The performance of DE basically depends on the mutation strategy, the crossover operator.
In this article we propose an efficient hybrid evolutionary algorithm that embeds the difference vector-based mutation scheme, the crossover and the selection strategy of Differential Evolution (DE) into another recently developed global optimization algorithm known as Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES).
On the other hand, if the A111T mutation preceded the crossover, the intermediate haplotype (C22) would be predicted to experience the same selective pressure as C11.
To study the influence of the mutation or the crossover operator alone, the values for p m and p c are set to zero or one, depending on which influence is investigated.
On ES we use the mutation operators covariance matrix adaptation (CMA), the 1/5th success rule as well as correlated, global, local, and no mutation paired with the crossover operators one- and n-point (n = 3), UNDX, and no crossover, each with p m = 0.8, p c = 0.2.
The parameters of the genetic algorithm, such as the size of the population, the probability to perform mutation or crossover, the elite percentage of the population and the number of the iterations can be directly determined by the user.
The parameters used in the inversion are as follows: both the mutation probability and the crossover probability are 0.2, and the numbers of sample and generation are 100 and 40, respectively.
From the obtained results, it is observed that SaDE algorithm is able to self-adapt the mutation strategy and the crossover rate and hence performs better than the other variants of DE and the constrained PSO algorithm.
The design of self-feedback learning phase seeks the maintaining of good exploitation ability while the introduction of the mutation and crossover phase aims at the improvement of exploration ability in original TLBO.
Moreover, bimodal distribution parameter setting is proposed for the control parameters of the mutation and crossover operators in this paper, with the aim of balancing the exploration and exploitation abilities of DE.
