Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
Strand design for biomolecular computation
Theoretical Computer Science - Natural computing
DNA sequence design using templates
New Generation Computing
PUNCH: An Evolutionary Algorithm for Optimizing Bit Set Selection
DNA 7 Revised Papers from the 7th International Workshop on DNA-Based Computers: DNA Computing
DNASequencesGenerator: A Program for the Construction of DNA Sequences
DNA 7 Revised Papers from the 7th International Workshop on DNA-Based Computers: DNA Computing
Developing Support System for Sequence Design in DNA Computing
DNA 7 Revised Papers from the 7th International Workshop on DNA-Based Computers: DNA Computing
Controlled Elitist Non-dominated Sorting Genetic Algorithms for Better Convergence
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
A PCR-based Protocol for In Vitro Selection of Non-crosshybridizing Oligonucleotides
DNA8 Revised Papers from the 8th International Workshop on DNA Based Computers: DNA Computing
From RNA Secondary Structure to Coding Theory: A Combinatorial Approach
DNA8 Revised Papers from the 8th International Workshop on DNA Based Computers: DNA Computing
A Software Tool for Generating Non-crosshybridizing Libraries of DNA Oligonucleotides
DNA8 Revised Papers from the 8th International Workshop on DNA Based Computers: DNA Computing
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
A Model to Optimize DNA Sequences Based on Particle Swarm Optimization
AMS '08 Proceedings of the 2008 Second Asia International Conference on Modelling & Simulation (AMS)
Evaluation of Ordering Methods for DNA Sequence Design Based on Ant Colony System
AMS '08 Proceedings of the 2008 Second Asia International Conference on Modelling & Simulation (AMS)
Handbook of Parametric and Nonparametric Statistical Procedures
Handbook of Parametric and Nonparametric Statistical Procedures
Improved Genetic Algorithm for Designing DNA Sequences
ISECS '09 Proceedings of the 2009 Second International Symposium on Electronic Commerce and Security - Volume 01
A survey: algorithms simulating bee swarm intelligence
Artificial Intelligence Review
DNA Sequences Optimization Based on Gravitational Search Algorithm for Reliable DNA Computing
BIC-TA '11 Proceedings of the 2011 Sixth International Conference on Bio-Inspired Computing: Theories and Applications
Biomolecular computing and programming
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Multiobjective evolutionary optimization of DNA sequences for reliable DNA computing
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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The design of reliable DNA sequences is crucial in many engineering applications which depend on DNA-based technologies, such as nanotechnology or DNA computing. In these cases, two of the most important properties that must be controlled to obtain reliable sequences are self-assembly and self-complementary hybridization. These processes have to be restricted to avoid undesirable reactions, because in the specific case of DNA computing, undesirable reactions usually lead to incorrect computations. Therefore, it is important to design robust sets of sequences which provide efficient and reliable computations. The design of reliable DNA sequences involves heterogeneous and conflicting design criteria that do not fit traditional optimization methods. In this paper, DNA sequence design has been formulated as a multiobjective optimization problem and a novel multiobjective approach based on swarm intelligence has been proposed to solve it. Specifically, a multiobjective version of the Artificial Bee Colony metaheuristics (MO-ABC) is developed to tackle the problem. MO-ABC takes in consideration six different conflicting design criteria to generate reliable DNA sequences that can be used for bio-molecular computing. Moreover, in order to verify the effectiveness of the novel multiobjective proposal, formal comparisons with the well-known multiobjective standard NSGA-II (fast non-dominated sorting genetic algorithm) were performed. After a detailed study, results indicate that our artificial swarm intelligence approach obtains satisfactory reliable DNA sequences. Two multiobjective indicators were used in order to compare the developed algorithms: hypervolume and set coverage. Finally, other relevant works published in the literature were also studied to validate our results. To this respect the conclusion that can be drawn is that the novel approach proposed in this paper obtains very promising DNA sequences that significantly surpass other results previously published.