The gene expression process in nature produces different proteins in different cells from different portions of the DNA. Since proteins control almost every important activity in a living organism, at an abstract level, gene expression can be viewed as a process that evaluates the merit or “fitness” of the DNA. This distributed evaluation of the DNA would not be possible without a decomposed representation of the fitness function defined over the DNAs. This paper argues that, unless the living body was provided with such a representation, we have every reason to believe that it must have an efficient mechanism to construct this distributed representation. This paper demonstrates polynomial-time computability of such a representation by proposing a class of efficient algorithms. The main contribution of this paper is two-fold. On the algorithmic side, it offers a way to scale up evolutionary search by detecting the underlying structure of the search space. On the biological side, it proves that the distributed representation of the evolutionary fitness function in gene expression can be computed in polynomial-time. It advances our understanding about the representation construction in gene expression from the perspective of computing. It also presents experimental results support-ing the theoretical performance of the proposed algorithms.