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Distance-Based Bias in Model-Directed Optimization of Additively Decomposable Problems [article]

Martin Pelikan, Mark W. Hauschild
2012 arXiv   pre-print
Learning from experience (Hauschild & Pelikan, 2008; Hauschild, Pelikan, Sastry, & Goldberg, 2011; Pelikan, 2005) represents one approach to dealing with this issue.  ...  & Pelikan, 2008; Hauschild, Pelikan, Sastry, & Goldberg, 2011; Mühlenbein & Mahnig, 2002) .  ... 
arXiv:1201.2241v1 fatcat:hh2lhwt5tnh7revkh2zflwq5ci

Estimation of Distribution Algorithms [chapter]

Martin Pelikan, Mark W. Hauschild, Fernando G. Lobo
2015 Springer Handbook of Computational Intelligence  
doi:10.1007/978-3-662-43505-2_45 fatcat:wdnzqegmcnfirc3hkaw62gjwva

An introduction and survey of estimation of distribution algorithms

Mark Hauschild, Martin Pelikan
2011 Swarm and Evolutionary Computation  
To develop a method that was more broadly applicable, Hauschild, Pelikan, Sastry, and Goldberg (2008) proposed two different methods to restrict or penalize the allowable edges in hBOA model building  ...  Hauschild, Pelikan, Sastry, and Lima (2009) analyzed the models generated by hBOA when solving concatenated traps, random additively decomposable problems, hierarchical traps and 2D Ising spin glasses  ... 
doi:10.1016/j.swevo.2011.08.003 fatcat:dwuwfqma4zc5pesijpinqrpgdy

Enhancing Efficiency of Hierarchical BOA Via Distance-Based Model Restrictions [chapter]

Mark Hauschild, Martin Pelikan
2008 Lecture Notes in Computer Science  
Hauschild et al.  ...  This fact is explored by Hauschild et al.  ... 
doi:10.1007/978-3-540-87700-4_42 fatcat:27zrnbk7dzd27iflo7ao7vx4mu

Performance of Network Crossover on NK Landscapes and Spin Glasses [chapter]

Mark Hauschild, Martin Pelikan
2010 Parallel Problem Solving from Nature, PPSN XI  
One may also run an EDA on trial instances of the problem to learn promising network structures as suggested in (Hauschild, Pelikan, Sastry, & Goldberg, 2008) .  ...  In addition, linkage learning algorithms can also be used to find the structure of the problem by mining their generated models (Hauschild, Pelikan, Sastry, & Goldberg, 2008; .  ... 
doi:10.1007/978-3-642-15871-1_47 dblp:conf/ppsn/HauschildP10 fatcat:ahgmxdmnpfhv3h5cczhqldw27m

Advanced neighborhoods and problem difficulty measures

Mark Hauschild, Martin Pelikan
2011 Proceedings of the 13th annual conference on Genetic and evolutionary computation - GECCO '11  
(Pelikan, Sastry, Butz, & Goldberg, 2006; Pelikan, Sastry, Goldberg, Butz, & Hauschild, 2009 ).  ... 
doi:10.1145/2001576.2001662 dblp:conf/gecco/HauschildP11 fatcat:5owokwje3fbs7cawnlpfx73asu

Transfer Learning, Soft Distance-Based Bias, and the Hierarchical BOA [chapter]

Martin Pelikan, Mark W. Hauschild, Pier Luca Lanzi
2012 Lecture Notes in Computer Science  
Distance-Based Bias Based on Previous Runs of hBOA This section describes the approach to learning from experience developed by Pelikan and Hauschild [12] inspired mainly by the work of Hauschild et  ...  [12] follows the work of Hauschild et al. [6, 11, 20] . Given an ADF, we define the distance between two variables using a graph G of n nodes, one node per variable.  ... 
doi:10.1007/978-3-642-32937-1_18 fatcat:lyn34n636jd5pnlzpto4tdqfkm

Transfer Learning, Soft Distance-Based Bias, and the Hierarchical BOA [article]

Martin Pelikan, Mark W. Hauschild, Pier Luca Lanzi
2012 arXiv   pre-print
Distance-Based Bias Based on Previous Runs of hBOA This section describes the approach to learning from experience developed by Pelikan and Hauschild [12] inspired mainly by the work of Hauschild et  ...  [12] follows the work of Hauschild et al. [6, 11, 20] . Given an ADF, we define the distance between two variables using a graph G of n nodes, one node per variable.  ... 
arXiv:1203.5443v2 fatcat:j6x4vugp5zcfnf4p35cnyd3k7q

Second order heuristics in ACGP

Cezary Z. Janikow, John W. Aleshunas, Mark W. Hauschild
2011 Proceedings of the 13th annual conference companion on Genetic and evolutionary computation - GECCO '11  
Genetic Programming explores the problem search space by means of operators and selection. Mutation and crossover operators apply uniformly, while selection is the driving force for the search. Constrained GP changes the uniform exploration to pruned non-uniform, skipping some subspaces and giving preferences to others, according to some heuristics. Adaptable Constrained GP is a methodology for discovery of such useful heuristics. Both methodologies have previously demonstrated their surprising
more » ... capabilities using only first-order (parent-child) heuristics. Recently, they have been extended to second-order (parent-children) heuristics. This paper describes the second-order processing, and illustrates the usefulness and efficiency of this approach using a simple problem specifically constructed to exhibit strong second-order structure.
doi:10.1145/2001858.2002066 dblp:conf/gecco/JanikowAH11 fatcat:s25lphyunfgmtgvp32xn5ia7cy

Intelligent bias of network structures in the hierarchical BOA

Mark W. Hauschild, Martin Pelikan
2009 Proceedings of the 11th Annual conference on Genetic and evolutionary computation - GECCO '09  
More recently, Hauschild et al.  ...  This constantly increasing performance is in marked contrast to the speedup in evaluations.  ... 
doi:10.1145/1569901.1569959 dblp:conf/gecco/HauschildP09 fatcat:u3hu4w66gbaxbavhj5vvyxnnle

mirDIP 4.1—integrative database of human microRNA target predictions

Tomas Tokar, Chiara Pastrello, Andrea E M Rossos, Mark Abovsky, Anne-Christin Hauschild, Mike Tsay, Richard Lu, Igor Jurisica
2017 Nucleic Acids Research  
MicroRNAs are important regulators of gene expression, achieved by binding to the gene to be regulated. Even with modern high-throughput technologies, it is laborious and expensive to detect all possible microRNA targets. For this reason, several computational microRNA-target prediction tools have been developed, each with its own strengths and limitations. Integration of different tools has been a successful approach to minimize the shortcomings of individual databases. Here, we present mirDIP
more » ... v4.1, providing nearly 152 million human microRNAtarget predictions, which were collected across 30 different resources. We also introduce an integrative score, which was statistically inferred from the obtained predictions, and was assigned to each unique microRNA-target interaction to provide a unified measure of confidence. We demonstrate that integrating predictions across multiple resources does not cumulate prediction bias toward biological processes or pathways. mirDIP v4.1 is freely available at
doi:10.1093/nar/gkx1144 pmid:29194489 pmcid:PMC5753284 fatcat:4avz62zhafdvjen3c3tsgwtr3q

Image segmentation using a genetic algorithm and hierarchical local search

Mark Hauschild, Sanjiv Bhatia, Martin Pelikan
2012 Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference - GECCO '12  
This paper proposes a hybrid genetic algorithm to perform image segmentation based on applying the q-state Potts spin glass model to a grayscale image. First, the image is converted to a set of weights for a q-state spin glass and then a steady-state genetic algorithm is used to evolve candidate segmented images until a suitable candidate solution is found. To speed up the convergence to an adequate solution, hierarchical local search is used on each evaluated solution. The results show that
more » ... hybrid genetic algorithm with hierarchical local search is able to efficiently perform image segmentation. The necessity of hierarchical search for these types of problems is also clearly demonstrated.
doi:10.1145/2330163.2330253 dblp:conf/gecco/HauschildBP12 fatcat:2wc5ushldvh4jaysjqsttuzw4u

A bright future for addressing chemical emissions in life cycle assessment

Michael Z. Hauschild, Olivier Jolliet, Mark A. J. Huijbregts
2011 The International Journal of Life Cycle Assessment  
doi:10.1007/s11367-011-0320-8 fatcat:4isbubz34jfj7mbzovnbdzdwqi

Distance-based bias in model-directed optimization of additively decomposable problems

Martin Pelikan, Mark W. Hauschild
2012 Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference - GECCO '12  
Nonetheless, the approach of Hauschild et al.  ...  Using Distance-Based Bias in hBOA The basic idea of incorporating the distance-based bias based on prior runs into hBOA is inspired mainly by the work of Hauschild et al. [11] .  ... 
doi:10.1145/2330163.2330203 dblp:conf/gecco/PelikanH12 fatcat:4uwnf7355jdyvmuuhwdaizghry

Pairwise and problem-specific distance metrics in the linkage tree genetic algorithm

Martin Pelikan, Mark W. Hauschild, Dirk Thierens
2011 Proceedings of the 13th annual conference on Genetic and evolutionary computation - GECCO '11  
This approach is often referred to as learning from experience and has been studied especially in the context of estimation of distribution algorithms (EDAs) (Hauschild & Pelikan, 2008; Hauschild & Pelikan  ... 
doi:10.1145/2001576.2001713 dblp:conf/gecco/PelikanHT11 fatcat:la5ljeruxfbn7inqjegbe6n3me
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