Genetic Programming
  • Language: en
  • Pages: 819

Genetic Programming

  • Type: Book
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  • Published: 1992
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  • Publisher: MIT Press

Genetic programming may be more powerful than neural networks and other machinelearning techniques, able to solve problems in a wider range of disciplines. In this ground-breakingbook, John Koza shows how this remarkable paradigm works and provides substantial empirical evidencethat solutions to a great variety of problems from many different fields can be found by geneticallybreeding populations of computer programs. Genetic Programming contains a great many worked examplesand includes a sample computer code that will allow readers to run their own programs.In gettingcomputers to solve problems without being explicitly programmed, Koza stresses two points: thatseemingly different problems f...

Genetic Programming IV
  • Language: en
  • Pages: 590

Genetic Programming IV

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

Genetic Programming III
  • Language: en
  • Pages: 1154

Genetic Programming III

Genetic programming 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, optimal control, classification, system identification, function learning, and computational molecular biology. Among the solutions are 14 results competitive with human-produced results, including 10 rediscoveries of previously patented inventions. Researchers in artificial intelligence, machine learning, evolutionary computation, and genetic algorithms will find this an essential reference to the most recent and most important results in the rapidly growin...

Genetic Programming II
  • Language: en
  • Pages: 746

Genetic Programming II

Background on genetic algorithms, LISP, and genetic programming. Hierarchical problem-solving. Introduction to automatically defined functions: the two-boxes problem. Problems that straddle the breakeven point for computational effort. Boolean parity functions. Determining the architecture of the program. The lawnmower problem. The bumblebee problem. The increasing benefits of ADFs as problems are scaled up. Finding an impulse response function. Artificial ant on the San Mateo trail. Obstacle-avoiding robot. The minesweeper problem. Automatic discovery of detectors for letter recognition. Flushes and four-of-a-kinds in a pinochle deck. Introduction to biochemistry and molecular biology. Pred...

Artificial Life at Stanford, 1994
  • Language: en

Artificial Life at Stanford, 1994

  • Type: Book
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  • Published: 1994
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  • Publisher: Unknown

None

Every Vote Equal
  • Language: en
  • Pages: 1059

Every Vote Equal

  • Type: Book
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  • Published: 2013
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  • Publisher: Unknown

None

Foundations of Genetic Programming
  • Language: en
  • Pages: 260

Foundations of Genetic Programming

This is one of the only books to provide a complete and coherent review of the theory of genetic programming (GP). In doing so, it provides a coherent consolidation of recent work on the theoretical foundations of GP. A concise introduction to GP and genetic algorithms (GA) is followed by a discussion of fitness landscapes and other theoretical approaches to natural and artificial evolution. Having surveyed early approaches to GP theory it presents new exact schema analysis, showing that it applies to GP as well as to the simpler GAs. New results on the potentially infinite number of possible programs are followed by two chapters applying these new techniques.

Evolutionary Computing
  • Language: en
  • Pages: 314

Evolutionary Computing

This book constitutes the refereed post-workshop proceedings of the AISB International Workshop on Evolutionary Computing, held in Manchester, UK, in April 1997. The 22 strictly reviewed and revised full papers presented were selected for inclusion in the book after two rounds of refereeing. The papers are organized in sections on evolutionary approaches to issues in biology and economics, problem structure and finite landscapes, evolutionary machine learning and classifier systems, evolutionary scheduling, and more techniques and applications of evolutionary algorithms.

Philosophy and Simulation
  • Language: en
  • Pages: 240

Philosophy and Simulation

In his new book, the internationally renowned Manuel DeLanda provides a remarkably clear philosophical overview of the rapidly growing field of computer simulations.

Parameter Setting in Evolutionary Algorithms
  • Language: en
  • Pages: 318

Parameter Setting in Evolutionary Algorithms

One of the main difficulties of applying an evolutionary algorithm (or, as a matter of fact, any heuristic method) to a given problem is to decide on an appropriate set of parameter values. Typically these are specified before the algorithm is run and include population size, selection rate, operator probabilities, not to mention the representation and the operators themselves. This book gives the reader a solid perspective on the different approaches that have been proposed to automate control of these parameters as well as understanding their interactions. The book covers a broad area of evolutionary computation, including genetic algorithms, evolution strategies, genetic programming, estimation of distribution algorithms, and also discusses the issues of specific parameters used in parallel implementations, multi-objective evolutionary algorithms, and practical consideration for real-world applications. It is a recommended read for researchers and practitioners of evolutionary computation and heuristic methods.