Concerning the problems that the traditional Cartesian Genetic Programming (CGP) is lack of diversity of mutation operation and the evolutionary strategy used in it has limitations, an ADvanced Real-Valued Cartesian Genetic Programming algorithm based on quasi-oppositional mutation (AD-RVCGP) was proposed. Firstly, the 1+lambda evolutionary strategy was adopted in the evolution process in AD-RVCGP just like in the traditional CGP, that is lambda offsprings were generated by a parent only through mutation operation. Secondly, three mutation operators including quasi-oppositional mutation operator, terminal mutation operator and single-point mutation operator were dynamically selected in the process of evolution, and the information of oppositional individuals was used for the mutation operation. Finally, in the evolution process, different parents were selected in the algorithm to generate the next generation individuals according to the state of evolution stage. In the test of symbolic regression problem, the convergence speed of the proposed AD-RVCGP was about 30% faster than that of the traditional CGP, and the running time was about 20% less. In addition, the error between the optimal solution obtained by AD-RVCGP and the real optimal solution was smaller than the optimal solution obtained by the traditional CGP and the real optimal solution. Experimental results show that the proposed AD-RVCGP has high convergence speed and precision for solving problem.

Aiming at the problem that constrained differential evolution with single constraint handing technique is not suitable for all constrained optimization problems, a parallel constrained differential evolution algorithm using multi-constraint handing techniques was proposed. The algorithm divided an initial population into several sub-populations, and then the sub-populations evolved with different constraint handing techniques in parallel, they communicated with each other at fitness evaluation. By using four constraint handing techniques, the algorithm can find the best known optimization solution and compared with serial algorithm, the computation time is 1/4 while solving all benchmark functions. The experimental results show that the propsed algorithm is able to decrease computation time, and improve solution accuracy and convergence speed in the majority of test cases compared with corresponding serial algorithm and those algorithms which only use one constraint handing technique.