Abstract
The common use of static binary place-value codes for real-valued parameters of the phenotype in Holland's genetic algorithm (GA) forces either the sacrifice of representational precision for efficiency of search or vice versa. Dynamic Parameter Encoding (DPE) is a mechanism that avoids this dilemma by using convergence statistics derived from the GA population to adaptively control the mapping from fixed-length binary genes to real values. DPE is shown to be empirically effective and amenable to analysis; we explore the problem of premature convergence in GAs through two convergence models.
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Schraudolph, N.N., Belew, R.K. Dynamic Parameter Encoding for Genetic Algorithms. Machine Learning 9, 9–21 (1992). https://doi.org/10.1023/A:1022624728869
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DOI: https://doi.org/10.1023/A:1022624728869