Theory And Applications Study On Multi-mutation Meme-gene Co-evolution Algorithm

Genetic algorithm (GA), evolved from the biology's evolution rule, is a kind of random search method which adopts probability transfer rule. Since it has great potential in solving complexity and optimization, it has been extensively applied to engineering and technological field recently. At the same time, it results many improved genetic algorithms. The improved GA make a good effect when dealing with the different issue such as improved operator, blend strategy, and structure code as well. Even though the GA always has strong effect in special field, the default that lack the support of theory and the robotic and contrast restricts the GA's application greatly.In order to improve search efficiency and robotic and make full use of the production of GA and its intrinsic combination advantage, the paper combines the social biology and the GA. It not only brings forward meme-gene co-evolution strategy, but also constructs a new integrated co-evolution algorithm which comes from society evolvement and culture spreading theory and which has self-adaptive,self-organized structure strategy. The paper analyzes the co-evolution algorithms in details. And it is selectively applied to three issues in different field, and it gains some foundation and originality production.The paper comes down to several aspects:1,Basing on social biology's culture theory, it analyses meme evolvement and gene evolvement's basic character. And it integrates them to be operable math concept and brings forward meme classification structure and gene-culture information structure. Then, it comes out the meme evolution operators(copy,infection,revival) and constructs culture evolution strategy. Finally, it composes multi-mutation meme-gene co-evolution algorithm.2,As to CEOP issue, it describes a simple basic structure which can makes co-evolution algorithm and proves the complete convergence of algorithm. Based on the concept of guideline entropy, it draws several conclusions by analyzing the colony diversity of algorithm. Finally, it analyses the time complexity of the algorithm and researches how it is influenced by culture operator.3,The paper presents the evaluation standard of the GA's application capability. Basing on characters such as continuity, multi-peak, vibration, randomicity as well as large-scale, five functions are selected to test search ability and robustcity of co-evolution algorithm. Finally, it analyses the simulation result and researches the influence of algorithm brought by culture operators.4,Basing on the concept of collection overcast, it researches the task distribution issue and constitutes delaminated math model on task distribution issue. It puts out the co-evolution algorithm of subtask's decomposing. The experiment compare IGA,SGA to CN and validates the efficiency of co-evolution algorithm on the NP completeness issue.5,Being aim at the optimization issue of load of antenna near ground, it combines many GA strategies and puts forward strategic meme. And it puts out co-evolution algorithm of load of antenna design. And it emulates the optimization design of load of antenna near ground. Finally, it valuates the co-evolution algorithm's efficiency on the continuum search issues of multi-variable and multi-peak value.6,Being aiming at the knowledge of image model matching, it adopts single meme and real code. It puts out fast co-evolution matching algorithm strategy. Basing on NPROD resemble measurement, I emulate the indiscrimination model matching and discrimination model matching. Finally, it valuates the co-evolution algorithm's efficiency on the real code and real time search issue.The paper indicates:Multi-subject crossover can develop and improve methods on the technology application. And it can produce a new theory research direction.