in each generation have been retained , and ensure that the evolution of each generation , each individual fitness of the offspring are not less than the parent individuals fitness . In addition to the genetic operators involved in the genetic algorithm to innovative improvements , the author doing heavy knapsack problem empirical calculation process based on the knapsack problem genetic algorithm evolution algebra solution results the impact is greater than the conclusion of the population size , and quantitatively , given the specific values ??of each parameter , the number of objects that backpack n desirable initial group of 2n to 4n number of evolution . This is a very efficient genetic algorithm parameters .

Related Dissertations

  1. Large the Hongshan iron ore mine personnel tracking positioning system optimization study,TN929.5
  2. Development of the on-line Training and Examination System of Army,TP311.52
  3. Designs and Applications of Fuzzy Synthetic Evaluation Models Based on Parallel Algorithms,TP18
  4. Based on Genetic Algorithm Pishihang irrigation canal water allocation marshalling model of,S274
  5. Genetic Algorithm in logistics and warehousing Optimization Research,F259.2
  6. Mining resources based on genetic algorithm optimization model of,O224
  7. The Research and Application of Modified Algorithms About Fuzzy Predictive Functional Control,TP273
  8. Optimal Control of Emulsion System in Cold Rolling,TP273
  9. Research on the Marshalling-scheduling Model and Algorithms of Freight Trains Based on Game Theory,O225
  10. Research on Combinatorial Optimization Problem Based on DNA Self-Assemble,TP399-C8
  11. Multi-directional Mutation Genetic Algorithm and Research on Neural Network Optimization,TP18
  12. The Application of Using Genetic Algorithms on Universities Course-arranging System,TP18
  13. Research on Mobile Robot Path Planning and Simulation Realization,TP242
  14. Research on Routing Algorithmin Sensor Networks Based on Cluster with Mobile Sink,TP212.9
  15. Research and Implement of the Theme Crawler for Automotive Industry,TP391.3
  16. The Research and Implement on Camera Calibration Technology Based on Trifocal Tensor,TP391.41
  17. Decision Support System of Vehicle Scheduling in Double Level Garage,TP242
  18. The Studies on Some Improvements of the GA and Their Applications in SVM,TP18
  19. A Reduction Method for Artificial Neural Network Inputs Based on An Improved Genetic Algorithm,TP18
  20. Optimal Riser Design of Steel Casting Based on CAE Analysis,TG260
  21. In-furnace Temperature Information Included Combustion Optimization of a Utility Boiler,TK227.1

CLC: > Industrial Technology > Automation technology,computer technology > Automated basic theory > Artificial intelligence theory
© 2012 www.DissertationTopic.Net  Mobile