The Application of Hybridized Genetic Algorithms to the Protein Folding Problem

The protein folding problem consists of attempting to determine the native conformation of a protein given its primary structure. This study examines various methods of hybridizing a genetic algorithm implementation in order to minimize an energy function and predict the conformation (structure) of Met-enkephalin. Genetic Algorithms are semi-optimal algorithms designed to explore and exploit a search space. The genetic algorithm uses selection, recombination, and mutation operators on populations of strings which represent possible solutions to the given problem. One step in solving the protein folding problem is the design of efficient energy minimization techniques. A conjugate gradient minimization technique is described and tested with different replacement frequencies. Baidwinian, Lamarckian, and probabilistic Lamarckian evolution are all tested. Another extension of simple genetic algorithms can be accomplished with niching. Niching works by de-emphasizing solutions based on their proximity to other solutions in the space. Several variations of niching are tested. Experiments are conducted to determine the benefits of each hybridization technique versus each other and versus the genetic algorithm by itself. The experiments are geared toward trying to find the lowest possible energy and hence the minimum conformation of Met-enkephalin. In the experiments, probabilistic Lamarckian strategies were successful in achieving energies below that of the published minimum in QUANTA

Towards Next Generation Sequential and Parallel SAT Solvers

This thesis focuses on improving the SAT solving technology. The improvements focus on two major subjects: sequential SAT solving and parallel SAT solving. To better understand sequential SAT algorithms, the abstract reduction system Generic CDCL is introduced. With Generic CDCL, the soundness of solving techniques can be modeled. Next, the conflict driven clause learning algorithm is extended with the three techniques local look-ahead, local probing and all UIP learning that allow more global reasoning during search. These techniques improve the performance of the sequential SAT solver Riss. Then, the formula simplification techniques bounded variable addition, covered literal elimination and an advanced cardinality constraint extraction are introduced. By using these techniques, the reasoning of the overall SAT solving tool chain becomes stronger than plain resolution. When using these three techniques in the formula simplification tool Coprocessor before using Riss to solve a formula, the performance can be improved further. Due to the increasing number of cores in CPUs, the scalable parallel SAT solving approach iterative partitioning has been implemented in Pcasso for the multi-core architecture. Related work on parallel SAT solving has been studied to extract main ideas that can improve Pcasso. Besides parallel formula simplification with bounded variable elimination, the major extension is the extended clause sharing level based clause tagging, which builds the basis for conflict driven node killing. The latter allows to better identify unsatisfiable search space partitions. Another improvement is to combine scattering and look-ahead as a superior search space partitioning function. In combination with Coprocessor, the introduced extensions increase the performance of the parallel solver Pcasso. The implemented system turns out to be scalable for the multi-core architecture. Hence iterative partitioning is interesting for future parallel SAT solvers. The implemented solvers participated in international SAT competitions. In 2013 and 2014 Pcasso showed a good performance. Riss in combination with Copro- cessor won several first, second and third prices, including two Kurt-Gödel-Medals. Hence, the introduced algorithms improved modern SAT solving technology

A secure communication framework for wireless sensor networks

Today, wireless sensor networks (WSNs) are no longer a nascent technology and future networks, especially Cyber-Physical Systems (CPS) will integrate more sensor-based systems into a variety of application scenarios. Typical application areas include medical, environmental, military, and commercial enterprises. Providing security to this diverse set of sensor-based applications is necessary for the healthy operations of the overall system because untrusted entities may target the proper functioning of applications and disturb the critical decision-making processes by injecting false information into the network. One way to address this issue is to employ en-route-filtering-based solutions utilizing keys generated by either static or dynamic key management schemes in the WSN literature. However, current schemes are complicated for resource-constrained sensors as they utilize many keys and more importantly as they transmit many keying messages in the network, which increases the energy consumption of WSNs that are already severely limited in the technical capabilities and resources (i.e., power, computational capacities, and memory) available to them. Nonetheless, further improvements without too much overhead are still possible by sharing a dynamically created cryptic credential. Building upon this idea, the purpose of this thesis is to introduce an efficient and secure communication framework for WSNs. Specifically, three protocols are suggested as contributions using virtual energies and local times onboard the sensors as dynamic cryptic credentials: (1) Virtual Energy-Based Encryption and Keying (VEBEK); (2) TIme-Based DynamiC Keying and En-Route Filtering (TICK); (3) Secure Source-Based Loose Time Synchronization (SOBAS) for WSNs.Ph.D.Committee Chair: Copeland, John; Committee Co-Chair: Beyah, Raheem; Committee Member: Li, Geoffrey; Committee Member: Owen, Henry; Committee Member: Zegura, Ellen; Committee Member: Zhang, Fumi

New Directions in Subband Coding

Two very different subband coders are described. The first is a modified dynamic bit-allocation-subband coder (D-SBC) designed for variable rate coding situations and easily adaptable to noisy channel environments. It can operate at rates as low as 12 kb/s and still give good quality speech. The second coder is a 16-kb/s waveform coder, based on a combination of subband coding and vector quantization (VQ-SBC). The key feature of this coder is its short coding delay, which makes it suitable for real-time communication networks. The speech quality of both coders has been enhanced by adaptive postfiltering. The coders have been implemented on a single AT&T DSP32 signal processo

Proceedings of SAT Competition 2020 : Solver and Benchmark Descriptions

Non peer reviewe

Proceedings of SAT Race 2019 : Solver and Benchmark Descriptions

Non peer reviewe