The GP Bibliography

Genetic Programming Bibliography

The bibliography is part of the Collection of Computer Science Bibliographies, maintained and managed by W.B.Langdon and Tao Chen, and the single largest resource for Genetic Programming literature, research, and code review in the world. The Bibliography offers an extensive search 11,000+ entries.

Journals

Evolutionary Computation
Evolutionary Computation is an MIT Press Journal, for paper submission and review.

Genetic Programming and Evolvable Machines
Edited by Lee Spector, published through Springer

The journal of Genetic Programming and Evolvable Machines is devoted to reporting innovative and significant progress in automatic evolution of software and hardware. Methods for artificial evolution of active components, such as programs or machines, are rapidly developing branches of adaptive computation and adaptive engineering. They entail the development, evaluation and application of methods that mirror the process of neo-Darwinian evolution and produce as a result computational expressions such as algorithms or machines such as mechanical or electronic devices that actively process environmental information and transform their environment.

IEEE Transactions on Evolutionary Computation

The IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (TEVC), published six times a year, publishes archival quality original papers in evolutionary computation and related areas including nature-inspired algorithms, population-based methods, and optimization where selection and variation are integral, and hybrid systems where these paradigms are combined. Purely theoretical papers are considered as are application papers that provide general insights into these areas of computation.

Books

Genetic Programming: Theory and Practice
Edited by Rick Riolo, William P. Worzel, and Mark Kotanchek. 2003-current
Available from Amazon and Springer

The proceedings of the Genetic Programming Theory and Practice (GPTP) Workshop.

Cartesian Genetic Programming (Natural Computing Series)
by Julian F. Miller (Editor). 2011
Available from Amazon

Cartesian Genetic Programming (CGP) is a highly effective and increasingly popular form of genetic programming. It represents programs in the form of directed graphs, and a particular characteristic is that it has a highly redundant genotype–phenotype mapping, in that genes can be noncoding. It has spawned a number of new forms, each improving on the efficiency, among them modular, or embedded, CGP, and self-modifying CGP.

Automatic Quantum Computer Programming: A Genetic Programming Approach
by Lee Spector. 2010
Available from Amazon

Once realized, the potential of large-scale quantum computers promises to radically transform computer science. Despite large-scale international efforts, however, essential questions about the potential of quantum algorithms are still unanswered. Automatic Quantum Computer Programming is an introduction both to quantum computing for non-physicists and to genetic programming for non-computer-scientists. The book explores several ways in which genetic programming can support automatic quantum computer programming and presents detailed descriptions of specific techniques, along with several examples of their human-competitive performance on specific problems.

A Field Guide to Genetic Programming
by Riccardo Poli, William B. Langdon, and Nicholas F. McPhee with contributions by John R. Koza. 2008.
Available for free download

A Field Guide to Genetic Programming is an introduction to genetic programming (GP). GP is a systematic, domain-independent method for getting computers to solve problems automatically starting from a high-level statement of what needs to be done. Using ideas from natural evolution, GP starts from an ooze of random computer programs, and progressively refines them through processes of mutation and sexual recombination, until solutions emerge. All this without the user having to know or specify the form or structure of solutions in advance. GP has generated a plethora of human-competitive results and applications, including novel scientific discoveries and patentable inventions.

Linear Genetic Programming (Genetic and Evolutionary Computation)
by Markus F. Brameier, Wolfgang Banzhaf. 2007
Available from Amazon

Linear Genetic Programming presents a variant of Genetic Programming that evolves imperative computer programs as linear sequences of instructions, in contrast to the more traditional functional expressions or syntax trees. Typical GP phenomena, such as non-effective code, neutral variations, and code growth are investigated from the perspective of linear GP. This book serves as a reference for researchers; it includes sufficient introductory material for students and newcomers to the field.

Foundations of Genetic Programming
by W.B. Langdon and Riccardo Poli. 2002
Available from Amazon

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.

Automatic Re-engineering of Software Using Genetic Programming
by Conor Ryan. 2000
Available from Amazon

Automatic Re-engineering of Software Using Genetic Programming describes the application of Genetic Programming to a real world application area – software re-engineering in general and automatic parallelization specifically. Unlike most uses of Genetic Programming, this book evolves sequences of provable transformations rather than actual programs. It demonstrates that the benefits of this approach are twofold: first, the time required for evaluating a population is drastically reduced, and second, the transformations can subsequently be used to prove that the new program is functionally equivalent to the original.

Genetic Programming and Data Structures: Genetic Programming + Data Structures = Automatic Programming!
by W.B. Langdon. 1998
Available from Amazon

Computers that `program themselves’ has long been an aim of computer scientists. Recently genetic programming (GP) has started to show its promise by automatically evolving programs. Indeed in a small number of problems GP has evolved programs whose performance is similar to or even slightly better than that of programs written by people. The main thrust of GP has been to automatically create functions. While these can be of great use they contain no memory and relatively little work has addressed automatic creation of program code including stored data.

Genetic Programming: An Introduction
The Morgan Kaufmann Series in Artificial Intelligence
by Wolfgang Banzhaf, Peter Nordin, Robert E. Keller, Frank D. Francone. 1998
Available from Amazon

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.

Advances in Genetic Programming 3
by Spector, Lee, Langdon, William B., O’Reilly, Una-May, and Angeline, Peter (editors). 1999. Cambridge, MA: The MIT Press.
Available from Amazon

Advances in Genetic Programming 2
by Angeline, Peter J. and Kinnear, Kenneth E. Jr. (editors). 1996. Cambridge, MA: The MIT Press.
Available from Amazon

Advances in Genetic Programming
by Kinnear, Kenneth E. Jr. (editor). 1994. Cambridge, MA: The MIT Press.
Available from Amazon

Genetic Programming IV: Routine Human-Competitive Machine Intelligence
by John R. Koza, Martin A. Keane, Matthew J. Streeter, William Mydlowec, Jessen Yu, and Guido Lanza. 2003. MIT Press. Kluwer Academic Publishers.
Available from Springer

Genetic Programming III: Darwinian Invention and Problem Solving
by John R. Koza, Forrest H Bennett III, David Andre, and Martin A. Keane. 1999. Morgan Kaufmann Publishers.
Available from Amazon

Genetic Programming II: Automatic Discovery of Reusable Programs
by John Koza. 1994. MIT Press.
Available from Amazon

Genetic Programming: On the Programming of Computers by Means of Natural Selection
by John Koza. 1992. MIT Press.
Available from Amazon

For additional resources, visit Springer.com for conference proceedings, journals, and books.