| Summary: | Tree-Seed algorithm, TSA for short, is a population-based metaheuristic optimization algorithm proposed for solving continuous optimization problems inspired by the relation between trees and their seeds in nature. The artificial agents in TSA are trees and seeds which correspond to possible solutions to the optimization problem, and the optimization procedure is executed by the interaction between trees and seeds. In this study, a programming version of this algorithm by using a crossover solution generation mechanism has been proposed. The proposed algorithm is called TSp and its performance has been investigated on two problems, one of them is symbolic regression benchmark functions and the other is the long-term energy estimation model of Turkey. Firstly, the continuous parts of TSA, which are initialization and solution generation mechanisms, have been modified to solve automatic programming problems. The solution representation is also modified to solve the problem addressed by the study. As a result of these modifications, TSp has been obtained and applied to symbolic regression problems for performance judgment, energy estimation problems for real-world application. The experimental results of TSp have been compared with those of Genetic Programming, it is concluded that TSp is better than the GP in solving energy estimation problems. © 2022, Ismail Saritas. All rights reserved.
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