A genetic algorithm with genes-association recognition for flowshop scheduling problems

In this paper, we consider a flowshop scheduling problem with a special blocking RCb (Release when Completing Blocking). This flexible production system is prevalent in some industrial environments. Genetic algorithms are first proposed for solving these flowshop problems and different initial populations have been tested to find which is best adapted. Then, a method is proposed for further improving genetic algorithm found solutions, which consists in marking out recurrent genes association occurrences in an already genetic algorithm optimized population. This idea directly follows Holland's first statement about nature observations. Here, proposed idea is that populations well adapted to a problem have an adapted genetic code with common properties. We propose to mark out these properties in available genetic code to further improve genetic algorithm efficiency. Implementation of this method is presented and obtained results on flowshop scheduling problems are discussed.