Modification of the Genetic Algorithm for Solving Discrete Optimization Problems
Abstract
The paper analyzes the working mechanism of the genetic algorithm. Various sets of the genetic algorithm parameters were tested. The best combination of genetic algorithm operators for solving the substantial problem of discrete optimization is identified. The results of the modified algorithm are evaluated.
Current realization is developed in the Python programming language to solve the problem of forming the curriculum of the educational program. For a given set of professional competencies of the educational program, mathematical models for defining a set of disciplines from a given list of disciplines are constructed. Each mathematical model enables the best choice of disciplines for a given professional competence. Each professional competence in the educational program is followed by the genetic algorithm is run. The initial population is created using the generate_chromosome() function, then for each chromosome whether it is fit for solving the problem or not is calculated. All values are put in the fitnessValues list. To monitor the process of finding the optimal solution, the best and average values of the fitness function are tracked down in each generation. The obtained results are being put in maxFitnessValues and meanFitnessValues lists respectively.
Perspective research areas: design of analytical methods to evaluate the performance of the algorithm, as well as development of a mechanism to automatically adjust its main parameters.
Possible areas of use: to manage the educational process in educational organizations, application of the algorithm in the introduction the digital services as a part of software packages in automated systems.
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