Coevolutionary Model Researches And Applications Supporting Self-studying

Coevolution algorithm is a kind of new evolution algorithm on the base of Coevolution theory. Coevolution algorithm takes the adjustment of populations as well as that of populations and environment into account in evolving process. For the Coevolution algorithm overcomes many disadvantages of traditional evolution algorithm, more and more scholars contribute to this field, which makes it a hotspot in evolution computation for a decade.At the same time, because of the sharpness of the market competition, the expenditure idea has changed a lot. People no longer treat product function as the main factor of whether to buy something or not. Much attention has been paid to the attributes of creativity, outline, delightfulness as well as environment protection, which stand the important roll in competition. Facing this reality, many enterprises begin to realize the importance of product design and pay more attention to creative products. Creative design can satisfy the novel and ever-changing market demands and help to make the product more competitive effectively. Creative design can satisfy the novel and ever-changing market demands and help to make the product more competitive effectively. Thus, creative design has been a popular research item attracts many researchers in relevant fields home and abroad. The computer-aided creative concept design develops too.However, the creative concept design system now existing is lack of creation, supports graphic tools for designers only. It can't product concept individual to inspire designers'thinking according to their ideas, and the excellent experience and prior knowledge can not be preserved in time either.On account of the above considerations, this paper puts forward collaborative evolutionary algorithm supporting self-studying and applies it to creative concept design. So that it can extend evolution design from single population to multi-populations, save the excellent design experience and provide a favorable environment for designers. The main work is as follows:1,Propose a coevolutionary algorithm supporting self-studying.The basic thought stems from biological knowledge. Many communities that populations make up can utilize environmental resources, keep higher productivity, and have stability than single population community lasting effectively in biology. More over, because the gene part hunting zone is big in many populations'evolution course, the change of adaptation natural environment that it can be better regulates oneself. Just because of this, this paper combines the adaptive processes, learning and evolution, puts forward the coevolutionary algorithm supporting self-studying on the base of the theory of biological coevolution. Two adaptive learning operators, individual training operator and population annexation operator, are introduced and guarantee the diversity of the population by population multiplying in this mechanism, with which the performance is improved effectively.2. Construct a creative design model based on the coevolutionary algorithms above.Generative conceptual design intension is extensive and deep, the conceptual design in the general meaning is meant in a situation that the function and principle of the building are definite basically, design process of the architectural appearance modeling. This paper focuses on innovation of building design, evolves coevolutionary model supporting self-studying to creative concept design, and constructs a creative concept design system of building. It not only affords the feasibility of using design experience automatically and effectively, but also embodies the continuity and creativity of the design via the adaptive operation.3. Implement an architectural creative design system based on the coevolution model supporting self-studying.With HOOPS/NET adopted as the main skeleton, and entity modeling engine ACIS utilized as the sculpt kernel, the system is designed on Windows XP platform with VC++.NET 2003. In addition, SQL Server 2000 is used as database system. Finally, the analysis of instances shows that the design results are quite satisfying which is based on adaptive leaning among populations and on the self-studying of population.