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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
Springer Handbook of Computational Intelligence
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
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
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
(Pelikan, Sastry, Butz, & Goldberg, 2006; Pelikan, Sastry, Goldberg, Butz, & Hauschild, 2009 ). ...doi:10.1145/2001576.2001662 dblp:conf/gecco/HauschildP11 fatcat:5owokwje3fbs7cawnlpfx73asu
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  inspired mainly by the work of Hauschild et ...  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
Distance-Based Bias Based on Previous Runs of hBOA This section describes the approach to learning from experience developed by Pelikan and Hauschild  inspired mainly by the work of Hauschild et ...  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
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 surprisingdoi:10.1145/2001858.2002066 dblp:conf/gecco/JanikowAH11 fatcat:s25lphyunfgmtgvp32xn5ia7cy
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.
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
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 mirDIPdoi:10.1093/nar/gkx1144 pmid:29194489 pmcid:PMC5753284 fatcat:4avz62zhafdvjen3c3tsgwtr3q
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
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 thatdoi:10.1145/2330163.2330253 dblp:conf/gecco/HauschildBP12 fatcat:2wc5ushldvh4jaysjqsttuzw4u
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.
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.  . ...doi:10.1145/2330163.2330203 dblp:conf/gecco/PelikanH12 fatcat:4uwnf7355jdyvmuuhwdaizghry
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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