ABSTRACT
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.
- Angeline, P. J. Pollack, J. (1993) Evolutionary Module Acquisition, Proceedings of the 2nd Annual Conference on Evolutionary Programming, pp. 154--163, MIT Press, Cambridge.]]Google Scholar
- Dessi, A. Giani, A. Starita, A. (1999) An Analysis of Automatic Subroutine Discovery in Genetic Programming, GECCO 1999: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 996--1001, Morgan-Kaufmann, San Francisco.]]Google Scholar
- Koza, J. R. (1993) Genetic Programming: On the Programming of Computers by Means of Natural Selection, MIT Press, London.]] Google ScholarDigital Library
- Koza, J. R. (1994) Genetic Programming II: Automatic Discovery of Reusable Programs, MIT Press, London.]] Google ScholarDigital Library
- Miller, J. F. , Thomson, P., and Fogarty T. C. (1997) Designing Electronic Circuits Using Evolutionary Algorithms. Arithmetic Circuits: A Case Study, Genetic Algorithms and Evolution Strategies in Engineering and Computer Science: Recent Advancements and Industrial Applications. Editors: D. Quagliarella, J. Periaux, C. Poloni and G. Winter, Wiley.]]Google Scholar
- Miller, J. F. (1999) An Empirical Study of the Efficiency of Learning Boolean Functions using a Cartesian Genetic Programming Approach, GECCO 1999: Proceedings of the Genetic and Evolutionary Computation Conference, Orlando, Florida, pp 1135--1142, Morgan Kaufmann, San Francisco.]]Google Scholar
- Miller, J. F. Thomson, P. (2000) Cartesian Genetic Programming, Proceedings of the 3rd European Conference on Genetic Programming, Edinburgh, Lecture Notes in Computer Science, Vol. 1802, pp 121--132, Springer-Verlag, Berlin.]] Google ScholarDigital Library
- Poli, R. (1996), Parallel Distributed Genetic Programming, Technical Report, CSRP-96-15, University of Birmingham, UK.]]Google Scholar
- Rosca, J. P. (1995) Genetic Programming Exploratory Power and the Discovery of Functions, Proceedings of the 4th Annual Conference of Evolutionary Programming, San Diego, pp 719--736, MIT Press, Cambridge.]]Google Scholar
- Spector, L. (1996) Simultaneous evolution of programs and their control structures, Advances in Genetic Programming II, pp. 137--154, MIT Press, Cambridge.]] Google ScholarDigital Library
- Spector, L. (2001) Autoconstructive Evolution: Push, PushGP, and Pushpop, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO-2001, pp. 137--146. San Francisco, CA: Morgan Kaufmann Publishers]]Google Scholar
- Van Belle, T, and Ackley, D.H. (2001) Code Factoring and the Evolution of Evolvability, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO-2001, pp. 1383--1390. San Francisco, CA: Morgan Kaufmann Publishers]]Google Scholar
- Vassilev, V. K. and Miller J. F. (2000) The Advantages of Landscape Neutrality in Digital Circuit Evolution, Proceedings of the 3rd International Conference on Evolvable Systems: From Biology to Hardware (ICES2000), Lecture Notes in Computer Science, Vol. 1801, 252--263. Springer, Berlin.]] Google ScholarDigital Library
- Vassilev, V. K. and Miller J. F. (2000) Scalability Problems of Digital Circuit Evolution, 2nd NASA/DOD Workshop on Evolvable Hardware, IEEE Computer Society Press, pp. 55--64.]] Google ScholarDigital Library
- Walker, J. A. Miller, J. F. (2004) Evolution and Acquisition of Modules in Cartesian Genetic Programming, Proc. of the 7th European Conference on Genetic Programming, Lecture Notes in Computer Science, Vol. 3003, pp 187--197, Springer-Verlag, Berlin.]]Google ScholarCross Ref
- Woodward, J. R. (2003) Modularity in Genetic Programming, Proceedings of the Fifth European Conference on Genetic Programming, Lecture Notes in Computer Science, Vol. 2610, pp. 258--267, Springer-Verlag, Berlin.]] Google ScholarDigital Library
- Yu, T. and Miller, J. F. (2001) Neutrality and the Evolvability of Boolean Function Landscape, Proceedings of the 4th European Conference on Genetic Programming, Lecture Notes in Computer Science, Vol. 2038, pp. 204--217, Springer-Verlag, Berlin.]] Google ScholarDigital Library
- Investigating the performance of module acquisition in cartesian genetic programming
Recommendations
The Automatic Acquisition, Evolution and Reuse of Modules in Cartesian Genetic Programming
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 ...
Embedded cartesian genetic programming and the lawnmower and hierarchical-if-and-only-if problems
GECCO '06: Proceedings of the 8th annual conference on Genetic and evolutionary computationEmbedded 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 ...
A multi-chromosome approach to standard and embedded cartesian genetic programming
GECCO '06: Proceedings of the 8th annual conference on Genetic and evolutionary computationEmbedded 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-...
Comments