Furthermore, we applied the effective evolutionary model to a non-redundant set of globular protein structures contained in the Protein Data Bank (PDB), using several mutation schemes of increasing complexity.
The values of the parameters of the evolutionary algorithm (population size, crossover rate, mutation scheme, and number of generations) have been found to obtain a quite good tradeoff between design time and performance for the experiments carried out in this paper.
To perform comprehensive evaluation and to assess the effectiveness of the proposed self-adaptive crossover rate scheme and new mutation scheme, another version of EADE, named EADE*, has been tested and compared against EADE and other DE-based algorithms.
The proposed software named PDoublePop implements a client server model for parallel genetic algorithms with advanced features for the local genetic algorithms such as: an enhanced stopping rule, an advanced mutation scheme and periodical application of a local search procedure.
In this paper, a kind of discrete artificial bee colony with composite mutation strategies is presented to compensate the defects of the single mutation scheme that is easy to get into the local best for PFSSP, named CDABC.
In the mutation scheme, some randomly selected sites (with given probability) of the new set of the binary model population are switched.
Comparison with AFEP without p-best mutation demonstrates the improvement of performance due to the proposed mutation scheme alone.
Obviously, from mutation Eq. (11), it can be observed that the incorporation of the objective function value in the mutation scheme has two benefits.
This new mutation scheme helps to maintain effectively the balance between the global exploration and local exploitation abilities for searching process of the DE.
Secondly, execute mutation scheme.
