An efficient design for a multi-objective evolutionary algorithm to generate DNA libraries suitable for computation
|J. Chaves-González, J. Martinez-Gil. An efficient design for a multi-objective evolutionary algorithm to generate DNA libraries suitable for computation. Interdisciplinary Sciences: Computational Life Science, pages online first, DOI https://doi.org/10.1007/s12539-018-0303-6, 8, 2019.|
|Journal||Interdisciplinary Sciences: Computational Life Science|
The design of reliable DNA libraries that can be used for bio-molecular computing involves several heterogeneous conflicting design criteria that traditional optimization approaches do not fit properly. As it is well known, evolutionary algorithms are very appropriate for solving complex NP-hard optimization problems. However, these approaches take significant computational resources when large instances of complex problems are managed. This is the case for the design of DNA libraries suitable for computation, which involves a set of conflicting design criteria that have to be simultaneously optimized. The problem tackled in this paper involves four objectives and two constraints which are managed at the same time by a tested multi-objective evolutionary algorithm (MOEA) with thousands of individuals in the population. In this context, every computational approach would take several hours of execution time to generate high-quality DNA strands. In this paper, we present an analysis of the parallel MOEA which has been efficiently parallelized with the aim of generating reliable sets of DNA sequences. The results obtained in the study presented here show that the parallel approach is computationally very efficient and that the DNA libraries are highly reliable for computation.