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Metaheuristic Methods as an Approach to Solving Optimization Problems Using Genetic Algorithm

Student: Utkina Aliona

Supervisor: Kristina Alexandrovna Mozgovaya

Faculty: St.Petersburg School of Economics and Management

Educational Programme: Economics (Bachelor)

Year of Graduation: 2017

There is a growing interest in the study of meta-heuristic methods for solving optimization problems. The fact is, almost every industry faces the need to solve optimization problems, and in some cases the finding of an optimum becomes practically impossible. Meta-heuristic methods are a class of approximate algorithms designed to solve problems of combinatorial optimization. The main advantage of using these algorithms is the search for the most suitable solutions to problems without knowing the search space. In this paper, we consider a genetic algorithm belonging to the class of evolutionary methods. The purpose of this work is to create a tool for solving a certain class of optimization problems. "The Equal Piles Problem" was considered, as well as a new genetic algorithm that was restored in Java. This algorithm showed good results using randomly generated numbers, the frequency of getting into the exact solution was 28 trials of 30.

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