Extended Finite-State Machine Induction Using SAT-Solver.

Abstract-In the paper we describe the extended finite-state machine (EFSM) induction method that uses SAT-solver. Input data for the induction algorithm is a set of test scenarios. The algorithm consists of several steps: scenarios tree construction, compatibility graph construction, Boolean formula construction, SAT-solver invocation and finite-state machine construction from satisfying assignment. These extended finite-state machines can be used in automata-based programming, where programs are designed as automated controlled objects. Each automated controlled object contains a finite-state machine and a controlled object. The method described has been tested on randomly generated scenario sets of size from 250 to 2000 and on the alarm clock controlling EFSM induction problem where it has greatly outperformed genetic algorithm

Evaluating the Hardness of SAT Instances Using Evolutionary Optimization Algorithms

Propositional satisfiability (SAT) solvers are deemed to be among the most efficient reasoners, which have been successfully used in a wide range of practical applications. As this contrasts the well-known NP-completeness of SAT, a number of attempts have been made in the recent past to assess the hardness of propositional formulas in conjunctive normal form (CNF). The present paper proposes a CNF formula hardness measure which is close in conceptual meaning to the one based on Backdoor set notion: in both cases some subset B of variables in a CNF formula is used to define the hardness of the formula w.r.t. this set. In contrast to the backdoor measure, the new measure does not demand the polynomial decidability of CNF formulas obtained when substituting assignments of variables from B to the original formula. To estimate this measure the paper suggests an adaptive (?,?)-approximation probabilistic algorithm. The problem of looking for the subset of variables which provides the minimal hardness value is reduced to optimization of a pseudo-Boolean black-box function. We apply evolutionary algorithms to this problem and demonstrate applicability of proposed notions and techniques to tests from several families of unsatisfiable CNF formulas

Light focusing by silicon nanosphere structures under conditions of magnetic dipole and quadrupole resonances

Metalens is a planar device for light focusing. In this work, we design and optimize c-Si nanosphere metalenses working on the magnetic dipole and quadrupole resonances of the c-Si nanoparticle. Resonant optical response of c-Si nanostructures is simulated by the multipole decomposition method along with the zero-order Born approximation. Limitations of this approach are investigated. The obtained results of optimization are verified by simulation via the T-matrix method

Evolutionary and genetic algorithms for design of metadevices working on electric dipole resonance

All-dielectric nanophotonics is a rapidly growing field of modern science. Metasurfaces and other planar devices based on all-dielectric nanoparticles lead to manage the light propagation at the nanoscale. Impressive effects such as perfect absorption, invisibility, chirality effects, negative refraction, light focusing in the area with size smaller than wavelength, nano-lasing etc - can be achieved with all-dielectric technologies. While it is needed to use more and more complicated designs for solution of modern nanophotonics' currents tasks, non-classical methods of optimization become relevant. For example, to design reconfigurable metalenses with an additional degree of freedom such as polarizability or temperature dependence, evolutionary or genetic algorithms show their high applicability. In this work, we show a new approach to design metalenses with evolutionary and genetic algorithms. © 2020 IOP Publishing Ltd