In this chapter an artificial problem solver inspired in natural genotype/phenotype systems – gene expression programming – is presented. As an introduction, 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 are better understood. The work proceeds with a detailed description of the architecture of the main players of this new algorithm (chromosomes and expression trees), 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. And finally, the chapter closes with an advanced application in which gene expression programming is used to evolve computer programs for diagnosing breast cancer. 1. Evolutionary Algorithms in Problem Solving The way nature solves problems and creates complexity has inspired scientists to create artificial systems that learn by themselves how to solve a particular problem. The first attempts were done in the 1950s by Friedberg (Friedberg 1958; Friedberg et al. 1959), bu
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