Informatica

The field of Quantum Genetic Algorithm (QGA) has seen extensive efforts to develop reasonable algorithmic designs mainly due to its potential as well as the intrinsic difficulty to reproduce the necessary processes with the limited amount of quantum resources available, although it still remains one of rather theoretical concepts of approach to achieving quantum optimization. In this paper, we propose a dynamic encoding scheme that combines quantum adaptive search with iterative approximation of the search region. The method reuses the same quantum index register while updating the classical coordinate mapping associated with its basis states, thereby increasing local coordinate resolution without globally refining the entire continuous domain. Through noiseless statevector simulations on benchmark functions, we compare the proposed method with selected QGA variants in terms of final optimization accuracy and simulated qubit usage. The results show improved optimization performance under similar maximum qubit constraints, establishing a simulation-level resource advantage.

Machine Translation has become an important tool in overcoming the language barrier. The quality of translations depends on the languages and used methods. The research presented in this paper is based on well-known standard methods for Statistical Machine Translation that are advanced by a newly proposed approach for optimizing the weights of translation system components. Better weights of system components improve the translation quality. In most cases, machine translation systems translate to/from English and, in our research, English is paired with a Slavic language, Slovenian. In our experiment, we built two Statistical Machine Translation systems for the Slovenian-English language pair of the Acquis Communautaire corpus. Both systems were optimized using self-adaptive Differential Evolution and compared to the other related optimization methods. The results show improvement in the translation quality, and are comparable to the other related methods.

This paper reviews the interplay between global optimization and probability models, concentrating on a class of deterministic optimization algorithms that are motivated by probability models for the objective function. Some complexity results are described for the univariate and multivariate cases.

We describe an adaptive algorithm for approximating the global minimum of a continuous univariate function. The convergence rate of the error is studied for the case of a random objective function distributed according to the Wiener measure.

We consider finite population slotted ALOHA where each of n terminals has its own transmission probability pi. Given the overall traffic load λ, the probabilities pi are determined in such a way as to maximize throughput. This is achieved by solving a constrained optimization problem. The results of Abramson (1970) are obtained as a special case. Our recent results are improved (Mathar and Žilinskas, 1993).