Genetic Programming Iii Darwinian Invention And Problem Solving
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Genetic Programming III
Genetic programming (GP) is a method for getting a computer to solve a problem by telling it what needs to be done instead of how to do it. Koza, Bennett, Andre, and Keane present genetically evolved solutions to dozens of problems of design, control, classification, system identification, and computational molecular biology. Among the solutions are 14 results competitive with human-produced results, including 10 rediscoveries of previously patented inventions.
Genetic Programming III
This 45-minute videotape surveys the main points of the book Genetic Programming III: Darwinian Invention and Problem Solving and presents genetically evolved solutions to problems of analog electrical circuit design, optimal control, classification, system identification points, function learning, and computational molecular biology. Fourteen of the results are competitive with previously published human-produced results and ten infringe on previously issued patents or duplicate the functionality of previously issued patents.
Genetic Programming IV
Author: John R. Koza
language: en
Publisher: Springer Science & Business Media
Release Date: 2005-03-21
Genetic Programming IV: Routine Human-Competitive Machine Intelligence presents the application of GP to a wide variety of problems involving automated synthesis of controllers, circuits, antennas, genetic networks, and metabolic pathways. The book describes fifteen instances where GP has created an entity that either infringes or duplicates the functionality of a previously patented 20th-century invention, six instances where it has done the same with respect to post-2000 patented inventions, two instances where GP has created a patentable new invention, and thirteen other human-competitive results. The book additionally establishes: GP now delivers routine human-competitive machine intelligence GP is an automated invention machine GP can create general solutions to problems in the form of parameterized topologies GP has delivered qualitatively more substantial results in synchrony with the relentless iteration of Moore's Law