New generation chaotic based root development algorithms

The search for the best method for solving problems for scientists has become a remarkable subject in recent years. Many meta-heuristic methods have been proposed for the solution of mathematical problems that cannot be solved by classical methods. Meta-intuitive approaches do not guarantee the best solution. But they try to give near best results. The fact that meta-heuristic approaches can be adapted to problems has increased its importance over time. Physics-based, chemistry-based, biology-based, math-based, social-based, music-based, sports-based, herd intelligence-based and hybrid-based meta-intuitive algorithms, which are evaluated in 9 different categories, especially herd intelligence-based algorithms are more popular due to their success in solving problems. Herd intelligence-based algorithms are aimed at developing optimization methods by examining animal and plant behavior. Recent studies on plants have shown that plants exhibit as smart behaviors as animals. Root Development Algorithms have taken the most popular field of study on the smart approaches of plants. Because Root Development Algorithms provide great benefits in modeling and solving real life problems. In addition, the difficulty of mathematical problems in daily life has brought to the fore the necessity of developing the recommended optimization methods and for this purpose, chaotic maps have been used in meta-heuristic optimization algorithms. In this thesis, meta-heuristic optimization methods, root development algorithms and chaotic maps in the literature are examined. To increase the performance and performance of optimization algorithms, new generation hybrid chaotic maps have been proposed and new generation hybrid chaotic map based root development algorithms have been developed with these maps. Also, in order to test the performance of these methods, the proposed algorithms have been run with benchmarking functions commonly used in the literature. Then, performance measurement was made on the engineering problems and feature selector in the data set and the results were compared and presented.