Since the early 1990s, genetic programming (GP) a discipline whose goal is to enable the automatic generation of computer programs has emerged as one of the most promising paradigms for fast, productive software development. GP combines biological metaphors gleaned from Darwin's theory of evolution with computer-science approaches drawn from the field of machine learning t Since the early 1990s, genetic programming (GP) a discipline whose goal is to enable the automatic generation of computer programs has emerged as one of the most promising paradigms for fast, productive software development. GP combines biological metaphors gleaned from Darwin's theory of evolution with computer-science approaches drawn from the field of machine learning to create programs that are capable of adapting or recreating themselves for open-ended tasks. This unique introduction to GP provides a detailed overview of the subject and its antecedents, with extensive references to the published and online literature. In addition to explaining the fundamental theory and important algorithms, the text includes practical discussions covering a wealth of potential applications and real-world implementation techniques. Software professionals needing to understand and apply GP concepts will find this book an invaluable practical and theoretical guide.
Genetic Programming: An Introduction On The Automatic Evolution Of Computer Programs And Its Applications
Since the early 1990s, genetic programming (GP) a discipline whose goal is to enable the automatic generation of computer programs has emerged as one of the most promising paradigms for fast, productive software development. GP combines biological metaphors gleaned from Darwin's theory of evolution with computer-science approaches drawn from the field of machine learning t Since the early 1990s, genetic programming (GP) a discipline whose goal is to enable the automatic generation of computer programs has emerged as one of the most promising paradigms for fast, productive software development. GP combines biological metaphors gleaned from Darwin's theory of evolution with computer-science approaches drawn from the field of machine learning to create programs that are capable of adapting or recreating themselves for open-ended tasks. This unique introduction to GP provides a detailed overview of the subject and its antecedents, with extensive references to the published and online literature. In addition to explaining the fundamental theory and important algorithms, the text includes practical discussions covering a wealth of potential applications and real-world implementation techniques. Software professionals needing to understand and apply GP concepts will find this book an invaluable practical and theoretical guide.
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30 review for Genetic Programming: An Introduction On The Automatic Evolution Of Computer Programs And Its Applications
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Clemens Lode –
A very technical book which covers state-of-the-art (well, 1997) scientific knowledge about genetic programming. It's a great compilation of studies with many diagrams but it's not for the faint-hearted. Genetic programming is a field of Artificial Intelligence where the programmer (you?) does not try to solve the problem. Instead a simulation is created with which the AI trains itself. While there is no guarantee for achieving the best solution, it tends to come very close while taking very lit A very technical book which covers state-of-the-art (well, 1997) scientific knowledge about genetic programming. It's a great compilation of studies with many diagrams but it's not for the faint-hearted. Genetic programming is a field of Artificial Intelligence where the programmer (you?) does not try to solve the problem. Instead a simulation is created with which the AI trains itself. While there is no guarantee for achieving the best solution, it tends to come very close while taking very little time. Basically, it can solve any type of problem, as long as the problem can be formulated and typed into the computer.
Jacob Okoro –
Awesome read
Cosmin Gheorghita –
Bit too abstract. Too many ramifications spreading out in neural science, microbiology, DNA & RNA modelling, the theory of evolution. Every chapter points to at least 10 other books that have almost nothing to do with AI. I feel like it defeats the purpose of the book, it being about a specific technique of AI.
Joakim Ekblad –
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