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Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers, 2008
Cartesian Genetic Programming (CGP) is a well-known form of Genetic Programming developed by Julian Miller in 1999-2000. In its classic form, it uses a very simple integer address-based genetic representation of a program in the form of a directed graph. Graphs are very useful program representations and can be applied to many domains (e.g.
Julian Francis Miller, Simon L. Harding
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Cartesian Genetic Programming (CGP) is a well-known form of Genetic Programming developed by Julian Miller in 1999-2000. In its classic form, it uses a very simple integer address-based genetic representation of a program in the form of a directed graph. Graphs are very useful program representations and can be applied to many domains (e.g.
Julian Francis Miller, Simon L. Harding
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Introduction to genetic programming
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers, 2009Genetic programming (GP) is a collection of evolutionary computation techniques that allow computers to solve problems automatically. 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.
Riccardo Poli, Nicholas Freitag McPhee
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IEEE Expert, 1995
A major challenge in applying genetic programming to expert-system development is that the ubiquitous presence of irrelevant code makes a genetically induced program difficult to understand. The trait-mining technique extracts the expressions that comprise the program's salient problem elements. >
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A major challenge in applying genetic programming to expert-system development is that the ubiquitous presence of irrelevant code makes a genetically induced program difficult to understand. The trait-mining technique extracts the expressions that comprise the program's salient problem elements. >
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Polymorphism and Genetic Programming
2001Types have been introduced to Genetic Programming (GP) by researchers with different motivation. We present the concept of types in GP and introduce a typed GP system, PolyGP, that supports polymorphism through the use of three different kinds of type variable. We demonstrate the usefulness of this kind of polymorphism in GP by evolving two polymorphic
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Generalisation in genetic programming
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation, 2011Genetic programming can evolve large general solutions using a tiny fraction of possible fitness test sets. Just one test may be enough.
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Proceedings of the 8th annual conference on Genetic and evolutionary computation, 2006
A study on the performance of solutions generated by Genetic Programming (GP) when the training set is relaxed (in order to allow for a wider definition of the desired solution) is presented. This performance is assessed through 2 important features of a solution: its generalization error and its bloat, a common problem of GP individuals. We show how a
Luis E. Da Costa, Jacques-André Landry
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A study on the performance of solutions generated by Genetic Programming (GP) when the training set is relaxed (in order to allow for a wider definition of the desired solution) is presented. This performance is assessed through 2 important features of a solution: its generalization error and its bloat, a common problem of GP individuals. We show how a
Luis E. Da Costa, Jacques-André Landry
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AUTOMATIC PROGRAMMING AND PROGRAM MAINTENANCE WITH GENETIC PROGRAMMING
International Journal of Software Engineering and Knowledge Engineering, 1995Automatic programming is discussed in the context of software engineering. An approach to automatic programming is presented, which utilizes software engineering principles in the synthesis and maintenance of programs. As a simple demonstration, program-equivalent Turing machines are synthesized, encapsulated, reused, and maintained by genetic ...
Frederick E. Petry +1 more
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Accelerated Genetic Programming
2018Symbolic regression by the genetic programming is one of the options for obtaining a mathematical model for known data of output dependencies on inputs. Compared to neural networks (MLP), they can find a model in the form of a relatively simple mathematical relationship. The disadvantage is their computational difficulty.
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Genetic programming that ensures programs are original
2009 IEEE Congress on Evolutionary Computation, 2009Conventional genetic programming (GP) does not guarantee no revisits, i.e., a program may be generated for fitness evaluations more than one time. This is clearly wasteful in applications that involve expensive and/or time consuming fitness evaluations.
Shiu Yin Yuen, Shing Wa Leung
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Convolutional Genetic Programming
2019In recent years Convolutional Neural Networks (CNN) have come to dominate many machine learning tasks, specially those related to image analysis, such as object recognition. Herein we explore the possibility of developing image denoising filters by stacking multiple Genetic Programming (GP) syntax trees, in a similar fashion to how CNNs are designed ...
Lino Alberto RodrÃguez Coayahuitl +2 more
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