Šiame darbe išanalizuoti genetinių algoritmų (GA) veikimo principai. Analizuojamos egzistuojančios modeliavimo aplinkos ir genetiniais algoritmais pagrįsti modeliavimo įrankiai. Kuriant modeliavimo įrankį, nustatyti funkciniai ir nefunkciniai reikalavimai. Realizuotas sukurtas įrankis ir atliktas pasirinktos esybės evoliucijos modeliavimas. "16th International Conference on Information and Software Technologies" konferencijoje buvo pristatytas pranešimas “Genetic Algorithm Modeling Approach for Mobile Malware Evolution Forecasting”. Panaudojus jame pateiktus parametrų duomenis, atlikti bandymai su sukurtu modeliavimo įrankiu. Taip pat XIV jaunųjų mokslininkų konferencijoje „Mokslas - Lietuvos ateitis“ 2011, pristatytas straipsnis „Informacijos saugos dalykinės srities esybių evoliucijos modeliavimo įrankio, paremto genetiniais algoritmais, kūrimas “, šis straipsnis buvo parašytas remiantis šiuo darbu. Darbą sudaro: 7 skyriai, 26 paveikslai, 7 lentelės, 2 priedai. Literatūros sąraše 52 šaltiniai. / This study analyzes operating principles of the genetic algorithms (GA), also submit proposals for the calculation of GA. Discuss the existing simulation environment and tools to implement GA. Towards a modeling tool to determine the functional and non-functional requirements. Marketed developed tool and to carry out tests for selected test of evolutionary analysis. ‘16th International Conference on Information and Software Technologies’ was presented the article ‘Genetic Algorithm Modeling Approach for Mobile Malware Evolution forecasting’ using the parameters details of this article perform the tests with simulation tool. Also XIV Conference of Young Scientists ‘Science - The future of Lithuania‘ 2011, was introduced the article ‘Development of the subject area of the information security beings evolutionary modeling tool based on genetic algorithms’. This article was written on the basis of this work. Thesis consist of: 7 chapters, 26 pictures, 7 tables, 2 appendixes, 52 bibliographical entries.

Difficult problems are tasks which number of possible solutions increase exponentially or factorially. Application of common mathematical methods for finding proper solution in polynomial time is ineffective. Signal prediction is an example of diffucult problem. Signal is represented with a time serie and there is no explicit mathematical formula describing the signal. When genetic algorithms are applicated, they try to discover hidden patterns in time serie. These patterns can be used for prediction. Implication rules are used for discovery of these hidden patterns in time serie. Each rule is represented by one chromosome in population. Rules consist of two parts: conditional part and result part. Rules in population are compared with time serie and then the rules are evaluated according to their success in prediction. After the evaluation of rules, simulated evolution is started. Result of this evolution process is a group of rules which represent the most distinct patterns in time serie. These rules are then validated on validation set. Application is implemented in JAVA programming language.

Improving the predictability of airline schedules in the National Airspace System (NAS) has been a constant endeavor, particularly as system delays grow with ever-increasing demand. Airline schedules need to be resistant to perturbations in the system including Ground Delay Programs (GDPs) and inclement weather. The strategic search heuristic proposed in this dissertation significantly improves airline schedule reliability by assigning airport departure and arrival slots to each flight in the schedule across a network of airports. This is performed using a multi-objective optimization approach that is primarily based on historical flight and taxi times but also includes certain airline, airport, and FAA priorities. The intent of this algorithm is to produce a more reliable, robust schedule that operates in today's environment as well as tomorrow's 4-Dimensional Trajectory Controlled system as described the FAA's Next Generation ATM system (NextGen). This novel airline schedule optimization approach is implemented using a multi-objective evolutionary algorithm which is capable of incorporating limited airport capacities. The core of the fitness function is an extensive database of historic operating times for flight and ground operations collected over a two year period based on ASDI and BTS data. Empirical distributions based on this data reflect the probability that flights encounter various flight and taxi times. The fitness function also adds the ability to define priorities for certain flights based on aircraft size, flight time, and airline usage. The algorithm is applied to airline schedules for two primary US airports: Chicago O'Hare and Atlanta Hartsfield-Jackson. The effects of this multi-objective schedule optimization are evaluated in a variety of scenarios including periods of high, medium, and low demand. The schedules generated by the optimization algorithm were evaluated using a simple queuing simulation model implemented in AnyLogic. The scenarios were simulated in AnyLogic using two basic setups: (1) using modes of flight and taxi times that reflect highly predictable 4-Dimensional Trajectory Control operations and (2) using full distributions of flight and taxi times reflecting current day operations. The simulation analysis showed significant improvements in reliability as measured by the mean square difference (MSD) of filed versus simulated flight arrival and departure times. Arrivals showed the most consistent improvements of up to 80% in on-time performance (OTP). Departures showed reduced overall improvements, particularly when the optimization was performed without the consideration of airport capacity. The 4-Dimensional Trajectory Control environment more than doubled the on-time performance of departures over the current day, more chaotic scenarios. This research shows that airline schedule reliability can be significantly improved over a network of airports using historical flight and taxi time data. It also provides for a mechanism to prioritize flights based on various airline, airport, and ATC goals. The algorithm is shown to work in today's environment as well as tomorrow's NextGen 4-Dimensional Trajectory Control setup.