Abstract : For successful and efficient use of GAs, it is not enough to simply apply simple GAs (SGAs). In addition, it is necessary to find a proper representation for the problem and to integrate linkage information about the problem structure. Similarly, it is important to develop appropriate search operators that fit well to the properties of the genotype encoding and that can learn linkage information to assisst in creating and not in destroying the building blocks. Besides, the representation must at least be able to encode all possible solutions of an optimization problem, and genetic operators such as crossover and mutation should be applicable to it. In this chapter, sequential alternation strategies between two coding schemes are formulated in the framework of dynamic change of genotype encoding in GAs for function optimization. Likewise, new variants of GAs for difficult optimization problems are developed using a parallel implementation of GAs and evolving a dynamic exchange of individual representation in the context of dual coding concepts. Numerical experiments show that the evolved proposals significantly outperform a SGA with static single coding.