Using genetic algorithms for the single allocation hub location problem

Hub location problem is an NP-hard problem that frequently arises in the design of transportation and distribution systems, postal delivery networks, and airline passenger flow. This work focuses on the Single Allocation Hub Location Problem (SAHLP). Genetic Algorithms (GAs) for the capacitated and uncapacitated variants of the SAHLP based on new chromosome representations and crossover operators are explored. The GAs is tested on two well-known sets of real-world problems with up to 200 nodes. The obtained results are very promising. For most of the test problems the GA obtains improved or best-known solutions and the computational time remains low. The proposed GAs can easily be extended to other variants of location problems arising in network design planning in transportation systems.

GA approach for finding Rough Set decision rules based on bireducts

Feature selection plays an important role in knowledge discovery and data mining nowadays. In traditional rough set theory, feature selection using reduct - the minimal discerning set of attributes - is an important area. Nevertheless, the original definition of a reduct is restrictive, so in one of the previous research it was proposed to take into account not only the horizontal reduction of information by feature selection, but also a vertical reduction considering suitable subsets of the original set of objects. Following the work mentioned above, a new approach to generate bireducts using a multi--objective genetic algorithm was proposed. Although the genetic algorithms were used to calculate reduct in some previous works, we did not find any work where genetic algorithms were adopted to calculate bireducts. Compared to the works done before in this area, the proposed method has less randomness in generating bireducts. The genetic algorithm system estimated a quality of each bireduct by values of two objective functions as evolution progresses, so consequently a set of bireducts with optimized values of these objectives was obtained. Different fitness evaluation methods and genetic operators, such as crossover and mutation, were applied and the prediction accuracies were compared. Five datasets were used to test the proposed method and two datasets were used to perform a comparison study. Statistical analysis using the one-way ANOVA test was performed to determine the significant difference between the results. The experiment showed that the proposed method was able to reduce the number of bireducts necessary in order to receive a good prediction accuracy. Also, the influence of different genetic operators and fitness evaluation strategies on the prediction accuracy was analyzed. It was shown that the prediction accuracies of the proposed method are comparable with the best results in machine learning literature, and some of them outperformed it.

Niagara Resort and Tourist Association Records, 1975-1976, 1978-1983, 1985, 1987-1989, n.d.

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Lighting recommendations for the Ryan Electric Scintillator and Fireworks Program for the official dedication Niagara Falls illumination, 1925

Walter D’Arcy Ryan was born in 1870 in Kentville, Nova Scotia. He became the chief of the department of illumination at the General Electric Company of Schenectady, New York. He was a founder in the field of electrical illumination. He built the electric steam scintillator which had numerous nozzles and valves. The operator would release steam through the valves. The nozzles all had names which included: Niagara, fan, snake, plume, column, pinwheel and sunburst. The steam scintillator was combined with projectors, prismatic reflectors, flashers and filters to produce the desired effects. In 1920 a group of businessmen from Niagara Falls, New York formed a group who called themselves the “generators’. They lobbied the American and Canadian governments to improve the illumination of the Falls. They were able to raise $58, 000 for the purchase and installation of 24 arc lights to illuminate the Falls. On February 24th, 1925 the Niagara Falls Illumination Board was formed. Initially, the board had a budget of $28,000 for management, operation and maintenance of the lights. The power was supplied free by the Ontario Power Company. They had 24 lights installed in a row on the Ontario Power Company surge tank which was next to the Refectory in Victoria Park on the Canadian side. The official opening ceremony took place on June 8th, 1925 and included a light parade in Niagara Falls, New York and an international ceremony held in the middle of the Upper Steel Arch Bridge. Walter D’Arcy Ryan was the illuminating engineer and A.D. Dickerson who was his New York field assistant directed the scintillator. with information from American Technological Sublime by David E. Nye and the Niagara Falls info website Location: Brock University Archives Source Information: Subject Headings: Added Entries: 100 Ryan, W. D’A. |q (Walter D’Arcy), |d 1870-1934 610 General Electric Company 650 Lighting, Architectural and decorative 650 Lighting |z New York (State) |z Niagara Falls 700 Dickerson, A.F. 700 Schaffer, J.W. Related material held at other repositories: The Niagara Falls Museum in Niagara Falls, Ontario has a program (pamphlet) dedicating new lighting in 1958 and it has postcards depicting the illumination of the Falls. Some of Ryan’s accomplishments can be seen at The Virtual Museum of the City of San Francisco. Described by: Anne Adams Date: Sept 26,Upper Steel Arch Bridge. Walter D’Arcy Ryan was the illuminating engineer and A.D. Dickerson who was his New York field assistant directed the scintillator. with information from American Technological Sublime by David E. Nye and the Niagara Falls info website

Application of Reptation Quantum Monte Carlo and Related Methods to LiH

This work investigates mathematical details and computational aspects of Metropolis-Hastings reptation quantum Monte Carlo and its variants, in addition to the Bounce method and its variants. The issues that concern us include the sensitivity of these algorithms' target densities to the position of the trial electron density along the reptile, time-reversal symmetry of the propagators, and the length of the reptile. We calculate the ground-state energy and one-electron properties of LiH at its equilibrium geometry for all these algorithms. The importance sampling is performed with a single-determinant large Slater-type orbitals (STO) basis set. The computer codes were written to exploit the efficiencies engineered into modern, high-performance computing software. Using the Bounce method in the calculation of non-energy-related properties, those represented by operators that do not commute with the Hamiltonian, is a novel work. We found that the unmodified Bounce gives good ground state energy and very good one-electron properties. We attribute this to its favourable time-reversal symmetry in its target density's Green's functions. Breaking this symmetry gives poorer results. Use of a short reptile in the Bounce method does not alter the quality of the results. This suggests that in future applications one can use a shorter reptile to cut down the computational time dramatically.