The aim of the article is to present a mathematical definition of the object model, that is known in computer science as TreeList and to show application of this model for design evolutionary algorithm, that purpose is to generate structures based on this object. The first chapter introduces the reader to the problem of presenting data using the TreeList object. The second chapter describes the problem of testing data structures based on TreeList. The third one shows a mathematical model of the object TreeList and the parameters, used in determining the utility of structures created through this model and in evolutionary strategy, that generates these structures for testing purposes. The last chapter provides a brief summary and plans for future research related to the algorithm presented in the article.
Reihaneh Kardehi Moghaddam and Navid Moshtaghi Yazdan
an GA. In the course of this gradual evolution, the system learns behavior from the environment and presents proper solutions to user queries in the application phase.
The first classifier system was the learning classifier system (LCS), which was introduced in 1976 by Holland. In this system, each rule was evaluated with a criterion called “strength.” The strength of a rule would increase the proportion to its accurate responses to training problems within the framework of reinforcement learning criteria, and an evolutionary search algorithm (normally the GA
S. Rajaram, G. Rajkumar, R. Balasundaram and D. Srinivasan
low current and low dielectric pressure the material removal rate is low. When these factors increased, then MRR is also increased.
Figure 3 shows that spark gap has less effect on MRR than on current, and Figure 4 shows that dielectric pressure has more effect on MRR than on spark gap. Figure 5 shows the interaction effect of various input parameters on MRR.
Interaction effect of various parameters on MRR
MATLAB-based Genetic Algorithm
Genetic algorithm (GA) is the commonly used evolutionary computing optimization technique
 S.G. Ahmed. Automatic generation of basis test paths using variable length genetic algorithm, Journal Information Processing Letters, Volume 114 Issue 6, June, 2014, pp. 304-316.
 R. Alavi, S. Lofti. The New Approach for Software Testing Using a Genetic Algorithm Based on Clustering Initial Test Instances, International Conference on Computer and Software Modeling 2011, IPCSIT vol.14 (2011).
 E. Alba, F. Chicano. Observations in using parallel and sequential evolutionaryalgorithms for