Gene expression programming

Abstract: Introduction: The Biological Perspective.- The Entities of Gene Expression Programming.- The Basic Gene Expression Algorithm.- The Basic GEA in Problem Solving.- Numerical Constants and the GEP-RNC Algorithm.- Automatically Defined Functions in Problem Solving.- Polynomial Induction and Time Series Prediction.- Parameter Optimization.- Decision Tree Induction.- Design of Neural Networks.- Combinatorial Optimization.- Evolutionary Studies.

Abstract: Gene expression programming is a full fledged genotype/phenotype system that evolves computer programs encoded in linear chromosomes of fixed length. The structural organization of the linear chromosomes allows the unconstrained and fruitful (in the sense that no invalid phenotypes will follow) operation of important genetic operators such as mutation, transposition, and recombination as the expression of each gene results always in valid programs. Although simple, the genotype/phenotype system of gene expression programming is the first artificial genotype/phenotype system with a complex and sounding translation mechanism. Indeed, the interplay between genotype (chromosomes) and phenotype (expression trees) is at the core of the tremendous increase in performance observed in gene expression programming. Furthermore, gene expression programming shares with genetic programming the same kind of tree representation and, therefore, with GEP it is possible, for one thing, to retrace easily the steps undertaken by genetic programming and, for another, to explore easily new frontiers opened up by the crossing of the phenotype threshold. In this tutorial, the fundamental differences between gene expression programming and its predecessors, genetic algorithms and genetic programming, are briefly summarized so that the evolutionary advantages of gene expression programming could be better understood. The work proceeds with a detailed description of the main players in this new algorithm, focusing mainly on the interactions between them and how the simple yet revolutionary structure of the chromosomes allows the efficient, unconstrained exploration of the search space.

Abstract: A comparison between four Genetic Programming techniques 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, closely followed by Linear Genetic Programming.