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Presents an introduction both to quantum computing for non-physicists and to genetic programming for non-computer-scientists. This book explores ways in which genetic programming can support automatic quantum computer programming and offers descriptions of specific techniques, along with several examples of their human-competitive performance.

Quantum computers. --- Automatic programming (Computer science) --- Genetic programming (Computer science)

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This is the first book that attempts to provide a framework in which to embed an automatic programming system based on evolutionary learning (genetic programming) into a traditional software engineering environment. As such, it looks at how traditional software engineering knowledge can be integrated with an evolutionary programming process in a symbiotic way.

Computer software --- Genetic programming (Computer science) --- Computer programming --- Genetic algorithms --- Evaluation. --- Quality control.

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Genetic programming (Computer science) --- Computer programming --- Genetic algorithms --- Programació genètica (Informàtica)

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Hypertension and its resultant complications do occur in childhood and track into adulthood. It’s estimated that > 3% of all children have hypertension, with an even greater prevalence among obese children (20-47%). The etiology of hypertension is generally described as primary (essential) or secondary with most secondary causes related to cardio-renal disease. While primary hypertension is on the rise, all children should undergo an evaluation to investigate for a secondary cause of their hypertension. Mild to moderate hypertension is most commonly asymptomatic but may be associated with subtle cardiac, renal, neurological and/or psychosocial.

genetic programming --- pheochromocytoma --- developmental origins --- kidney transplant --- microbiome --- obesity --- LVH --- paraganglioma

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Genetic Algorithms (GAs) are one of several techniques in the family of Evolutionary Algorithms - algorithms that search for solutions to optimization problems by "evolving" better and better solutions. Genetic Algorithms have been applied in science, engineering, business and social sciences. This book consists of 16 chapters organized into five sections. The first section deals with some applications in automatic control, the second section contains several applications in scheduling of resources, and the third section introduces some applications in electrical and electronics engineering. The next section illustrates some examples of character recognition and multi-criteria classification, and the last one deals with trading systems. These evolutionary techniques may be useful to engineers and scientists in various fields of specialization, who need some optimization techniques in their work and who may be using Genetic Algorithms in their applications for the first time. These applications may be useful to many other people who are getting familiar with the subject of Genetic Algorithms.

Genetic algorithms. --- GAs (Algorithms) --- Genetic searches (Algorithms) --- Algorithms --- Combinatorial optimization --- Evolutionary computation --- Genetic programming (Computer science) --- Learning classifier systems --- Artificial intelligence