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J. Kratica, “Improvement of Simple Genetic Algorithm for Solving the Uncapacitated Warehouse Location Problem,” In: R. Roy, T. Furuhachi and P. K. Chawdhry, Eds., Advances in Soft Computing—Engineering Design and Manufactining, Springer-Verlang London Limited, London, 1999, pp. 390-402.

has been cited by the following article:

• TITLE: On Some Basic Concepts of Genetic Algorithms as a Meta-Heuristic Method for Solving of Optimization Problems

  AUTHORS: Milena Bogdanović

  KEYWORDS: Genetic Algorithm, Individuals, Genetic Operator, Selection, Crossover, Mutation

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.4 No.8, August 2, 2011

  ABSTRACT: The genetic algorithms represent a family of algorithms using some of genetic principles being present in nature, in order to solve particular computational problems. These natural principles are: inheritance, crossover, mutation, survival of the fittest, migrations and so on. The paper describes the most important aspects of a genetic algorithm as a stochastic method for solving various classes of optimization problems. It also describes the basic genetic operator selection, crossover and mutation, serving for a new generation of individuals to achieve an optimal or a good enough solution of an optimization problem being in question.