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The Research of Genetic Algorithm Parameter Setting Based on Inductive Learning and Case-Based Reasoning

Author: LiZuo
Tutor: CuiDuWu
School: Xi'an University of Technology
Course: Computer Software and Theory
Keywords: Inductive Learning Case-based Reasoning Decision Tree Learning Evolutionary Computation Genetic Algorithm
CLC: TP18
Type: Master's thesis
Year: 2009
Downloads: 34
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Abstract


In nature, all kinds of creatures have good adaptability to their living environment.In order to evolve endlessly, various species live in a competitive environment that is so called survival of the fittest. Evolutionary computation is the simulation of organic evolution in nature.Human being,the leader of other creatures, have the outstanding ability to learn.They can gain knowledge from learning,and record and utilize them completely.Machine learning, is to make the computer can simulate the learning behavior of human being, automatically by learning to acquire knowledge and skills, and constantly improve the performance and achieve self-improvement. Inductive Learning and Case-Based Reasoning are mature technologies in the development of machine learning, and have been widely studied and applied.The job that simulating human evolution mechanism and combining the traditional evolutionary computation and machine learning, can be a distinct way to conduct an evolutionary computation system whit higher intelligence level. There is detailed description and analysis about this combination in the paper.This thesis firstly discusses the research situation of Inductive Learning. And it introduces some decision-tree algorithms of Inductive Learning. Then it also introduces some basic techniques of CBR such as case representation, case retrieval, case revisal, and case base maintenance. Expounds the application fields and previous improvement methods of genetic algorithm as an important branch of evolutionary computation in detail, and finally introduces De Jong’s research work in function optimization.According to these theories mentioned above, the thesis raises some new application of Inductive Learning in CBR. Some algorithms of Decision tree learning are applied to case retrieval, case revisal and case base maintenance. Finally, combined with the application of Genetic Algorithm in Function Optimization, the thesis has brought forward an integrated framework of Inductive Learning and CBR, presented Genetic Algorithm research model based on Inductive Learning and CBR, which can be used in choosing algorithm type and setting parameter in the design of Genetic Algorithm, presented integrated architecture of the model and key techniques.

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