A novel evolutionary algorithm solving optimization problems

This paper develops an novel evolutionary algorithm, I Ching algorithm (ICA) for solving optimization problems. The new algorithm employs an novel method by implying new operators from I Ching, which comes from ancient Chinese culture. There are some transformation methods such as a penalty method and a multiplier method. The penalty method is often used to solve optimization problems, because the solutions are often near the boundary of the feasible set and the method is used easily for its simplicity. In design the ICA, three operators - mutation operator, turnover operator, and mutual operator were developed by the authors based on the concept of I Ching transformations. These new operators are very flexible and search on the designed I Ching network in the evolution procedure. The proposed algorithm was applied to solving two optimization benchmark functions, Booth function and Hump function. Then, we compare the performance of ICA with genetic algorithm. The experimental results show that our proposed I Ching algorithm performs better than genetic algorithm in reaching the global optimum. It is much faster than those of genetic algorithms. Additionally, the ICA is also a universal method, which is suitable to different optimization problems.