1. An Approach of Artificial Intelligence Application for Laboratory Tests Evaluation Ş.l.univ.dr.ing. Corina SĂVULESCU University of Piteşti

2. The principal domains where GA have successfully applied to optimization problems function optimization  function optimization  image processing  classification and machine learning  training of neural networks  systems’ control

3. Why using a GA? Why using a GA?  are stochastic algorithms  use a vocabulary borrowed from natural genetics  are more robust than existing directed search methods  maintain a population of potential solutions  the structure of a simple GA is the same as the structure of any evolution program

4. A GA for a particular problem must have the following five components:  a genetic representation for potential solutions to the problem  a way to create an initial population of potential solutions  an evaluation function that plays the role of environment rating solution in term of their “fitness”  a genetic operator that alter composition of children  a set of values for various parameters that the genetic algorithm uses

5. GA’s principles N individuals Generation 3 Generation 2 Generation 1 Generation 0 Fitness

6. The structure of the chosen genetic algorithm Generation of initial population P(t) Step 1:

7. The structure of the chosen genetic algorithm The evaluation function is applied for each chromosome of the P(t) population, determining their fitness values Step 2: S =

8. The structure of the chosen genetic algorithm The population's chromosomes are sorted based on their fitness value determined during the previous step Step 3:

9. The structure of the chosen genetic algorithm The best chromosomes are selected, and they will be placed unconditionally in the next population P(t+1) The best chromosomes are selected, and they will be placed unconditionally in the next population P(t+1) Step 4: 50 % 30 % 15 % 5 %

10. The structure of the chosen genetic algorithm The chromosomes that are object to the crossover operator are then selected Step 5:

11. The structure of the chosen genetic algorithm The descendants from the previous step are subject to the mutation operator, resulting new members for the P(t+1) population The descendants from the previous step are subject to the mutation operator, resulting new members for the P(t+1) population Step 6:

12. The structure of the chosen genetic algorithm The population P(t+1) is completed with individuals selected randomly from the P(t) population The population P(t+1) is completed with individuals selected randomly from the P(t) population Step 7:

13. The application description Fig. 1 – System's index response

14. Results of the system identification Original model 0.62.5 Model identified without noise 0.612.59 Model identified with noise 0.652.79 (rad/sec) Where are the function’s parameters:

15. Identified system's response

16. The application of the genetic algorithm in electrophoresis tests

17. Positioning the agarose gel

18. The application of serum on the agarose gel

19. The electrophoresis machine

20. Drying incubator

21. An example of results using the agarose gel

22. The applications of GA to the electrophoresis tests

23. Application of the genetic algorithm in electrophoresis tests

24. The results obtained from using a GA from the same example

26. The test result

27. Conclusions This application is an alternative method for evaluation of the laboratory tests (in special electrophoresis tests), using artificial intelligence. This application is an alternative method for evaluation of the laboratory tests (in special electrophoresis tests), using artificial intelligence. The main advantage of this method is the need of minimal medical knowledge. Therefore, GA implementation is an instrument easy to use by low/medium trained personnel, offering tests results quickly and clearly. The main advantage of this method is the need of minimal medical knowledge. Therefore, GA implementation is an instrument easy to use by low/medium trained personnel, offering tests results quickly and clearly.