DNA Computing Algorithms Research Articles

Solving two-dimensional cutting stock problem via a DNA computing algorithm

Two-dimensional cutting stock problem (TDCSP) is a well-known combinatorial optimization problem in which a given set of two-dimensional small pieces with different shapes should be cut from a given main board so that the demand of each small piece is satisfied and the total waste is minimized. Since TDCSP is an NP-complete problem, it is unsolvable in polynomial time on electronic computers. However, using the structure of DNA molecules, DNA computing algorithms are capable to solve NP-complete problems in polynomial time. In this paper, a DNA computing algorithm based on the sticker model is presented to find the optimal solution to TDCSP. It is proved that the time complexity of this algorithm on DNA computers is polynomial considering the number of small pieces and the length and width of the main board.

Developing Tools for Networks of Processors

A great deal of research eort is currently being made in the realm of so called natural computing. Natural computing mainly focuses on the denition, formal description, analysis, simulation and programming of new models of computation (usually with the same expressive power as Turing Machines) inspired by Nature, which makes them particularly suitable for the simulation of complex systems.Some of the best known natural computers are Lindenmayer systems (Lsystems, a kind of grammar with parallel derivation), cellular automata, DNA computing, genetic and evolutionary algorithms, multi agent systems, arti- cial neural networks, P-systems (computation inspired by membranes) and NEPs (or networks of evolutionary processors). This chapter is devoted to this last model.

Immunity-Based Optimal Estimation Approach for a New Real Time Group Elevator Dynamic Control Application for Energy and Time Saving

Nowadays, the increasing use of group elevator control systems owing to increasing building heights makes the development of high-performance algorithms necessary in terms of time and energy saving. Although there are many studies in the literature about this topic, they are still not effective enough because they are not able to evaluate all features of system. In this paper, a new approach of immune system-based optimal estimate is studied for dynamic control of group elevator systems. The method is mainly based on estimation of optimal way by optimizing all calls with genetic, immune system and DNA computing algorithms, and it is evaluated with a fuzzy system. The system has a dynamic feature in terms of the situation of calls and the option of the most appropriate algorithm, and it also adaptively works in terms of parameters such as the number of floors and cabins. This new approach which provides both time and energy saving was carried out in real time. The experimental results comparatively demonstrate the effects of method. With dynamic and adaptive control approach in this study carried out, a significant progress on group elevator control systems has been achieved in terms of time and energy efficiency according to traditional methods.

Swarm Intelligence and Its Applications

Swarm Intelligence and Its Applications

Evolutionary neural networks and DNA computing algorithms for dual-axis motion control

A new method is proposed to deal with the dual-axis control of a multi-variables system with two induction motors. Investigation of resolving the cross-coupling problem of dual-axis platform is addressed by a neural net-based decoupling compensator and a sufficient condition ensuring closed-loop stability is derived. An evolutionary algorithm processing the universal seeking capability is proposed for finding the optimal connecting weights of the neural decoupling compensator and the gains of PID controllers. Extensive numerical studies verify the performance and applicability of the proposed design under a variety of operating conditions.

Membrane computing and complexity theory: A characterization of PSPACE

A P system is a natural computing model inspired by information processing in cells and cellular membranes. We show that confluent P systems with active membranes solve in polynomial time exactly the class of problems PSPACE. Consequently, these P systems prove to be equivalent (up to a polynomial time reduction) to the alternating Turing machine or the PRAM computer. Similar results were achieved also with other models of natural computation, such as DNA computing or genetic algorithms. Our result, together with the previous observations, suggests that the class PSPACE provides a tight upper bound on the computational potential of biological information processing models.

A new approach to advance the DNA computing

It has been evidenced that DNA computing can solve those problems which are currently intractable on even the fastest electronic computers. The algorithm design for DNA computing, however, is not straightforward. Both a significant knowledge of the DNA molecule and a strong background in computer engineering are required to develop efficient DNA computing algorithms. The existing models based on which a few DNA computing algorithms were developed are not sufficiently powerful and robust to attract potential users. In this paper, a new DNA computing model is introduced based on which new algorithms are developed to solve the 3-Coloring problem. These new algorithms are presented as vehicles for demonstrating the advantages of the new model, and they can be expanded to solve other NP-complete problems. Our new algorithms can significantly speed up computation and therefore achieve a better time performance. Furthermore, with the given resource, our algorithms can solve problems of much greater size as compared to existing DNA computing algorithms. In addition, the error rate can be greatly reduced by applying our new algorithms. All of the advantages provided by the new model make DNA computing very efficient and attractive in solving computational intense problems.

A surface-based DNA algorithm for the minimal vertex coverproblem*

Abstract DNA computing was proposed for solving a class of intractable computational problems, of whichthe computing time will grow exponentially with the problem size. Up to now. many achievements have been made to improve its performance and increase its reliability. It has been shownmany times that the surface-based DNA computing technique has very low error rate, but the technique has not been widely used in the DNA computing algorithms design. In this paper, a surface-based DNA computing algorithm for minimal vertex cover problem, a problem well-known for its exponential difficulty, is introduced. This work provides further evidence for the ability of surface-based DNA computing in solving NP-complete problems.

Computers are from Mars, organisms are from Venus

Biology and computer science share a natural affinity. Physicist Erwin Schrodinger envisioned life as an aperiodic crystal, observing that the organizing structure of life is neither completely regular, like a pure crystal, nor completely chaotic and without structure, like dust in the wind. Perhaps this is why biological information has never satisfactorily yielded to classical mathematical analysis. Machine computations combine elegant algorithms with brute-force calculations, which seems a reasonable approach to this aperiodic structure. Likewise, computing seeks to create a machine that can flexibly solve diverse problems. In nature, such plastic problem solving resides uniquely in the domain of organic matter. Thus, examining how organisms solve problems can lead to new computation and algorithm development approaches that devour the problems that are so easy to approach using a computer, yet so difficult to tackle in the laboratory. The paper discusses the two most successful uses of computers in biology, comparative sequence analysis and in silico cloning. It considers DNA computing and genetic algorithms.