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Genetic Programming (GP) is an automated method for creating computer programs starting from a high-level description of the problem to be solved. Many variants of GP have been proposed in the recent years. In this paper we are reviewing the main GP variants with linear representation. Namely, Linear Genetic Programming, Gene Expression Programming, Multi Expression Programming, Grammatical Evolution, Cartesian Genetic Programming and Stack-Based Genetic Programming. A complete description is provided for each method. The set of applications where the methods have been applied and several Internet sites with more information about them are also given. [ABSTRACT FROM AUTHOR]

A complex model for evolving the update strategy of a Particle Swarm Optimisation (PSO) algorithm is described in this paper. The model is a hybrid technique that combines a Genetic Algorithm (GA) and a PSO algorithm. Each GA chromosome is an array encoding a meaning for updating the particles of the PSO algorithm. The Evolved PSO algorithm is compared to several human-designed PSO algorithms by using ten artificially constructed functions and one real-world problem. Numerical experiments show that the Evolved PSO algorithm performs similarly and sometimes even better than the Standard approaches for the considered problems. The Evolved PSO is highly scalable (regarding the size of the problem's input), being able to solve problems having different dimensions. [ABSTRACT FROM AUTHOR]