Chaos Genetic Algorithm And The Application For Image Matching

Chaos Genetic Algorithm (CGA) is an algorithm based on Genetic Algorithm (GA) andChaotic Algorithm. It not onlyhas the advantages of parallelism, global Optimization of GA,but also has ergodicity, initial-value sensitivity, order inter-structure, predictability of ChaoticAlgorithm.CGAcan traverse each solution of a limited solution problem; CGAcan be closertoglobalsolutionindirectionofproblemoftheinfinitesolutionproblem.The paper introduces the basic concepts of GA and Chaotic theory, analysis the GA'sshortcomingsandtheimportantcharacteristicsofChaotictheory; Secondly,importantlystudyCGA based on chaotic selection strategy,regularity cross operation,regularity mutationoperation, improve control parameters,initial population and termination conditions; Again,the experiment of discrete two-dimensional knapsack problems and continuousGoldstein_Price function, tests the performance of CGA, which compares to GAand generalChaotic Algorithm. The results show that the performance of CGA is better than GA andgeneral Chaotic Algorithm; Finally, CGA is used in the non-compressed grayscale imagematching and compressed color image matching, and carries out the traditional no differenceimagematchingandthedifferenceimagematching.TheresultsofexperimentsshowthatthisCGA,theeachgroupoptimalsolutionofwhichisgraduallyapproachingtotheglobaloptimalsolution,hashighernumericalstability,quickerconvergence rate, higher solution accuracy comparing to GAand general Chaotic Algorithm.CGA avoids the randomness to the impact of CGA, which allows researchers to track theentire search process, find the problem and research the problem. The exploration of thispaperwillbebeneficialforthespreadofgeneticalgorithm.