A connection-level call admission control using genetic algorithm for MultiClass multimedia services in wireless networks

Call admission control in a wireless cell in a personal communication system (PCS) can be modeled as an M/M/C/C queuing system with m classes of users. Semi-Markov Decision Process (SMDP) can be used to optimize channel utilization with upper bounds on handoff blocking probabilities as Quality of Service constraints. However, this method is too time-consuming and therefore it fails when state space and action space are large. In this paper, we apply a genetic algorithm approach to address the situation when the SMDP approach fails. We code call admission control decisions as binary strings, where a value of “1” in the position i (i=1,…m) of a decision string stands for the decision of accepting a call in class-i; a value of “0” in the position i of the decision string stands for the decision of rejecting a call in class-i. The coded binary strings are feed into the genetic algorithm, and the resulting binary strings are founded to be near optimal call admission control decisions. Simulation results from the genetic algorithm are compared with the optimal solutions obtained from linear programming for the SMDP approach. The results reveal that the genetic algorithm approximates the optimal approach very well with less complexity

AI Feynman: a Physics-Inspired Method for Symbolic Regression

A core challenge for both physics and artificial intellicence (AI) is symbolic regression: finding a symbolic expression that matches data from an unknown function. Although this problem is likely to be NP-hard in principle, functions of practical interest often exhibit symmetries, separability, compositionality and other simplifying properties. In this spirit, we develop a recursive multidimensional symbolic regression algorithm that combines neural network fitting with a suite of physics-inspired techniques. We apply it to 100 equations from the Feynman Lectures on Physics, and it discovers all of them, while previous publicly available software cracks only 71; for a more difficult test set, we improve the state of the art success rate from 15% to 90%.Comment: 15 pages, 2 figs. Our code is available at https://github.com/SJ001/AI-Feynman and our Feynman Symbolic Regression Database for benchmarking can be downloaded at https://space.mit.edu/home/tegmark/aifeynman.htm

Variable Selection using Non-Standard Optimisation of Information Criteria

The question of variable selection in a regression model is a major open research topic in econometrics. Traditionally two broad classes of methods have been used. One is sequential testing and the other is information criteria. The advent of large datasets used by institutions such as central banks has exacerbated this model selection problem. This paper provides a new solution in the context of information criteria. The solution rests on the judicious selection of a subset of models for consideration using nonstandard optimisation algorithms for information criterion minimisation. In particular, simulated annealing and genetic algorithms are considered. Both a Monte Carlo study and an empirical forecasting application to UK CPI infation suggest that the new methods are worthy of further consideration.Simulated Annealing, Genetic Algorithms, Information criteria, Model selection, Forecasting, Inflation

Approximate Two-Party Privacy-Preserving String Matching with Linear Complexity

Consider two parties who want to compare their strings, e.g., genomes, but do not want to reveal them to each other. We present a system for privacy-preserving matching of strings, which differs from existing systems by providing a deterministic approximation instead of an exact distance. It is efficient (linear complexity), non-interactive and does not involve a third party which makes it particularly suitable for cloud computing. We extend our protocol, such that it mitigates iterated differential attacks proposed by Goodrich. Further an implementation of the system is evaluated and compared against current privacy-preserving string matching algorithms.Comment: 6 pages, 4 figure

Data based identification and prediction of nonlinear and complex dynamical systems

We thank Dr. R. Yang (formerly at ASU), Dr. R.-Q. Su (formerly at ASU), and Mr. Zhesi Shen for their contributions to a number of original papers on which this Review is partly based. This work was supported by ARO under Grant No. W911NF-14-1-0504. W.-X. Wang was also supported by NSFC under Grants No. 61573064 and No. 61074116, as well as by the Fundamental Research Funds for the Central Universities, Beijing Nova Programme.Peer reviewedPostprin

Genetic-Algorithm-based Light Curve Optimization Applied to Observations of the W UMa star BH Cas

I have developed a procedure utilizing a Genetic-Algorithm-based optimization scheme to fit the observed light curves of an eclipsing binary star with a model produced by the Wilson-Devinney code. The principal advantages of this approach are the global search capability and the objectivity of the final result. Although this method can be more efficient than some other comparably global search techniques, the computational requirements of the code are still considerable. I have applied this fitting procedure to my observations of the W UMa type eclipsing binary BH Cassiopeiae. An analysis of V-band CCD data obtained in 1994/95 from Steward Observatory and U- and B-band photoelectric data obtained in 1996 from McDonald Observatory provided three complete light curves to constrain the fit. In addition, radial velocity curves obtained in 1997 from McDonald Observatory provided a direct measurement of the system mass ratio to restrict the search. The results of the GA-based fit are in excellent agreement with the final orbital solution obtained with the standard differential corrections procedure in the Wilson-Devinney code.Comment: 9 pages, 2 figures, 2 tables, uses emulateapj.st