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Linear Combination of Distance Measures for Surrogate Models in Genetic Programming [article]

Martin Zaefferer, Jörg Stork, Oliver Flasch, Thomas Bartz-Beielstein
2018 arXiv   pre-print
In the context of genetic programming, surrogate modeling still poses a challenge, due to the complex genotype-phenotype relationships.  ...  We compare the measures and suggest to use their linear combination in a kernel. We test the resulting model in an optimization framework, using symbolic regression problem instances as a benchmark.  ...  In addition, we recorded the weights used for linear combination of the distances in each iteration, to evaluate the contribution of each distance function over time.  ... 
arXiv:1807.01019v1 fatcat:4m7nmmk5yfcgto54xvocaa4ioq

Model-based methods for continuous and discrete global optimization

Thomas Bartz-Beielstein, Martin Zaefferer
2017 Applied Soft Computing  
In recent years, surrogate models gained importance for discrete optimization problems. This article, which consists of three parts, takes care of this development.  ...  Here, six strategies for dealing with discrete data structures are introduced. A new approach for combining surrogate information via stacking is proposed in the third part.  ...  This work is part of a project that has received funding from the European Unions Horizon  ... 
doi:10.1016/j.asoc.2017.01.039 fatcat:ghmsan4pdjgalab5akbzgubco4

Environmental and geographic variables are effective surrogates for genetic variation in conservation planning

Jeffrey O. Hanson, Jonathan R. Rhodes, Cynthia Riginos, Richard A. Fuller
2017 Proceedings of the National Academy of Sciences of the United States of America  
Here we show that freely available environmental and geographic variables can be used as effective surrogates for genetic data in conservation planning.  ...  Significance To protect biodiversity for the long term, nature reserves and other protected areas need to represent a broad range of different genetic types.  ...  Secondly, we used geographic distances as surrogates for neutral genetic variation.  ... 
doi:10.1073/pnas.1711009114 pmid:29087942 pmcid:PMC5715761 fatcat:j4ysug2hcjfvvp6v4f3ns2lea4

On Using Surrogates with Genetic Programming

Torsten Hildebrandt, Jürgen Branke
2015 Evolutionary Computation  
But these models usually require a numerical representation, and therefore can not be used with the tree representation of Genetic Programming (GP).  ...  Original citation: Hildebrandt, Torsten and Branke, Juergen (2014) On using surrogates with genetic programming. Working Paper. Coventry, UK: University of Warwick, WBS.  ...  Acknowledgment The authors would like to thank Alberto Moraglio for the source code to compute the Structural Hamming Distance (SHD) as a syntactic/genotypic similarity measure between trees.  ... 
doi:10.1162/evco_a_00133 pmid:24967694 fatcat:twhncrokxneddoqf2x6ldbav3a

Combining Genetic Programming and Particle Swarm Optimization to Simplify Rugged Landscapes Exploration [article]

Gloria Pietropolli, Giuliamaria Menara, Mauro Castelli
2022 arXiv   pre-print
To create such a surrogate model, we consider a linear genetic programming approach enhanced by a self-tuning fitness function.  ...  The proposed algorithm, called the GP-FST-PSO Surrogate Model, achieves satisfactory results in both the search for the global optimum and the production of a visual approximation of the original benchmark  ...  The goal of the proposed method, named the GP-FST-PSO Surrogate Model (which in turn stands for Genetic Programming Fuzzy Self-Tuning Particle Swarm Optimization Surrogate Model ), is the definition of  ... 
arXiv:2206.03241v1 fatcat:3tuj2pfjmrbzbo7hs7gvp6qq4e

An alternative approach to avoid overfitting for surrogate models

Huu Minh Nguyen, Ivo Couckuyt, Luc Knockaert, Tom Dhaene, Dirk Gorissen, Yvan Saeys
2011 Proceedings of the 2011 Winter Simulation Conference (WSC)  
This paper introduces a new auxiliary measure for the optimization of the hyperparameters of surrogate models which, when used in conjunction with a cheap accuracy measure, is fast and effective at avoiding  ...  A crucial step in the building of surrogate models is finding a good set of hyperparameters, which determine the behavior of the model.  ...  Ivo Couckuyt and Huu Minh Nguyen are funded by the Institute for the Promotion of Innovation by Science and Technology in Flanders (IWT-Vlaanderen).  ... 
doi:10.1109/wsc.2011.6147981 dblp:conf/wsc/NguyenCKDGS11 fatcat:dol6yipqyfbbjetk447y6bdrqm

A Surrogate Model-Based Hybrid Approach for Stochastic Robust Double Row Layout Problem

Xing Wan, Xing-Quan Zuo, Xin-Chao Zhao
2021 Mathematics  
A surrogate model is used to linearize the nonlinear term in the MIP to achieve a mixed integer linear programming model, which can be readily solved by an exact method to yield high-quality solutions  ...  Aiming at the random distribution of product demands, we study a stochastic robust double row layout problem (SR-DRLP). A mixed integer programming (MIP) model is established for SR-DRLP.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/math9151711 fatcat:idt3h5o45zetfggh6pblx7jctm

A Kriging-Based Approach to Autonomous Experimentation with Applications to X-Ray Scattering

Marcus M. Noack, Kevin G. Yager, Masafumi Fukuto, Gregory S. Doerk, Ruipeng Li, James A. Sethian
2019 Scientific Reports  
The distribution and correlation of the data is used to generate a corresponding uncertainty across the surrogate model.  ...  By suggesting follow-up measurements in regions of greatest uncertainty, the algorithm maximally increases knowledge with each added measurement.  ...  Acknowledgements The work was partially funded through the Center for Advanced Mathematics for Energy Research Applications  ... 
doi:10.1038/s41598-019-48114-3 pmid:31413339 pmcid:PMC6694190 fatcat:otncotgdlbfkzgs7wal4gzqhfi

Improving NeuroEvolution Efficiency by Surrogate Model-based Optimization with Phenotypic Distance Kernels [article]

Jörg Stork, Martin Zaefferer, Thomas Bartz-Beielstein
2019 arXiv   pre-print
While a combination of both procedures appears as a valuable solution, the definition of adequate distance measures for the surrogate modeling process is difficult.  ...  In this study, we will extend cartesian genetic programming of artificial neural networks by the use of surrogate model-based optimization.  ...  Acknowledgements: This work is supported by the German Federal Ministry of Education and Research in the funding program Forschung an Fachhochschulen under the grant number 13FH007IB6.  ... 
arXiv:1902.03419v1 fatcat:3bxk5lpwgrgajjvjfbjppf6tby

A surrogate similarity measure for the mean-variance frontier optimisation problem under bound and cardinality constraints

Francisco Guijarro, Prodromos E. Tsinaslanidis
2019 Journal of the Operational Research Society  
A genetic algorithm is applied for the identification of the assets in the portfolio, whilst the asset weights in the portfolios are obtained by a quadratic programming model.  ...  This brings us to propose a surrogate similarity measure for the optimization of the constrained frontier, which differs from a previous proposal where no bound constraints were considered.  ...  Acknowledgements We would like to thank two anonymous referees for their constructive comments and suggestions that substantially improved this article.  ... 
doi:10.1080/01605682.2019.1657367 fatcat:e7zrzzurcveyzfvjdub7rddch4

Evolving optimal agendas for package deal negotiation

Shaheen Fatima, Ahmed Kattan
2011 Proceedings of the 13th annual conference on Genetic and evolutionary computation - GECCO '11  
The proposed system uses a Surrogate Model based on Radial Basis Function Networks (RBFNs) to speed up the evolution. The negotiation scenario is as follows.  ...  The agenda is important because the outcome of negotiation depends on it. Furthermore, a and b will, in general, get different utilities/profits from different agendas.  ...  These include Genetic Programming, Radial Basis Function Network Interpolation, and Gaussian Process Regression. Genetic programming is very powerful framework for approximating unknown functions.  ... 
doi:10.1145/2001576.2001646 dblp:conf/gecco/FatimaK11 fatcat:uwgre6w2v5czjmnyeuni43c2gq

MISS: a non-linear methodology based on mutual information for genetic association studies in both population and sib-pairs analysis

Helena Brunel, Joan-Josep Gallardo-Chacón, Alfonso Buil, Montserrat Vallverdú, José Manuel Soria, Pere Caminal, Alexandre Perera
2010 Computer applications in the biosciences : CABIOS  
The MISS methodology has been contrasted with a multiple linear regression (MLR) method used for genetic association in both, a population-based study and a sib-pairs analysis and with the maximum entropy  ...  conditional probability modelling (MECPM) method, which searches for predictive multi-locus interactions.  ...  MLR-based models are the most commonly used method for measuring the linear dependencies between SNPs and phenotypes in the context of genetic association studies.  ... 
doi:10.1093/bioinformatics/btq273 pmid:20562420 fatcat:eo4im25wb5ctvep6dyqxholhmq

Genetic programming-based symbolic regression for goal-oriented dimension reduction

Gyula Dorgo, Tibor Kulcsar, Janos Abonyi
2021 Chemical Engineering Science  
The main benefit of the approach is that the evolved equations are interpretable and can be utilized in surrogate models.  ...  For the optimization of the application-oriented data visualization cost function, a Multigene genetic programming (GP)-based algorithm is introduced to optimize the structures of the equations used for  ...  projection of data and can also generate inputs for machine-learning models used in the chemical industry as surrogate models or software sensors.  ... 
doi:10.1016/j.ces.2021.116769 fatcat:ysirrtdbxrgd5mcjzys7ly673a

Active Learning to Understand Infectious Disease Models and Improve Policy Making

Lander Willem, Sean Stijven, Ekaterina Vladislavleva, Jan Broeckhove, Philippe Beutels, Niel Hens, Marcel Salathé
2014 PLoS Computational Biology  
Modeling plays a major role in policy making, especially for infectious disease interventions but such models can be complex and computationally intensive.  ...  of complex model runs.  ...  We also thank the audience of the April 2012 SIMID workshop for constructive feedback on our presentation ''Symbolic regression for modeling epidemiological systems'' (  ... 
doi:10.1371/journal.pcbi.1003563 pmid:24743387 pmcid:PMC3990517 fatcat:orspuqbmjzdyreh5ttbv3wrcja

Speeding-Up Expensive Evaluations in High-Level Synthesis Using Solution Modeling and Fitness Inheritance [chapter]

Christian Pilato, Daniele Loiacono, Antonino Tumeo, Fabrizio Ferrandi, Pier Luca Lanzi, Donatella Sciuto
2010 Computational Intelligence in Expensive Optimization Problems  
In this paper, we present an evolutionary approach to HLS that extends previous works in three respects: (i) we exploit the NSGA-II, a multi-objective genetic algorithm, to fully automate the design space  ...  exploration without the need of any human intervention, (ii) we replace the expensive evaluation process of candidate solutions with a quite accurate regression model, and (iii) we reduce the number of  ...  In general a performance metric can provide either a relative measure (e.g., Non Dominated Combined Set Ratio [37] ) or an absolute measure (e.g., S metric [38] ).  ... 
doi:10.1007/978-3-642-10701-6_26 fatcat:uf6kjwnlzrcihfzafgkce6yzyu
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