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In this paper, we design a hierarchical execution architecture of genetic algorithms (GAs). We present a variant of genetic algorithms known as Hierarchical GA (HGA) based on the proposed architecture. In HGA, the population is divided into several sub-populations, which are initially placed at the lowest level. When the GA process at the level terminates, the GA process goes up to the next level...
Multi-population-based bio-inspired computation may use migration among groups to increase the search diversity. Through good solutions exchanged among sub-populations, better solutions may be found with a high probability. In this paper, we propose two algorithms to dynamically adjust the two primary parameters, migration interval and migration rate, for flexibly reflect solution situation for effective...
Multipopulation Genetic Algorithms (MGAs) are island model genetic algorithms composed of spatially semi-isolated sub-populations, each evolving in parallel by its own pace and occasionally interacting with its neighborhoods by interchanging (usually good) individuals, called migration. Since the migration process is the kernel mechanism of MGAs for preventing premature convergence, many previous...
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