Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A New Interpretation of Schema Notation that Overtums the Binary Encoding Constraint
Proceedings of the 3rd International Conference on Genetic Algorithms
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The Schemata Theory analyzes the effect of the selection process, mutation and crossover over the number of individuals that belong to a given schema, within generations. This analysis considers, in its original form, the binary coding and operators. In this article, we present an analogous study, focusing on the real number coding and arthmetical operators. Unfortunately, the conventional schema definition is tightly dependent on discrete alphabets. Therefore, following a generalization of the concept of schema, we present a particular definition that suits better the continuous domain. Using this new definition, we reach an expression similar to the Fundamental Theorem of Genetic Algorithms [6] valid for the real coding of chromosomes.