The success of binary-coded genetic algorithms (GAs) in problems having discrete search space largely depends on the coding used to represent the problem variables and on the crossover operator that propagates building-blocks from parent strings to children strings. In solving optimization problems having continuous search space, binary-coded GAs discretize the search space by using a coding of the problem variables in binary strings. However, the coding of real-valued variables in...
CITATION STYLE
Deb, K., & Agrawal, R. B. (1994). Simulated Binary Crossover for Continuous Search Space. Complex Systems, 9, 1–34. Retrieved from citeulike-article-id:2815748
Mendeley helps you to discover research relevant for your work.