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Genetic and Evolutionary Computation – GECCO 2004
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Fitness Distance Correlation in Structural Mutation Genetic Programming
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Difficulty of Unimodal and Multimodal Landscapes in Genetic Programming
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The density of states — A measure of the difficulty of optimisation problems
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What Makes a Problem Hard for a Genetic Algorithm? Some Anomalous Results and Their Explanation
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Foundations of Genetic Programming
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Genetic Programming IV: Routine Human-Competitive Machine Intelligence
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Correlated and uncorrelated fitness landscapes and how to tell the difference
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Concepts of inductive genetic programming
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Analyzing Deception in Trap Functions
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Predictive Models Using Fitness Distributions of Genetic Operators
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Genetic Algorithm Difficulty and the Modality of Fitness Landscapes
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The Neutral Theory of Molecular Evolution
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Fitness Landscapes and Evolvability
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Genetic algorithms in search, optimization, and machine learning
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Fitness Distributions: Tools for Designing Efficient Evolutionary Computations
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