Molecular programming: evolving genetic programs in a test tube
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Multiobjective Optimization in Bioinformatics and Computational Biology
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Hardware acceleration of multi-deme genetic algorithm for the application of DNA codeword searching
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Expert Systems with Applications: An International Journal
A hybrid quantum chaotic swarm evolutionary algorithm for DNA encoding
Computers & Mathematics with Applications
Application of a novel IWO to the design of encoding sequences for DNA computing
Computers & Mathematics with Applications
Improved Quantum Evolutionary Algorithm Combined with Chaos and Its Application
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
Implementation of Binary Particle Swarm Optimization for DNA Sequence Design
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part II: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living
A multi-threaded DNA tag/anti-tag library generator for multi-core platforms
CIBCB'09 Proceedings of the 6th Annual IEEE conference on Computational Intelligence in Bioinformatics and Computational Biology
An Ant Colony System for DNA sequence design based on thermodynamics
ACST '08 Proceedings of the Fourth IASTED International Conference on Advances in Computer Science and Technology
Generating DNA code word for DNA computing with realtime PCR
ACST '08 Proceedings of the Fourth IASTED International Conference on Advances in Computer Science and Technology
EvoOligo: oligonucleotide probe design with multiobjective evolutionary algorithms
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Dinucleotide step parameterization of pre-miRNAs using multi-objective evolutionary algorithms
EvoBIO'07 Proceedings of the 5th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
Multiplex PCR assay design by hybrid multiobjective evolutionary algorithm
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Evolutionary model for sequence generation
ICIC'07 Proceedings of the intelligent computing 3rd international conference on Advanced intelligent computing theories and applications
DNA13'07 Proceedings of the 13th international conference on DNA computing
Microarray probe design using ε-multi-objective evolutionary algorithms with thermodynamic criteria
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
ICIC'06 Proceedings of the 2006 international conference on Computational Intelligence and Bioinformatics - Volume Part III
A bayesian algorithm for in vitro molecular evolution of pattern classifiers
DNA'04 Proceedings of the 10th international conference on DNA computing
Bacterially inspired evolving system with an application to time series prediction
Applied Soft Computing
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Engineering Applications of Artificial Intelligence
Improving the design of sequences for DNA computing: A multiobjective evolutionary approach
Applied Soft Computing
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DNA computing relies on biochemical reactions of DNA molecules and may result in incorrect or undesirable computations. Therefore, much work has focused on designing the DNA sequences to make the molecular computation more reliable. Sequence design involves with a number of heterogeneous and conflicting design criteria and traditional optimization methods may face difficulties. In this paper, we formulate the DNA sequence design as a multiobjective optimization problem and solve it using a constrained multiobjective evolutionary algorithm (EA). The method is implemented into the DNA sequence design system, NACST/Seq, with a suite of sequence-analysis tools to help choose the best solutions among many alternatives. The performance of NACST/Seq is compared with other sequence design methods, and analyzed on a traveling salesman problem solved by bio-lab experiments. Our experimental results show that the evolutionary sequence design by NACST/Seq outperforms in its reliability the existing sequence design techniques such as conventional EAs, simulated annealing, and specialized heuristic methods.