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- W2300651777 abstract "Genetic algorithms are a stochastic search method based on processes observed in nature such as natural selection, genetic recombination, and mutation. Genetic algorithms have been shown to be effective for solving optimization problems with large numbers of local optima and complex non-linear interactions. Genetic algorithms globally sample the search space and exploit statistical information taken from a database of encoded strings to allocate reproductive opportunities and infer new information about the search space. The resulting selective pressure directs the search toward partitions of the function space that contain above average solutions. However, this information feedback can cause the simple genetic algorithm to converge on sub-optimal solutions.The underlying mechanisms of two iterative genetic search strategies are examined and analyzed. These strategies search for better representations of the function space and maintain genetic diversity by reinitializing the genetic population periodically. The performances of these algorithms are compared with that of several other search algorithms using a suite of standard test functions, noisy test functions and real-world problems. One real-world problem involves geophysical data imaging characterized by a very large search space (2$sp{6000}$).Delta coding, an iterative genetic search strategy, sustains search by reinitializing the population when diversity has been adequately exploited and explores a new mapping of hyperspace with each reinitialization. Delta coding improves genetic search performance on many standard optimization functions, effectively solving these problems by locating an easier mapping of the function spaces. Several issues associated with search space representation are examined.Canonical delta folding, also an iterative genetic search algorithm, provides alternative mechanisms for changing the mapping of a search space via heuristics. Empirical evidence suggests that these heuristics are difficult to apply adaptively. Analysis also indicates that genetic search using canonical delta folding often becomes trapped in sub-optimal partitions of the search space.Additionally, new evidence is provided that standard test suites used for genetic algorithm performance comparisons may be inadequate. A simple stochastic hill-climbing algorithm is shown to solve most of these test functions when a Gray coding representation is used and also performs better than many genetic algorithms on these problems." @default.
- W2300651777 created "2016-06-24" @default.
- W2300651777 creator A5077277516 @default.
- W2300651777 date "1995-11-20" @default.
- W2300651777 modified "2023-09-28" @default.
- W2300651777 title "Remapping hyperspace and subpartition sampling in iterative genetic search" @default.
- W2300651777 hasPublicationYear "1995" @default.
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