Adaptive Co-evolution Model And Its Application

Co-evolution algorithm has been developed as a kind of new evolution algorithm extensivelyon the base of co-evolution theory for a decade. The difference between co-evolution algorithmand traditional evolution algorithm is that co-evolution algorithm takes the adjustment ofpopulations as well as that of populations and environment into account in evolving process. Forthe advantages of co-evolution algorithm, more and more scholars contribute to this field, whichmakes it a hotspot in evolution computation.Because of the ever sharpness of the market competition, the expenditure idea has changed alot. People no longer treat product function as the main factor of whether to buy something ornot. Much attention has been paid to the attributes of creativity, outline, delightfulness as well asenvironment protection, which stand the important roll in competition. Facing this reality, manyenterprises begin to realize the importance of product design and pay more attention to creativeproducts. Creative design can satisfy the novel and ever-changing market demands and help tomake the product more competitive effectively. Thus, creative design has been a popularresearch item attracts many researchers in relevant fields home and abroad.By far, no systematic method can direct designers to carry out creative design orderly. In fact,most of the instances are created by modifying the products already existed rather than designwithout any foundation. The rise of the evolution computation provides us a new approach forconceptual creative design;therefore, it has become one of the most important techniques forcomputational and creative design.Much attention has been paid to the construction and application of an adaptive co-evolutionmodel in this paper. So that it can extend evolution design from single population tomulti-populations and provide a supporting platform of collaboration and creation for distributeddesigners by studying a new co-evolution mechanism especially for creative design. The mainwork is as follows:1. Propose an adaptive co-evolution mechanism based on learning.Traditional Adaptive Genetic Algorithms pay too much attention to the adaptability of theirevolution parameters, while the complexity of choosing parameters is directly depends on theobject function. The variety of adaptive styles in nature and the biology theory of co-evolutionenlighten us to bring forward the adaptive co-evolution mechanism. Three adaptive learningoperators are introduced and two adaptive processes of learning and evolution are combined inthis mechanism, with which the performance is improved effectively in experiments.2. Put forward a best-matching selection mechanism for designCooperative Co-evolution Algorithms show some randomicity and rely on the speciality ofgiven problems in choosing collaborating individuals. The best-matching selection mechanismespecially for creative design is put forward in this paper. It can provide effective informationfor composing of a complete solution;thus, enhance the intelligence level in design process.3. Construct an adaptive co-evolution model supporting creative designIn view of the uniqueness of creative design, an adaptive co-evolution model is constructedon the base of previous mechanisms. With the best-matching selection mechanism utilized, aseries of operators as adaptive learning and individual evaluation are designed. It affords thefeasibility of using design experience automatically and effectively. Also it shows theimpartibility of the artistic attributes as well as the natural adaptability of a design works via theadaptive operation.4. Implement an architectural creative design system based on the co-evolution modelThe adaptive co-evolution model is applied to architectural design field and a design system,on the base of the model, is realized. With HOOPS/NET adopted as the main skeleton, andentity modeling engine ACIS utilized as the sculpt kernel, the system is designed onWindowsXP platform with VC++.NET 2003. In addition, SQL Server 2000 is used as databasesystem. Finally, the analysis of instances shows that the design results are quite satisfying whichis based on adaptive leaning among populations and that of populations and environment.