Skip header Section
Skip Bibliometrics Section
Automatic Re-engineering of Software Using Genetic ProgrammingNovember 1999
Abstract
No abstract available.
Cited By
Chennupati G, Azad R and Ryan C Performance Optimization of Multi-Core Grammatical Evolution Generated Parallel Recursive Programs Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, (1007-1014)- Chennpati G, Azad R and Ryan C On the Automatic Generation of Efficient Parallel Iterative Sorting Algorithms Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation, (1369-1370)
- White D and Singer J Rethinking Genetic Improvement Programming Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation, (845-846)
- Cody-Kenny B, Galván-López E and Barrett S locoGP Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation, (811-818)
- Langdon W, Modat M, Petke J and Harman M Improving 3D medical image registration CUDA software with genetic programming Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation, (951-958)
- Zhang J, Chen J, Hao D, Xiong Y, Xie B, Zhang L and Mei H Search-based inference of polynomial metamorphic relations Proceedings of the 29th ACM/IEEE International Conference on Automated Software Engineering, (701-712)
- Cody-Kenny B and Barrett S The Emergence of Useful Bias in Self-focusing Genetic Programming for Software Optimisation Proceedings of the 5th International Symposium on Search Based Software Engineering - Volume 8084, (306-311)
- Harman M, Lakhotia K, Singer J, White D and Yoo S (2013). Cloud engineering is Search Based Software Engineering too, Journal of Systems and Software, 86:9, (2225-2241), Online publication date: 1-Sep-2013.
- Harman M, Mansouri S and Zhang Y (2012). Search-based software engineering, ACM Computing Surveys (CSUR), 45:1, (1-61), Online publication date: 1-Nov-2012.
- Harman M The role of artificial intelligence in software engineering Proceedings of the First International Workshop on Realizing AI Synergies in Software Engineering, (1-6)
- Harman M, Langdon W, Jia Y, White D, Arcuri A and Clark J The GISMOE challenge: constructing the pareto program surface using genetic programming to find better programs (keynote paper) Proceedings of the 27th IEEE/ACM International Conference on Automated Software Engineering, (1-14)
- Koza J Introduction to genetic programming tutorial Proceedings of the 12th annual conference companion on Genetic and evolutionary computation, (2137-2262)
- Harman M, Binkley D, Gallagher K, Gold N and Krinke J (2009). Dependence clusters in source code, ACM Transactions on Programming Languages and Systems, 32:1, (1-33), Online publication date: 1-Oct-2009.
- Harman M and Tratt L Pareto optimal search based refactoring at the design level Proceedings of the 9th annual conference on Genetic and evolutionary computation, (1106-1113)
- Harman M The Current State and Future of Search Based Software Engineering 2007 Future of Software Engineering, (342-357)
- Cheang S, Leung K and Lee K (2006). Genetic parallel programming, Evolutionary Computation, 14:2, (129-156), Online publication date: 1-Jun-2006.
- Koza J Human-competitive applications of genetic programming Advances in evolutionary computing, (663-682)
- Koza J, Keane M, Yu J, Bennett F and Mydlowec W (2000). Automatic Creation of Human-Competitive Programs and Controllers by Means of Genetic Programming, Genetic Programming and Evolvable Machines, 1:1-2, (121-164), Online publication date: 1-Apr-2000.
Recommendations
Using genetic programming for the induction of novice procedural programming solution algorithms
SAC '02: Proceedings of the 2002 ACM symposium on Applied computingThis paper describes a genetic programming system for the induction of solutions to novice procedural programming problems. This genetic programming system will form part of a generic architecture for the development of intelligent programming tutors ...
Neural network crossover in genetic algorithms using genetic programming
AbstractThe use of genetic algorithms (GAs) to evolve neural network (NN) weights has risen in popularity in recent years, particularly when used together with gradient descent as a mutation operator. However, crossover operators are often omitted from ...
Genetic programming: a tutorial introduction
GECCO '12: Proceedings of the 14th annual conference companion on Genetic and evolutionary computationGenetic programming emerged in the early 1990's as one of the most exciting new evolutionary algorithm paradigms. It has rapidly grown into a thriving area of research and application. While sharing the evolutionary inspired algorithm principles of a ...