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.
Position and displacement analysis of a spherical model of a human knee joint using the vector method was presented. Sensitivity analysis and parameter estimation were performed using the evolutionary algorithm method. Computer simulations for the mechanism with estimated parameters proved the effectiveness of the prepared software. The method itself can be useful when solving problems concerning the displacement and loads analysis in the knee joint.
Position and displacement analysis of a spherical model of a human knee joint using the vector method was presented. Sensitivity analysis and parameter estimation were performed using the evolutionary algorithm method. Computer simulations for the mechanism with estimated parameters proved the effectiveness of the prepared software. The method itself can be useful when solving problems concerning the displacement and loads analysis in the knee joint
 Bodaszewski W. and Szczepiński W. (2005): Shaping structure elements by the method of discontinuous stress fields . – Warsaw: BEL Studio, PWN (in Polish).
 Bodaszewski W. (2013): Statical analyses and shaping of complex thin-walled structures . – Warsaw: BEL Studio (in Polish).
 Bodaszewski W. (2004, 2005): Algorithms of the method of statically admissible discontinuous stress fields (SADSF) . – Engineering Transactions, vol.52, No.3, pp.175-193; vol.52, No.4, pp.281-302; vol.53, No.1, pp.15-30; vol.53, No.2, pp.119
 D.E. Goldberg. Genetic algorithm in Search, Optimization, and Machine Learning, Addison Wesley, Reading, MA 1989 .
 L. Chambers. Practical Handbook of Genetic Algorithms, aplications, CRC Press, New York 2001 .
 V. Kvasnička, J. Pospíchal, P. Tiňo. Evolutionaryalgorithms, STU, Bratislava 2000 (in Slovak).
 Sysweld: Reference manual, ESI Group, 2015 .
 V. Voštiar. Numerical Analysis of Residual Streses after Welding in Planar Parts, Diploma Thesis, STU Engineering Faculty in Bratislava, Bratislava
1. Marian Popescu, Ilie Borcoși, “Increasing productivity at the level of the power plant by optimizing the distributed systems and hierarchical systems”, Annals of the University Constantin Brancusi, Series Engeneering, ISSN 1842-4856. No.3/2013, pp. 241-244, (2013)
2. Ion Marian Popescu, “Necessity Implementation of advanced control algorithms in the complex processes in the energy industry”, Annals of the University of Craiova, Series: Automation, Computer, Electronics and Mechatronics, ISSN 1841-0626, Vol.11 (38), issue 1, (2014
 Dulęba I., Metody i algorytmy planowania ruchu robotów mobilnych i manipulacyjnych, EXIT, Warszawa 2001 [Methods an algorithms used for planning movement of mobile and manipulation robots - available in Polish].
 Fossen T. I., Fjellstad O. E., Nonlinear modelling of marine vehicle in 6 degrees of freedom, ‘Journal of Mathematical Modelling of Systems’, 1995, No. 1.
 Fossen T. I., Guidance and Control of Ocean Vehicles, John Wiley & Sons Ltd., 1994.
 Garus J., Dynamika i
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