Research On The Genetic Algorithm In Computer Aided Sofa Design And Its Application

The researches on artificial neural networks, cellular automata and evolutionarycomputation characterized by self-organizing and self-learning and adaptation, rootingfrom simulating some natural phenomenon or the process have been developedgreatly as the science and technology entering the time when multi-disciplinesintercross, seeps mutually and affects mutually.Genetic Algorithm (GA) is widely used and efficient random search optimizationmethods, which premised on the principle of the evolution theory.. In various studiesabout Computational Intelligence, this algorithm is concerned widely. This algorithmwhich is inspirited by the Darwin's evolution theory and the Mendel's Genetics,simulates processes in natural system that follow the laws of the Genetic Species andthe principle of the natural selection and survival of the fittest.The evolutionary computation technique had been shown great potential andfar-reaching impact on design optimization, but it is far from realizing a system ofmatching the human performance. In particular, in some innovation designapplications fields, such as construction, art, music, design, it is difficult to evaluatethe fitness because the measure depends mainly on the human mind. How the. systemcan provide the evolution opportunity for the individual at the same time maximumavoid degenerating in the execution optimization process and may realize theintelligent fitness function have become the hot spot in the research oncomputer-aided conceptual design.Artificial neural networks(ANN) and genetic algorithm(GA) based on biology havebeen used widely because of their superiority.To solve the related questions, there has been specific contribution in this article asfollows:1. This paper is devoted to GA. First, it analyzes the basic theorems of GA, the.improvement of GA,. design method of GA and analyzes astringency of GA, andpresents a uniform standard for astringency, then a kind of GA based on tree structureis reviewed. Finally, it discusses the progress of GA in CAD. followed by the analysisof the main problems in this researching field.2. Analyze the basic theorems, characteristics, and the classification of RBFNN.First, introduce, the concept of RBFNN. RBFNN which has drawn much attention due to its simple structure, good generalization ability and fast convergence. And it hasbeen successfully used in many fields, such as system identification, informationprocessing, pattern recognition, intellectual control and data mining.3. Research on the ANN and GA which are tied in wedlock.By analyzing the biological development model in the ecosystem,the paper hasemphasized biological evolution combined congenital/hereditary feature andcompetitive learning. And based on the traditional genetic algorithm, we use themethod of ANN and GA which are tied in wedlock. Select GA finding rough optimumvalue area as initial value of ANN, and it is trained by ANN, The fitness function ofGA is approximated by artificial neural network. Problem expressing of fitnessfunction is solved. The new coevolutionary algorithm not only strengthened capacityof intelligent search but also accelerated the convergency of the population.4. The application-test of the. new coevolutionary algorithm in the computer-aidedconceptual design(CACD)The paper has studied the application of the newcoevolutionary algorithm, in the computer-aided conceptual design(CACD) based oncombination Principle. and case-based reasoning mechanism. And the system hasshowed many construction examples of ingenious idea and innovative shape. Finally,the experiment results showed the new algorithm has higher-performancecharacteristic than traditional genetic algorithm.A study is conducted on coevolutionary technique and its application incomputer-aided conceptual design (CACD)in this paper. It is hoped that it couldpromoted the development of the relate research fields.