An analysis of automatic subroutine discovery in genetic programming (1999)

by A Dessi, A Giani, A Starita
Venue:in Proceedings of the Genetic and Evolutionary Computation Conference
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Evolution and Acquisition of Modules in Cartesian Genetic Programming

by James Alfred Walker, Julian Francis Miller - In Proc. of the 7th European Conference on Genetic Programming, volume 3003 of LNCS , 2004
"... Abstract. The paper presents for the first time automatic module acquisition and evolution within the graph based Cartesian Genetic Programming method. The method has been tested on a set of even parity problems and compared with Cartesian Genetic Programming without modules. Results are given that ..."
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Abstract. The paper presents for the first time automatic module acquisition and evolution within the graph based Cartesian Genetic Programming method. The method has been tested on a set of even parity problems and compared with Cartesian Genetic Programming without modules. Results are given that show that the new modular method evolves solutions up to 20 times quicker than the original non-modular method and that the speedup is more pronounced on larger problems. Analysis of some of the evolved modules shows that often they are lower order parity functions. Prospects for further improvement of the method are discussed. 1
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An Analysis of the Impact of Functional Programming Techniques on Genetic Programming

by Gwoing Tina Yu , 1999
"... Genetic Programming (GP) automatically generates computer programs to solve specified problems. It develops programs through the process of a “create-test-modify ” cycle which is similar to the way a human writes programs. There are various functional programming tech-niques that human programmers c ..."
Abstract - Cited by 11 (0 self) - Add to MetaCart
Genetic Programming (GP) automatically generates computer programs to solve specified problems. It develops programs through the process of a “create-test-modify ” cycle which is similar to the way a human writes programs. There are various functional programming tech-niques that human programmers can use to accelerate the program development process. This research investigated the applicability of some of the functional techniques to GP and ana-lyzed their impact on GP performance. Among many important functional techniques, three were chosen to be included in this research, due to their relevance to GP. They are polymorphism, implicit recursion and higher-order functions. To demonstrate their applicability, a GP system was developed with those techniques incorporated. Furthermore, a number of experiments were conducted using the system. The results were then compared to those generated by other GP systems which do not support these functional features. Finally, the program search space of the general even-parity problem was analyzed to explain how these techniques impact GP performance. The experimental results showed that the investigated functional techniques have made GP more powerful in the following ways: 1) polymorphism has enabled GP to solve problems that are very difficult for standard GP to solve, i.e. nth and map programs; 2) higher-order functions and implicit recursion have enhanced GP’s ability in solving the general even-parity problem to a greater degree than with any other known methods. Moreover, the analysis showed that these techniques directed GP to generate program solutions in a way that has never been previously reported. Finally, we provide the guidelines for the application of these techniques to other problems.

Improving the Evolvability of Digital Multipliers Using Embedded Cartesian Genetic Programming and Product Reduction

by James Alfred Walker, Julian Francis Miller - Proceedings of 6th International Conference in Evolvable Systems. Springer, LNCS 3637 , 2005
"... Abstract. Embedded Cartesian Genetic Programming (ECGP) is a form of Ge-netic Programming based on an acyclic directed graph representation. In this paper we investigate the use of ECGP together with a technique called Product Reduction (PR) to reduce the time required to evolve a digital multiplier ..."
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Abstract. Embedded Cartesian Genetic Programming (ECGP) is a form of Ge-netic Programming based on an acyclic directed graph representation. In this paper we investigate the use of ECGP together with a technique called Product Reduction (PR) to reduce the time required to evolve a digital multiplier. The results are compared with Cartesian Genetic Programming (CGP) with and without PR and show that ECGP improves evolvability and also that PR im-proves the performance of both techniques by up to eight times on the digital multiplier problems tested. 1
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Investigating the performance of module acquisition in cartesian genetic programming

by James Alfred Walker - In Proc. of the 2005 Genetic and Evolutionary Computation Conference , 2005
"... Embedded Cartesian Genetic Programming (ECGP) is a form of the graph based Cartesian Genetic Programming (CGP) in which modules are automatically acquired and evolved. In this paper we compare the efficiencies of the ECGP and CGP techniques on three classes of problem: digital adders, digital multip ..."
Abstract - Cited by 8 (5 self) - Add to MetaCart
Embedded Cartesian Genetic Programming (ECGP) is a form of the graph based Cartesian Genetic Programming (CGP) in which modules are automatically acquired and evolved. In this paper we compare the efficiencies of the ECGP and CGP techniques on three classes of problem: digital adders, digital multipliers and digital comparators. We show that in most cases ECGP shows a substantial improvement in performance over CGP and that the computational speedup is more pronounced on larger problems.
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Embedded cartesian genetic programming and the lawnmower and hierarchical-if-and-only-if problems

by James Alfred Walker - In Proc. of GECCO. ACM , 2006
"... Embedded Cartesian Genetic Programming (ECGP) is an extension of the directed graph based Cartesian Genetic Programming (CGP), which is capable of automatically acquiring, evolving and re-using partial solutions in the form of modules. In this paper, we apply for the first time, CGP and ECGP to the ..."
Abstract - Cited by 6 (5 self) - Add to MetaCart
Embedded Cartesian Genetic Programming (ECGP) is an extension of the directed graph based Cartesian Genetic Programming (CGP), which is capable of automatically acquiring, evolving and re-using partial solutions in the form of modules. In this paper, we apply for the first time, CGP and ECGP to the well known Lawnmower problem and to the Hierarchical-if-and-Only-if problem. The latter is normally associated with Genetic Algorithms. Computational effort figures are calculated from the results of both CGP and ECGP and our results compare favourably with other techniques.
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Genetic Programming Track

by James Alfred Walker, Julian Francis Miller, Rachel Cavill
"... Embedded Cartesian Genetic Programming (ECGP) is an extension of Cartesian Genetic Programming (CGP) that can automatically acquire, evolve and re-use partial solutions in the form of modules. In this paper, we introduce for the first time a new multi-chromosome approach to CGP and ECGP that allows ..."
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Embedded Cartesian Genetic Programming (ECGP) is an extension of Cartesian Genetic Programming (CGP) that can automatically acquire, evolve and re-use partial solutions in the form of modules. In this paper, we introduce for the first time a new multi-chromosome approach to CGP and ECGP that allows difficult problems with multiple outputs to be broken down into many smaller, simpler problems with single outputs, whilst still encoding the entire solution in a single genotype. We also propose a multi-chromosome evolutionary strategy which selects the best chromosomes from the entire population to form the new fittest individual, which may not have been present in the population. The multi-chromosome approach to CGP and ECGP is tested on a number of multiple output digital circuits. Computational Effort figures are calculated for each problem and compared against those for CGP and ECGP. The results indicate that the use of multiple chromosomes in both CGP and ECGP provide a significant performance increase on all problems tested.
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Improving the Evolvability of Digital Multipliers using Embedded Cartesian Genetic Programming and Product Reduction

by James Alfred, Walker Julian, Francis Miller
"... Abstract. Embedded Cartesian Genetic Programming (ECGP) is a form of Genetic Programming based on an acyclic directed graph representation. In this paper we investigate the use of ECGP together with a technique called Product Reduction (PR) to reduce the time required to evolve a digital multiplier. ..."
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Abstract. Embedded Cartesian Genetic Programming (ECGP) is a form of Genetic Programming based on an acyclic directed graph representation. In this paper we investigate the use of ECGP together with a technique called Product Reduction (PR) to reduce the time required to evolve a digital multiplier. The results are compared with Cartesian Genetic Programming (CGP) with and without PR and show that ECGP improves evolvability and also that PR improves the performance of both techniques by up to eight times on the digital multiplier problems tested. 1
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Context-Aware Mutati on: A Modul ar, C ontext Aware Mutation Operator for Genetic Programming ABSTRACT

by Hammad Majeed
"... This paper introduces a new type of mutation, Context-Aware Mutation, which is inspired by the recently introduced context-aware crossover. Context-Aware mutation operates by replacing existing sub-trees with modules from a previously constructed repository of possibly useful subtrees. We describe a ..."
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This paper introduces a new type of mutation, Context-Aware Mutation, which is inspired by the recently introduced context-aware crossover. Context-Aware mutation operates by replacing existing sub-trees with modules from a previously constructed repository of possibly useful subtrees. We describe an algorithmic way to produce the repository from an initial, exploratory run and test various GP set ups for producing the repository. The results show that when the exploratory run uses context-aware crossover and the main run uses context-aware mutation, not only is the final result significantly better, the overall cost of the runs in terms of individuals evaluated is significantly lower.
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EVOLVING MODULAR PROGRAMS BY EXTRACTING REUSABLE FUNCTIONS USING SIGNIFICANCE TESTING

by Anthony Oren Loeppert
"... ACKNOWLEDGEMENTS I’d like to thank my parents and wife for encouraging and supporting me to start and complete my thesis. My advisor, Dr. Huber, of course, provided much patience and advice during this project. ..."
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ACKNOWLEDGEMENTS I’d like to thank my parents and wife for encouraging and supporting me to start and complete my thesis. My advisor, Dr. Huber, of course, provided much patience and advice during this project.
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This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 1 The Automatic Acquisition, Evolution and Reuse of Modules in C

by James Alfred Walker, Julian Francis Miller
"... Abstract—This paper presents a generalization of the graph-based genetic programming (GP) technique known as Cartesian genetic programming (CGP). We have extended CGP by utilizing automatic module acquisition, evolution, and reuse. To benchmark the new technique, we have tested it on: various digita ..."
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Abstract—This paper presents a generalization of the graph-based genetic programming (GP) technique known as Cartesian genetic programming (CGP). We have extended CGP by utilizing automatic module acquisition, evolution, and reuse. To benchmark the new technique, we have tested it on: various digital circuit problems, two symbolic regression problems, the lawnmower problem, and the hierarchical if-and-only-if problem. The results show the new modular method evolves solutions quicker than the original nonmodular method, and the speedup is more pronounced on larger problems. Also, the new modular method performs fa-vorably when compared with other GP methods. Analysis of the evolved modules shows they often produce recognizable functions. Prospects for further improvements to the method are discussed. Index Terms—Automatically defined functions (ADFs), Carte-sian genetic programming (CGP), embedded Cartesian genetic programming (ECGP), genetic programming (GP), graph-based representations, modularity, module acquisition. I.
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