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Genetic Systems Programming: Theory and Experiences (Studies in Computational Intelligence)

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Designing complex programs such as operating systems, compilers, filing systems, data base systems, etc. is an old ever lasting research area. Genetic programming is a relatively new promising and growing research area. Among other uses, it provides efficient tools to deal with hard problems by evolving creative and competitive solutions. Systems Programming is generally strewn with such hard problems. This book is devoted to reporting innovative and significant progress about the contribution of genetic programming in systems programming. The contributions of this book clearly demonstrate that genetic programming is very effective in solving hard and yet-open problems in systems programming. Followed by an introductory chapter, in the remaining contributed chapters, the reader can easily learn about systems where genetic programming can be applied successfully. These include but are not limited to, information security systems, compilers, data mining systems, stock market prediction systems, robots and automatic programming.

When the genetic algorithm first appeared in the 1960s and 1970s, it was an academic curiosity that was primarily useful in understanding certain aspects of how evolution worked in nature. In the 1980s, in tandem with the increased availability of computing power, practical applications of genetic and evolutionary computation first began to appear in specialized fields. In the 1990s, the relentless iteration of Moore’s law – which tracks the 100-fold increase in computational power every 10 years – enabled genetic and evolutionary computation to deliver the first results that were comparable and competitive with the work of creative humans. As can be seen from the preface and table of contents, the field has already begun the 21st century with a cornucopia of applications, as well as additions to the methodology and theory, including applications to information security systems, compilers, data mining systems, stock market prediction systems, robotics, and automatic programming.

Looking forward three decades, there will be a 1,000,000-fold increase in computational power. Given the impressive human-competitive results already delivered by genetic programming and other techniques of evolutionary computation, the best is yet to come.
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