Sophisticated search techniques form the backbone of modern machine learning and data analysis. Computer systems that are able to extract information from huge data sets (data mining), to recognize patterns, to do classification, or to suggest diagnoses, in short, systems that are adaptive and — to some extent — able to learn, fundamentally rely on effective and efficient search techniques. The ability of organisms to learn and adapt to signals from their environment is one of the core features of life. Technically, any adaptive system needs some kind of search operator in order to explore a feature space which describes all possible configurations of the system. Usually, one is interested in “optimal” or at least close to “optimal” configurations defined with respect to a specific application domain: the weight settings of a neural network for correct classification of some data, parameters that describe the body shape of an airplane with minimum drag, a sequence of jobs assigned to a flexible production line in a factory resulting in minimum idle time for the machine park, the configuration for a stable bridge with minimum weight or minimum cost to build and maintain, or a set of computer programs that implement a robot control task with a minimum number of commands.
KeywordsGenetic Algorithm Genetic Programming Evolution Strategy Strategy Parameter Metropolis Algorithm
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