Mind evolutionary computation for a kind of non-numerical optimization problems

Mind evolutionary computation for a kind of non-numerical optimization problems is introduced, which offers a new all-purpose method for solving the non-numerical optimization. First an all-purpose coding method is induced according to the common characteristics of these problems. Then a series of concepts, for example character, information matrix, etc., are introduced. Consequently an all-purpose similartaxis and dissimilation operations of mind evolutionary computation for those problems are designed. In similartaxis operation, information matrix memorizes the characters of the superior individuals and new individuals are generated under the instruction of the characters of these superior individuals, which not only increases the useful information, but also strengthens the evolution direction. Dissimilation operation is for global search, which makes the algorithm have global convergence. Finally its global convergence is proved with combinatorial theory and Markov chain. Our experiments show that this algorithm is feasible and effective.