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.

Due to the lack of performance analysis while designing a distributed Evolutionary Algorithm (dEA), the designed algorithm cannot reach the expected speedup. To solve this problem, a comprehensive performance analysis method was proposed. According to the components of dEAs, factors that influence the performance of dEAs can be divided into three parts, namely, evolutionary cost, fitness evaluation cost and communication cost. Firstly, the feature of evolutionary cost under different individual encoding lengths was studied. Then when the evolutionary cost was kept unchanged, the fitness evaluation cost was controlled by using the delay function of the operating system and the communication cost was controlled by changing the length of individual encoding. Finally, the effect of each factor was tested through control variable method. The experimental results reveal the constraint relation among the three factors and point out the necessary conditions for speeding up dEAs.