Gene Expression Programming: A New Adaptive Algorithm for Solving Problems

By Cândida Ferreira. Complex Systems, 2001

2,355 citations recorded by the source corpus.

Gene expression programming, a genotype/phenotype genetic algorithm (linear and ramified), is presented here for the first time as a new technique for the creation of computer programs. Gene expression programming uses character linear chromosomes composed of genes structurally organized in a head and a tail. The chromosomes function as a genome and are subjected to modification by means of mutation, transposition, root transposition, gene transposition, gene recombination, and oneand two-point recombination. The chromosomes encode expression trees which are the object of selection. The creation of these separate entities (genome and expression tree) with distinct functions allows the algorithm to perform with high efficiency that greatly surpasses existing adaptive techniques. The suite of problems chosen to illustrate the power and versatility of gene expression programming includes symbolic regression, sequence induction with and without constant creation, block stacking, cellular automata rules for the density-classification problem, and two problems of boolean concept learning: the 11-multiplexer and the GP rule problem.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/b42cd49dc7d2dd2c50962118b57868d7626540cc

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Citations in the corpus

Global Optimization Algorithms -- Theory and Application (2009, background): “For instance, consider the grammar for mathematical expressions with the terminal symbols Σ = √· , *, /, -, +, a, b } given as example in [766].”
Modelling long‐term monthly temperatures by several data‐driven methods using geographical inputs (2015, methodology): “The mathematical modelling form of GEP was first introduced by Ferreira (2001a, 2006b).”
Applications of Gene Expression Programming and Regression Techniques for Estimating Compressive Strength of Bagasse Ash based Concrete (2020, background): “[39] based on the population evolutionary algorithm and is named gene expression programming (GEP).”
Gene expression models for prediction of longitudinal dispersion coefficient in streams (2015, methodology): “To express the genetic information encoded in the gene, Ferreira (2001) used expression tree (ET) representations.”
Evolutionary algorithms and their applications to engineering problems (2020, background): “Gene expression programming [27] Ferreira Chromosomes encode expression trees which are the object of selection 2001”