Improvement of Markov chain processes for mathematical optimization of cancer treatment

Biologists have uncovered some of the most basic mechanisms by which normal cells develop into cancerous tumors. These biological theories can be transformed into adequate mathematical models. For this reason, we attempt to study the evolution of cancer cells using the Markov Chain Processes. Based on Markov chain Processes, cancer chemotherapy will be applied on them to treat the disease. However, chemotherapy is a complex treatment mode that requires balancing the benefits of treating tumors using anti-cancer drugs with the adverse toxic side-effects caused by these drugs. Some methods of computational optimization, Genetic Algorithm (GA) in particular, have proven to be useful in helping to strike the right balance. The purpose of this paper is to put in place a strategy to solve an optimal problem to facilitate finding optimal chemotherapeutic treatments which cause the death of cancer and have fewer side effects based on a chemotherapy treatment defined by the oncologist.