A comparison between four evolutionary techniques for solving symbolic regression problems is presented in this paper. The compared methods are multi-expression programming, gene expression programming, grammatical evolution, and linear genetic programming. The comparison includes all aspects of the considered evolutionary algorithms: individual representation, fitness assignment, genetic operators, and evolutionary scheme. Several numerical experiments using five benchmarking problems are carried out. Two test problems are taken from PROBEN1 and contain real-world data. The results reveal that multi-expression programming has the best overall behavior for the considered test problems
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