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Genetic programming: on the programming of computers by means of natural selectionDecember 1992
- Author:
- John R. Koza
Abstract
No abstract available.
Cited By
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- Mekhaznia T Nature inspired heuristics for attack of simplified DES algorithm Proceedings of the 6th International Conference on Security of Information and Networks, (311-315)
- Assis C, Pereira A, Pereira M and Carrano E Using genetic programming to detect fraud in electronic transactions Proceedings of the 19th Brazilian symposium on Multimedia and the web, (337-340)
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- Pereira Nunes B, Mera A, Casanova M, Fetahu B, P. Paes Leme L and Dietze S Complex Matching of RDF Datatype Properties Proceedings of the 24th International Conference on Database and Expert Systems Applications - Volume 8055, (195-208)
- Ghannem A, Boussaidi G and Kessentini M Model Refactoring Using Interactive Genetic Algorithm Proceedings of the 5th International Symposium on Search Based Software Engineering - Volume 8084, (96-110)
- Mahouachi R, Kessentini M and Cinnéide M Search-Based Refactoring Detection Using Software Metrics Variation Proceedings of the 5th International Symposium on Search Based Software Engineering - Volume 8084, (126-140)
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- Pham D, Castellani M and Le Thi H Nature-Inspired Intelligent Optimisation Using the Bees Algorithm Transactions on Computational Intelligence XIII - Volume 8342, (38-69)
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- Rivers R and Tauritz D Evolving black-box search algorithms employing genetic programming Proceedings of the 15th annual conference companion on Genetic and evolutionary computation, (1497-1504)
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- Burlacu B, Affenzeller M, Kommenda M, Winkler S and Kronberger G Visualization of genetic lineages and inheritance information in genetic programming Proceedings of the 15th annual conference companion on Genetic and evolutionary computation, (1351-1358)
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- Iqbal M, Browne W and Zhang M Comparison of two methods for computing action values in XCS with code-fragment actions Proceedings of the 15th annual conference companion on Genetic and evolutionary computation, (1235-1242)
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- Sim K and Hart E Generating single and multiple cooperative heuristics for the one dimensional bin packing problem using a single node genetic programming island model Proceedings of the 15th annual conference on Genetic and evolutionary computation, (1549-1556)
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- Neshatian K and Varn L On the Optimality of Subsets of Features Selected by Heuristic and Hyper-heuristic Approaches Revised Selected Papers of PAKDD 2013 International Workshops on Trends and Applications in Knowledge Discovery and Data Mining - Volume 7867, (440-451)
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- Mei Y and Zhang M A comprehensive analysis on reusability of GP-evolved job shop dispatching rules 2016 IEEE Congress on Evolutionary Computation (CEC), (3590-3597)
- Iqbal M, Zhang M and Xue B Improving classification on images by extracting and transferring knowledge in genetic programming 2016 IEEE Congress on Evolutionary Computation (CEC), (3582-3589)
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- Awuley A and Ross B Feature selection and classification using age layered population structure genetic programming 2016 IEEE Congress on Evolutionary Computation (CEC), (2417-2426)
- Le N, Xuan H, Brabazon A and Thi T Complexity measures in Genetic Programming learning: A brief review 2016 IEEE Congress on Evolutionary Computation (CEC), (2409-2416)
- Naqvi S and Browne W Adapting learning classifier systems to symbolic regression 2016 IEEE Congress on Evolutionary Computation (CEC), (2209-2216)
- Ashlock D and Brown J Evolutionary partitioning regression with function stacks 2016 IEEE Congress on Evolutionary Computation (CEC), (1469-1476)
- Rahati A and Rakhshani H A gene expression programming framework for evolutionary design of metaheuristic algorithms 2016 IEEE Congress on Evolutionary Computation (CEC), (1445-1452)
- Kelly J and Jacob C evoVersion: Visualizing evolutionary histories 2016 IEEE Congress on Evolutionary Computation (CEC), (814-821)
- Hughes J, Brown J and Khan A Smartphone gait fingerprinting models via genetic programming 2016 IEEE Congress on Evolutionary Computation (CEC), (408-415)
- Masood A, Mei Y, Chen G and Zhang M Many-objective genetic programming for job-shop scheduling 2016 IEEE Congress on Evolutionary Computation (CEC), (209-216)
- Yan L, Mei Y, Ma H and Zhang M Evolutionary web service composition: A graph-based memetic algorithm 2016 IEEE Congress on Evolutionary Computation (CEC), (201-208)
- Heravi A and Houghten S A methodology for disease gene association using centrality measures 2016 IEEE Congress on Evolutionary Computation (CEC), (24-31)
- de Melo V Breast cancer detection with logistic regression improved by features constructed by Kaizen programming in a hybrid approach 2016 IEEE Congress on Evolutionary Computation (CEC), (16-23)
- Büttner T, Roennau A, Heppner G, Pfotzer L and Dillmann R Bio-inspired optimization of kinematic models for multi-legged walking robots 2016 6th IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob), (430-435)
Index Terms
- Genetic programming: on the programming of computers by means of natural selection
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Genetic programming (book) One main idea—the generation of programs by massive trial and error—is developed in this book. Koza begins with a set of random programs in generation zero. He uses the analogy of biological evolution, so the most successful programs have an increased chance to reproduce and recombine in each succeeding generation. The author uses a standard format whereby the programs consist of a set of inputs and a set of functions capable of being applied hierarchically in a LISP-like composition. The inputs and functions vary according to the specific application, but the programs can always be viewed as tree structures. This structure allows genetic recombination to exchange intact subtrees. Thus, meaningful offspring are likely. The above method is applied to dozens of problems in a wide variety of areas. None of the examples realizes a useful way to perform a useful task, however. The difficulty lies in the difference between genetic programming and natural selection as it occurs in nature. Koza's examples all need explicit detailed fitness measures, which often are already program solutions. In any case, determining fitness for all the programs of every generation is the most time-consuming and limiting factor of this approach. The first four chapters are introductory, giving background material on machine learning and genetic algorithms. Chapter 5 describes genetic programming. The details are left for chapter 6, which covers such questions as how the fitness measure is normalized. Chapters 7 through 9 give some examples and compute statistics on population sizes for some tasks. Chapters 10 to 23 each introduce a new form of application, with some detailed examples. Chapter 10 covers error-driven evolution. Chapter 11 discusses cost-driven evolution. Chapter 12 discusses behavior, using the example of artificial ant feeding. Chapter 13 addresses subsumption, with the example of a robot finding and pushing a box. Chapter 14 considers entropy, including random number generation. Chapter 15 is about strategy and considers the case of minimax games. Chapter 16, on coevolution, studies both-sides-minimax games. Chapter 17 covers classification, including distinguishing two spirals. Chapter 18 describes iteration, using sequence finding as an example. Chapter 19 presents constraint syntax. Chapter 20 covers building blocks. Chapter 21 discusses hierarchy, with the example of parity functions. Chapter 22 addresses parallelization. Chapter 23, on ruggedness, uses the example of a biathlon. The biathlon example alternates the fitness measure so the same program learns two endpoints—symbolic regression and artificial ant feeding, depending on interpretation . In the remaining chapters, the author discusses extraneous inputs and functions, considers some other issues, and compares genetic programming with other artificial intelligence paradigms. A long chapter is devoted to spontaneous generation of self-replicating programs. The implementation details are presented in a set of appendices occupying 88 pages. The purpose of this book is to convince the reader of the importance of genetic programming by the multitude and variety of applications. It fails because none of the examples rises to the level of actual utility. We have reached the stage where we expect our artificial intelligence products to be competitive. The best feature of this work is the structure-preserving crossover resulting from the tree structure of the programs. We need to look for such mechanisms to contain meaningful blocks in biological recombination. Genetic programming (videotape) The one-hour video covers the same ground as the book without much of the background and details. It is especially appropriate in presenting the tree structure of the programs with crossover as an exchange of subtrees. About 20 of the examples from the book are covered. Many of these examples, such as artificial ant feeding, provide entertaining animation. The function hierarchy capability applied to the 11-parity function is explained well. The videotape provides a fast, painless way to learn genetic programming. I recommend it to those who remain curious about genetic programming after reading the above book review.
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