PMC:4331679 / 18268-19448 JSONTXT

{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/4331679","sourcedb":"PMC","sourceid":"4331679","source_url":"https://www.ncbi.nlm.nih.gov/pmc/4331679","text":"We applied the Genetic Algorithm to search for the network topologies that fit the real data well, in order to uncover the rewiring inside the tumour cells. There are several optimisation techniques available in the literature which can be used to search for the network topologies. These techniques include, for example, Simulated Annealing, Evolution Strategies, Genetic Algorithm, Evolutionary Programming etc. Simulated Annealing has population size of only one which represents current solution, thus the solution space is not as wide as GA. Similarly, Evolutionary Programming can only use mutation operator. By contrast, Genetic Algorithms have several solutions in the beginning and crossovers can be performed to generate the diversity in the population of network structures. While the probabilistic selection of GA helps us simulate the stochastic biochemical networks in better ways, Evolution Strategies uses deterministic selection [25,26]. After comparing these techniques, we decided to use Genetic Algorithm in our study. As a global search technique, GA is considered to be very robust as it makes very few assumptions about the problem under consideration [27].","tracks":[]}