Dissertation > Excellent graduate degree dissertation topics show

Researches on Improved Genetic Algorithm Base on Reinforcement Learning

Author: WangXiaoYan
Tutor: LiuQuan
School: Suzhou University
Course: Applied Computer Technology
Keywords: genetic algorithm reinforcement learning elitist strategy coevolution population diversity
CLC: TP18
Type: Master's thesis
Year: 2011
Downloads: 104
Quote: 0
Read: Download Dissertation

Abstract


Genetic algorithm, which is the earliest, most influential and widely applied, is a branch of Evolutionary Algorithms. As the global optimization and probability searching method, it has many advantages such as simplicity, robustness and commonality. Since it was proposed, it has been applied to engineering, machine learning, pattern recognition, image processing and other aspects. Despite all this, there are many problems should be perfect urgently in theory and application methods, such as premature convergence and slow convergence.In this thesis, some improved methods are proposed to avoid the premature convergence and low speed of convergence. The main research results are concluded as follows:i. A new multiple policy selection genetic algorithm based on reinforcement learning is proposed. The algorithm improves population diversity by using several different selection policies, which can avoid the premature convergence effectively. And population diversity is adjusted by reinforcement learning, which can maintain the population diversity in the appropriate range and enhance the self-adaptive capability to some extent. The experiments show that the algorithm has a high performance in the speed of convergence and search efficiency.ii. Based on the elite strategy and the concept of coevolution, a new double elite coevolutionary genetic algorithm is proposed. And the convergence of the algorithm is proved theoretically. Theoretical analysis proves that the algorithm converge to the global optimization solution. Tests on the functions show that the algorithm can find the global optimal solution for the most test functions, and it can also maintain the population diversity to a certain range. Compared with the existing algorithms, the double elite coevolutionary genetic algorithm has a higher performance in the speed of convergence and search efficiency.iii. In order to improve the self-evolution mechanism of double elite coevolutionary genetic algorithm further and enhance the reliability and self-adaptive capability of the algorithm, reinforcement learning algorithm is applied into double elite coevolutionary genetic algorithm. The experiments show that the combination between reinforcement learning algorithm and genetic algorithm can effectively overcome the randomness of genetic algorithm itself. And it provides a guideline of the combination between genetic algorithm and other reinforcement learning methods.

Related Dissertations

  1. Development of the Platform for Compressor Optimization Design and Aerodynamic Optimization Design in the Transonic Compressor,TH45
  2. Research on Cooperative Orbit Determination in Satellite Network Based on Multi-Agent System Theory,V474
  3. The Application of Fuzzy Comprehensive Evaluation Based on Genetic Algorithm in Vocational Evaluation of Classroom Teaching,G712
  4. Study on Taste Characteristic of Taste Peptide Enzymatic Production from Oyster Base on A Neural Network Method,TS254.4
  5. The Role of Media in the Rural Daily Life,D422.7
  6. Design and Realization of the Magnetic Antenna in MW and SW Bands Based on Genetic Algorithm,TN820
  7. Citrus Image Segmentation Based on Genetic Algorithm,TP391.41
  8. Research of Scheduling Algorithm Based on Hybrid Adaptive Genetic Algorithm in Computing Grid,TP393.09
  9. Public Transport Optimal Dispatching Based on the Genetic-Newton Algorithm,TP18
  10. BP network optimization based on genetic algorithm optimization of the biodiesel process,TE667
  11. The Research on Texture Synthesis Technology from Cloud Theory & Been Evolution Genetic Algorithm,TP391.41
  12. Research on Clustering Algorithm Based on Genetic Algorithm and Rough Set Theory,TP18
  13. Mining resources based on genetic algorithm optimization model of,O224
  14. The magnetorheological damper mechanical properties and Gun Recoil,TB535.1
  15. Optimization Study on Gating System and Molding Process Parameters of Injection Mold Based on Simulation,TQ320.662
  16. Research on the Milling Performance and Parameters Optimization with Large Parts of Heavy Machine,TG54
  17. Research of Adaptive Active Noise Control Based on Neural Network,TP183
  18. The Design and Implementation of Email Analysis and Forensies System,D918.2
  19. Sdesign and Implementation of Course Scheduling Management System,TP311.52
  20. Sentence Similarity Computing Research and Application of Intelligent Question Answering System,TP391.1

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