A genetic algorithm basic approach for software management project

Application of Genetic Algorithms for Decision-Making in Project Management: A Literature Review

“…In the literature analysis process, we seek answers to the following research questions: (1) Which challenges of project management have been addressed using genetic algorithms? (2) What are the opportunities and limitations of genetic algorithms in the project management decision-making process? (3) What are the potential solutions to the identified genetic algorithm problems?…”

“…In the articles, we see that the GA chromosome encoding strategies are project-specific, for example, project activities [2], [9], [14], [39] and tasks [14], [21], [39], process models [39], historical project information, resources [39], work packages and package list [4], as well as project team [4].…”

“…The most popular types of chromosome encoding for the genetic algorithm are binary code [2], [9], integer number string [4], nets or graph nodes [2], [14], [39].…”

“…Many models of GA solutions vary depending on the project's size, complexity, duration, working cost, and requirements [2]. There is no uniform methodology for tests and evaluation of genetic algorithm application in project management.…”

“…If the stopping criterion of the algorithm is not met, go to Step 2. All these steps include, evaluate and employ the following parameters: the number of generations, number of chromosomes in the population, crossover and mutation probability, types of different genetics operators [2].…”