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. Keywords. Adaptive encoding, real-valued parameters, ARGOT, premature convergence, genetic hitchhiking Genetic Algorithms (GAs) are capable of efficiently searching complex fitness landscapes utilizing methods inspired by biological evolution (Holland, 1975) . The work reported here used the GAuscd 1 software which implements a popular form of the GA, applying the operators of selective reproduction, mutation and crossover to a fixed-length binary genome. While the common restriction to fixed-length genetic representations in GA experiments is rooted in convention and convenience theoretical arguments have been made in support of binary alphabets (Goldberg, 1989, pp. 80-82). If the function to be optimized by the GA is defined over a continuous domain, its realvalued parameters can only have approximate genotypic representations as binary codes of a given length. The choice of code length determines not only the resolution of the GA search for this gene and the precision of its result, 2 but also the size of the space of representations the GA has to search. Unfortunately the GA searches long place-value codes rather inefficiently: in the early phases of the search a lot of computational effort is invested in evaluating the least significant digits of the gene. The optimal value for these digits, however, will typically depend on the more significant ones, and little useful information can be obtained from sampling the former until the latter have converged to their optimal value. Likewise, once this has happened there is no need to spend any more search effort on the most significant digitsthe plain GA though will pay equal attention to all digits of the representation throughout its search, thus wasting its implicit parallelism 3 on an inherently sequential search problem. Moreover, in the absence of meaningful fitness information early in the search, the least significant bit positions will converge to an arbitrary value due to genetic drift, a process
The Dynamic Parameter Encoding REF (DPE) algorithm provides the ability to encode real-valued parameters of arbitrary precision.
Dynamic Parameter Encoding for Genetic Algorithms
Machine Learning
Machine Learning
Mathematics