Alberto Moraglio
Krzysztof Krawiec
- A. Moraglio, K. Krawiec, C. Johnson, Geometric Semantic Genetic Programming, PPSN XII, 2012. Google Scholar
Digital Library
- K. Krawiec, P. Lichocki, Approximating Geometric Crossover in Semantic Space, GECCO 2009, Google ScholarDigital Library
- K. Krawiec, T. Pawlak, Locally Geometric Semantic Crossover: A Study on the Roles of Semantic and Homology in Recombination Operators, Genetic Programming and Evolvable Machines, 2013, Google ScholarDigital Library
- T. Pawlak, B. Wieloch, K. Krawiec, Semantic Backpropagation for Designing Genetic Operators in Genetic Programming, IEEE Transactions on Evolutionary Computation, 2014.Google Scholar
- L. Beadle, C. Johnson, Semantically Driven Crossover in Genetic Programming, CEC 2008, Google ScholarDigital Library
- L. Beadle, C. Johnson, Semantically Driven Mutation in Genetic Programming, CEC 2009, Google ScholarDigital Library
- N.Q. Uy, N.X. Hoai, M. O'Neill, R.I. McKay, E. Galvan-Lopez, Semantically-based crossover in genetic programming: application to real-valued symbolic regression, Genetic Programming and Evolvable Machines, 2011, Google ScholarDigital Library
- N.Q. Uy, N.X. Hoai, M. O'Neill, R.I. McKay, D.N. Phong, On the roles of semantic locality in genetic programming, Information Sciences, 2013, Google ScholarDigital Library
- N.Q. Uy, N.X. Hoai, Michael O'Neill, Semantics based mutation in genetic programming: The case for real-valued symbolic regression, MENDEL 2009.Google Scholar
- L. Beadle, C. Johnson, Semantic analysis of program initialisation in genetic programming, Genetic Programming and Evolvable Machines, 2009, Google ScholarDigital Library
- D. Jackson, Promoting Phenotypic Diversity in Genetic Programming, PPSN XI, 2010. Google ScholarDigital Library
- E. Galvan-Lopez, B. Cody-Kenny, L. Trujillo, A. Kattan, Using Semantics in the Selection Mechanism in Genetic Programming: a Simple Method for Promoting Semantic Diversity, CEC 2013.Google ScholarCross Ref
- R.E. Smith, S. Forrest, and A.S. Perelson. "Searching for diverse, coop- erative populations with genetic algorithms". In: Evolutionary Compu- tation 1.2 (1993). Google ScholarDigital Library
- Lasarczyk, C. W. G. & and Wolfgang Banzhaf, P. D. Dynamic Subset Selection Based on a Fitness Case Topology Evolutionary Computation, 2004, 12, 223--242 Google ScholarDigital Library
- Nguyen Quang Uy, Nguyen Xuan Hoai, Michael O'Neill, R. I. McKay, and Dao Ngoc Phong. On the roles of semantic locality of crossover in genetic programming. Information Sciences, 235:195--213, 20 June 2013. Google ScholarDigital Library
- Mauro Castelli, Leonardo Vanneschi, and Sara Silva. Semantic search-based genetic programming and the effect of intron deletion. IEEE Transactions on Cybernetics, 44(1):103--113, January 2014.Google ScholarCross Ref
- Langdon, W. B. & Poli, R. Foundations of Genetic Programming Springer-Verlag, 2002 Google ScholarDigital Library
- McPhee, N. F., Ohs, B. & Hutchison, T., Semantic Building Blocks in Genetic Programming, in O'Neill, M et al. (eds.) Proceedings of the 11th European Conference on Genetic Programming, EuroGP 2008, Springer, 2008, 4971, 134--145 Google ScholarDigital Library
- A. Moraglio, Towards a Geometric Unification of Evolutionary Algorithms, PhD Thesis, University of Essex, UK, 2007.Google Scholar
- A. Moraglio, R. Poli, Topological Interpretation of Crossover, Genetic and Evolutionary Computation Conference, pages 1377--1388, 2004.Google Scholar
- A. Moraglio, A. Mambrini, L. Manzoni, Runtime Analysis of Mutation-Based Geometric Semantic Geometric Programming on Boolean Functions, Foundations of Genetic Algorithms, 2013. Google ScholarDigital Library
- A. Moraglio, A. Mambrini, Runtime Analysis of Mutation-Based Geometric Semantic Genetic Programming for Basis Functions Regression, Genetic and Evolutionary Computation Conference, 2013. Google ScholarDigital Library
- A. Mambrini, L. Manzoni, A. Moraglio, Theory-Laden Design of Mutation-Based Geometric Semantic Genetic Programming for Learning Classification Trees, IEEE Congress on Evolutionary Computation 2013.Google Scholar
- A. Moraglio, J. McDermott, M. O'Neill, Geometric Semantic Grammatical Evolution, SMGP workshop at PPSN, 2014.Google Scholar
- A. Moraglio, An Efficient Implementation of GSGP using Higher-Order Functions and Memoization, SMGP workshop at PPSN, 2014.Google Scholar
- J. Fieldsend, A. Moraglio. Strength through diversity: Disaggregation and multi-objectivisation approaches for genetic programming, GECCO, 2015 (to appear). Google ScholarDigital Library
- L. Vanneschi, M. Castelli, L. Manzoni, S. Silva, A New Implementation of Geometric Semantic GP and Its Application to Problems in Pharmacokinetics, EuroGP 2013 Google ScholarDigital Library
- L. Vanneschi, S. Silva, M. Castelli, L. Manzoni, Geometric semantic genetic programming for real life applications, in Genetic Programming Theory and Practice XI, 2013Google Scholar
- R. Ffrancon, M. Schoenauer, Greedy Semantic Local Search for Small Solutions, Semantic Methods in Genetic Programming Workshop, GECCO'15, 2015. Google ScholarDigital Library
- T.P. Pawlak, Competent Algorithms for Geometric Semantic Genetic Programming, PhD Thesis, Poznan University of Technology, 2015.Google Scholar
- T.P. Pawlak, K. Krawiec, Progress properties and fitness bounds for geometric semantic search operators, Genetic Programming and Evolvable Machines, Vol. 17, pp. 5--23, March 2016. Google ScholarDigital Library
- Semantic genetic programming
Recommendations
Semantic Genetic Programming
GECCO Companion '15: Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary ComputationSemantic genetic programming is a recent, rapidly growing trend in Genetic Programming (GP) that aims at opening the 'black box' of the evaluation function and make explicit use of more information on program behavior in the search. In the most common ...
Cartesian Genetic Programming as an Optimizer of Programs Evolved with Geometric Semantic Genetic Programming
Genetic ProgrammingAbstractIn Geometric Semantic Genetic Programming (GSGP), genetic operators directly work at the level of semantics rather than syntax. It provides many advantages, including much higher quality of resulting individuals (in terms of error) in comparison ...
Semantic analysis of program initialisation in genetic programming
Population initialisation in genetic programming is both easy, because random combinations of syntax can be generated straightforwardly, and hard, because these random combinations of syntax do not always produce random and diverse program behaviours. ...
Comments