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Cartesian genetic programming: its status and future
2019
Genetic Programming and Evolvable Machines
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Cartesian genetic programming, a well-established method of genetic programming, is approximately 20 years old. It represents solutions to computational problems as graphs. ...
Its genetic encoding includes explicitly redundant genes which are wellknown to assist in effective evolutionary search. ...
Introduction The term "Cartesian genetic programming" (CGP) 1 first appeared in 1999 [65] . ...
doi:10.1007/s10710-019-09360-6
fatcat:vocd7cqshndefkib6nvbhu7gxa
Model-based evolutionary algorithms
2019
Proceedings of the Genetic and Evolutionary Computation Conference Companion on - GECCO '19
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Acknowledgements ◮ Selected images were re-used from the 2012 GECCO tutorial "Probabilistic Model-building Genetic Algorithms" by Martin Pelikan. ...
GECCO 2019 Tutorial -Model-Based Evolutionary Algorithms. 103/122 Model-Based Genetic Programming ◮ If looking at solutions node-based, and using a fixed template, essentially have Cartesian fixed-length ...
GECCO 2019 Tutorial -Model-Based Evolutionary Algorithms. 102/122 Model-Based Genetic Programming ◮ Early works did not use grammar, e.g PIPE (Probabilistic Incremental Program Evolution) ◮ Salustowicz ...
doi:10.1145/3319619.3323386
dblp:conf/gecco/ThierensB19
fatcat:w6prjwu7wzfe3citnfna2sdcbe
Model-based evolutionary algorithms
2018
Proceedings of the Genetic and Evolutionary Computation Conference Companion on - GECCO '18
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Acknowledgements ◮ Selected images were re-used from the 2012 GECCO tutorial "Probabilistic Model-building Genetic Algorithms" by Martin Pelikan. ...
GECCO 2018 Tutorial -Model-Based Evolutionary Algorithms. 103/122 Model-Based Genetic Programming ◮ If looking at solutions node-based, and using a fixed template, essentially have Cartesian fixed-length ...
GECCO 2018 Tutorial -Model-Based Evolutionary Algorithms. 102/122 Model-Based Genetic Programming ◮ Early works did not use grammar, e.g PIPE (Probabilistic Incremental Program Evolution) ◮ Salustowicz ...
doi:10.1145/3205651.3207874
dblp:conf/gecco/ThierensB18
fatcat:mjsqksmkunbkba3e2a6jfyyli4
Model-based evolutionary algorithms
2014
Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion - GECCO Comp '14
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Acknowledgements ◮ Selected images were re-used from the 2012 GECCO tutorial "Probabilistic Model-building Genetic Algorithms" by Martin Pelikan. ...
not always ◮ Grammar Guided Genetic Programming (GGGP) ◮ Grammars very useful to limit search space ◮ But how do we use it learn structural features? ...
GECCO 2016 Tutorial -Model-Based Evolutionary Algorithms. 37/110 Linkage Tree Genetic Algorithm ◮ The LTGA is an instance of GOMEA that uses a Linkage Tree as FOS model (Thierens & Bosman, 2010 , 2011 ...
doi:10.1145/2598394.2605344
dblp:conf/gecco/ThierensB14
fatcat:ocjcmqpwfbb5ln6hadekywb2hy
Model-Based Evolutionary Algorithms
2015
Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference - GECCO Companion '15
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Acknowledgements ◮ Selected images were re-used from the 2012 GECCO tutorial "Probabilistic Model-building Genetic Algorithms" by Martin Pelikan. ...
not always ◮ Grammar Guided Genetic Programming (GGGP) ◮ Grammars very useful to limit search space ◮ But how do we use it learn structural features? ...
GECCO 2016 Tutorial -Model-Based Evolutionary Algorithms. 37/110 Linkage Tree Genetic Algorithm ◮ The LTGA is an instance of GOMEA that uses a Linkage Tree as FOS model (Thierens & Bosman, 2010 , 2011 ...
doi:10.1145/2739482.2756584
dblp:conf/gecco/ThierensB15
fatcat:m3vngle7ong4ngopximusovgoa
Model-Based Evolutionary Algorithms
2016
Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion - GECCO '16 Companion
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Acknowledgements ◮ Selected images were re-used from the 2012 GECCO tutorial "Probabilistic Model-building Genetic Algorithms" by Martin Pelikan. ...
not always ◮ Grammar Guided Genetic Programming (GGGP) ◮ Grammars very useful to limit search space ◮ But how do we use it learn structural features? ...
GECCO 2016 Tutorial -Model-Based Evolutionary Algorithms. 37/110 Linkage Tree Genetic Algorithm ◮ The LTGA is an instance of GOMEA that uses a Linkage Tree as FOS model (Thierens & Bosman, 2010 , 2011 ...
doi:10.1145/2908961.2926975
dblp:conf/gecco/ThierensB16
fatcat:oiriel3cunbnhkqsy452yfypjq
Model-based evolutionary algorithms
2013
Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion - GECCO '13 Companion
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Acknowledgements ◮ Selected images were re-used from the 2012 GECCO tutorial "Probabilistic Model-building Genetic Algorithms" by Martin Pelikan. ...
not always ◮ Grammar Guided Genetic Programming (GGGP) ◮ Grammars very useful to limit search space ◮ But how do we use it learn structural features? ...
GECCO 2016 Tutorial -Model-Based Evolutionary Algorithms. 37/110 Linkage Tree Genetic Algorithm ◮ The LTGA is an instance of GOMEA that uses a Linkage Tree as FOS model (Thierens & Bosman, 2010 , 2011 ...
doi:10.1145/2464576.2480801
dblp:conf/gecco/ThierensB13a
fatcat:5wn2n4yjo5fgvexfq5plx4kb5y
Building Image Feature Kinetics for Cement Hydration Using Gene Expression Programming With Similarity Weight Tournament Selection
2015
IEEE Transactions on Evolutionary Computation
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GECCO 2007, ACM, p.
245-252
4
1
Miller, J., Harding, S.: Cartesian Genetic Programming. ...
using Cartesian Genetic Programming. ...
doi:10.1109/tevc.2014.2367111
fatcat:dsfdt3ngn5htfdg4ydmofrqznm
Deep learning in neural networks: An overview
2015
Neural Networks
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I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs ...
Compare Genetic Programming (GP) (Cramer, 1985) (see also Smith, 1980) which can be used to evolve computer programs of variable size (Dickmanns et al., 1987; Koza, 1992) , and Cartesian GP (Miller ...
In Proceedings of the Conference on Genetic and Evolutionary Computation (GECCO), pages 974-981. Datar, M., Immorlica, N., Indyk, P., and Mirrokni, V. S. (2004). ...
doi:10.1016/j.neunet.2014.09.003
pmid:25462637
fatcat:fniwacdkurh2pgbspkaf6uyhyq
Multi-objective evolution of fast and stable gaits on a physical quadruped robotic platform
2016
2016 IEEE Symposium Series on Computational Intelligence (SSCI)
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"Using cyclic genetic algorithms to evolve multi-loop control programs". In: IEEE International Conference Mechatronics and Automation, 2005. Vol. 1. ...
In: Proceedings of the Genetic and Evolutionary Computation Conference. GECCO '19. Association for Computing Machinery, 2019, pp. 133-141. doi: 10.1145/3321707.3321762. [58] Nordmoen, J., Nygaard, T. ...
doi:10.1109/ssci.2016.7850167
dblp:conf/ssci/NygaardTG16
fatcat:x3aoioqesvghzk7lbzpejridwi
Cost-to-Go Function Approximation
[chapter]
2017
Encyclopedia of Machine Learning and Data Mining
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In: Beyer H (ed) Proceedings of the 2005 conference on genetic and evolutionary computation (GECCO '05). ...
In: Spector L, Goodman ED, Wu A, Langdon WB, Voigt H-M, Gen M et al (eds) Proceedings of the genetic and evolutionary computation conference (GECCO-2001). ...
doi:10.1007/978-1-4899-7687-1_100093
fatcat:vse7ncdqs5atlosjhz7fhlj3im
Dagstuhl Reports, Volume 8, Issue 1, January 2018, Complete Issue
[article]
2018
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This seminar brought researchers working in genetic improvement and related areas, such as automated program repair, software testing and genetic programming, together. ...
Abstract We document the program and the immediate outcomes of Dagstuhl Seminar 18052 "Genetic Improvement of Software". ...
In: Genetic and Evolutionary Computation Conference (GECCO 2010). pp. 471-478. ...
doi:10.4230/dagrep.8.1
fatcat:dv5ntjliznbs7dwohrcmiwr3lm