5,293 research outputs found
Competent genetic-evolutionary optimization of water distribution systems
A genetic algorithm has been applied to the optimal design and rehabilitation of a water distribution system. Many of the previous applications have been limited to small water distribution systems, where the computer time used for solving the problem has been relatively small. In order to apply genetic and evolutionary optimization technique to a large-scale water distribution system, this paper employs one of competent genetic-evolutionary algorithms - a messy genetic algorithm to enhance the efficiency of an optimization procedure. A maximum flexibility is ensured by the formulation of a string and solution representation scheme, a fitness definition, and the integration of a well-developed hydraulic network solver that facilitate the application of a genetic algorithm to the optimization of a water distribution system. Two benchmark problems of water pipeline design and a real water distribution system are presented to demonstrate the application of the improved technique. The results obtained show that the number of the design trials required by the messy genetic algorithm is consistently fewer than the other genetic algorithms
Developing Efficient Metaheuristics for Communication Network Problems by using Problem-specific Knowledge
Metaheuristics, such as evolutionary algorithms or simulated annealing, are widely applicable heuristic optimization strategies that have shown encouraging results for a large number of difficult optimization problems. To show high performance, metaheuristics need to be adapted to the properties of the problem at hand. This paper illustrates how efficient metaheuristics can be developed for communication network problems by utilizing problem-specific knowledge for the design of a high-quality problem representation. The minimum communication spanning tree (MCST) problem finds a communication spanning tree that connects all nodes and satisfies their communication requirements for a minimum total cost. An investigation into the properties of the problem reveals that optimum solutions are similar to the minimum spanning tree (MST). Consequently, a problem-specific representation, the link biased (LB) encoding, is developed, which represents trees as a list of floats. The LB encoding makes use of the knowledge that optimum solutions are similar to the MST, and encodes trees that are similar to the MST with a higher probability. Experimental results for different types of metaheuristics show that metaheuristics using the LB-encoding efficiently solve existing MCST problem instances from the literature, as well as randomly generated MCST problems of different sizes and types
Automated unique input output sequence generation for conformance testing of FSMs
This paper describes a method for automatically generating unique input output (UIO) sequences for FSM conformance testing. UIOs are used in conformance testing to verify the end state of a transition sequence. UIO sequence generation is represented as a search problem and genetic algorithms are used to search this space. Empirical evidence indicates that the proposed method yields considerably better (up to 62% better) results compared with random UIO sequence generation
Survivable algorithms and redundancy management in NASA's distributed computing systems
The design of survivable algorithms requires a solid foundation for executing them. While hardware techniques for fault-tolerant computing are relatively well understood, fault-tolerant operating systems, as well as fault-tolerant applications (survivable algorithms), are, by contrast, little understood, and much more work in this field is required. We outline some of our work that contributes to the foundation of ultrareliable operating systems and fault-tolerant algorithm design. We introduce our consensus-based framework for fault-tolerant system design. This is followed by a description of a hierarchical partitioning method for efficient consensus. A scheduler for redundancy management is introduced, and application-specific fault tolerance is described. We give an overview of our hybrid algorithm technique, which is an alternative to the formal approach given
On the design of an ECOC-compliant genetic algorithm
Genetic Algorithms (GA) have been previously applied to Error-Correcting Output Codes (ECOC) in state-of-the-art works in order to find a suitable coding matrix. Nevertheless, none of the presented techniques directly take into account the properties of the ECOC matrix. As a result the considered search space is unnecessarily large. In this paper, a novel Genetic strategy to optimize the ECOC coding step is presented. This novel strategy redefines the usual crossover and mutation operators in order to take into account the theoretical properties of the ECOC framework. Thus, it reduces the search space and lets the algorithm to converge faster. In addition, a novel operator that is able to enlarge the code in a smart way is introduced. The novel methodology is tested on several UCI datasets and four challenging computer vision problems. Furthermore, the analysis of the results done in terms of performance, code length and number of Support Vectors shows that the optimization process is able to find very efficient codes, in terms of the trade-off between classification performance and the number of classifiers. Finally, classification performance per dichotomizer results shows that the novel proposal is able to obtain similar or even better results while defining a more compact number of dichotomies and SVs compared to state-of-the-art approaches
Channel routing optimization using a genetic algorithm
A modified approach for the application of Genetic Algorithm (GA) to the Channel Routing Problem has been proposed. The code based on the algorithm proposed in [1] has been implemented for the GA procedures of Initial Population Generation, Crossover, Mutation and Selection. A few improvements over the existing work have been made and the results so far obtained have been encouraging. Further experimentation is being done on the algorithm and other ideas generated during the development of the code are being implemented for faster convergence of the algorithm and for generation of more efficient results. Also application of variations of the GA technique like Vector GA and even other computationally intelligent techniques like Particle Swarm Optimization to the channel routing problem is being thought of
Joint assembly and genetic mapping of the Atlantic horseshoe crab genome reveals ancient whole genome duplication
Horseshoe crabs are marine arthropods with a fossil record extending back
approximately 450 million years. They exhibit remarkable morphological
stability over their long evolutionary history, retaining a number of ancestral
arthropod traits, and are often cited as examples of "living fossils." As
arthropods, they belong to the Ecdysozoa}, an ancient super-phylum whose
sequenced genomes (including insects and nematodes) have thus far shown more
divergence from the ancestral pattern of eumetazoan genome organization than
cnidarians, deuterostomes, and lophotrochozoans. However, much of ecdysozoan
diversity remains unrepresented in comparative genomic analyses. Here we use a
new strategy of combined de novo assembly and genetic mapping to examine the
chromosome-scale genome organization of the Atlantic horseshoe crab Limulus
polyphemus. We constructed a genetic linkage map of this 2.7 Gbp genome by
sequencing the nuclear DNA of 34 wild-collected, full-sibling embryos and their
parents at a mean redundancy of 1.1x per sample. The map includes 84,307
sequence markers and 5,775 candidate conserved protein coding genes. Comparison
to other metazoan genomes shows that the L. polyphemus genome preserves
ancestral bilaterian linkage groups, and that a common ancestor of modern
horseshoe crabs underwent one or more ancient whole genome duplications (WGDs)
~ 300 MYA, followed by extensive chromosome fusion
Feature Reinforcement Learning: Part I: Unstructured MDPs
General-purpose, intelligent, learning agents cycle through sequences of
observations, actions, and rewards that are complex, uncertain, unknown, and
non-Markovian. On the other hand, reinforcement learning is well-developed for
small finite state Markov decision processes (MDPs). Up to now, extracting the
right state representations out of bare observations, that is, reducing the
general agent setup to the MDP framework, is an art that involves significant
effort by designers. The primary goal of this work is to automate the reduction
process and thereby significantly expand the scope of many existing
reinforcement learning algorithms and the agents that employ them. Before we
can think of mechanizing this search for suitable MDPs, we need a formal
objective criterion. The main contribution of this article is to develop such a
criterion. I also integrate the various parts into one learning algorithm.
Extensions to more realistic dynamic Bayesian networks are developed in Part
II. The role of POMDPs is also considered there.Comment: 24 LaTeX pages, 5 diagram
Revisiting the Training of Logic Models of Protein Signaling Networks with a Formal Approach based on Answer Set Programming
A fundamental question in systems biology is the construction and training to
data of mathematical models. Logic formalisms have become very popular to model
signaling networks because their simplicity allows us to model large systems
encompassing hundreds of proteins. An approach to train (Boolean) logic models
to high-throughput phospho-proteomics data was recently introduced and solved
using optimization heuristics based on stochastic methods. Here we demonstrate
how this problem can be solved using Answer Set Programming (ASP), a
declarative problem solving paradigm, in which a problem is encoded as a
logical program such that its answer sets represent solutions to the problem.
ASP has significant improvements over heuristic methods in terms of efficiency
and scalability, it guarantees global optimality of solutions as well as
provides a complete set of solutions. We illustrate the application of ASP with
in silico cases based on realistic networks and data
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