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C. FERREIRA Complex Systems, 13 (2): 87-129, 2001 Gene Expression Programming: A New Adaptive Algorithm for Solving Problems Table of Contents   Abstract 1. Introduction 2. An Overview of Gene Expression Algorithms 3. The Genome of Gene Expression Programming Individuals 3.1. Open Reading Frames and Genes 3.2. Gene Expression Programming Genes 3.3. Multigenic Chromosomes 3.4. Expression Trees and the Phenotype 3.4.1. Information Decoding: Translation 3.4.2. Interactions of Sub-expression Trees 4. Fitness Functions and Selection 4.1. Fitness Functions 4.2. Selection 5. Reproduction with Modification 5.1. Replication 5.2. Mutation 5.3. Transposition and Insertion Sequence Elements 5.3.1. Transposition of Insertion Sequence Elements 5.3.2. Root Transposition 5.3.3. Gene Transposition 5.4. Recombination 5.4.1. One-point Recombination 5.4.2. Two-point Recombination 5.4.3. Gene Recombination 6. Six Examples of Gene Expression Programming in Problem Solving 6.1. Symbolic Regression 6.2. Sequence Induction and the Creation of Constants 6.3. Block Stacking 6.4. Evolving Cellular Automata Rules for the Density-classification Problem 6.4.1. The Density-classification Task 6.4.2. Two Gene Expression Programming Discovered Rules 6.5. Boolean Concept Learning 6.5.1. The Genetic Programming Rule Problem 6.5.2. The 11-multiplexer Problem 7. Conclusions Acknowledgments References Home | Next