Mind Evolutionary Computation For A Kind Of Non-numerical Optimization Problems

Mind Evolutionary Computation(MEC) was proposed by simulating the processes of human mind. It is a new potential evolutionary algorithm. MEC has been applied to numerical optimization problems, and some non-numerical optimization problems, for example traveling salesman problem, job-shop scheduling, and Modeling for Systems of Ordinary Differential Equations, are solved successfully with MEC. But the all-purpose algorithm of MEC for non-numerical problems doesn't exist.In this paper, MEC algorithm is introduced for a kind of non-numeric optimization problems which solution space is limit. First an all-purpose coding method is induced according to the common characteristics of those problems. Then a series of concepts ,for example character ,information matrix,etc,are introduced. So an all-purpose similartaxis and dissimilation operations of MEC for those problems are designed. Consequently MEC algorithm for a kind of non-numeric optimization problems is introduced and its global convergence is proved with combinatorial theory and Markov chain. We solve vertex coloring problem and job-shop scheduling with this algorithm. Our experiments show that this algorithm is feasible and effective. This algorithm is all-purpose and it is fitted for traveling salesman problem, job-shop scheduling, vertex coloring problem, the optimization of the artificial neural network architecture and Modeling for Systems, etc. When we solve a non-numerical problem with this algorithm, if this problem is converted reasonably and the character and information matrix of this problem are defined, then this algorithm can work. The MEC algorithm offers a new all-purpose and effective method for a kind of non-numerical problems.