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Modular Neuroevolution for Multilegged Locomotion
"... Legged robots are useful in tasks such as search and rescue because they can effectively navigate on rugged terrain. However, it is difficult to design controllers for them that would be stable and robust. Learning the control behavior is difficult because optimal behavior is not known, and the sear ..."
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Cited by 8 (2 self)
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Legged robots are useful in tasks such as search and rescue because they can effectively navigate on rugged terrain. However, it is difficult to design controllers for them that would be stable and robust. Learning the control behavior is difficult because optimal behavior is not known, and the search space is too large for reinforcement learning and for straightforward evolution. As a solution, this paper proposes a modular approach for evolving neural network controllers for such robots. The search space is effectively reduced by exploiting symmetry in the robot morphology, and encoding it into network modules. Experiments involving physically realistic simulations of a quadruped robot produce the same symmetric gaits, such as pronk, pace, bound and trot, that are seen in quadruped animals. Moreover, the robot can transition dynamically to more effective gaits when faced with obstacles. The modular approach also scales well when the number of legs or their degrees of freedom are increased. Evolved nonmodular controllers, in contrast, produce gaits resembling crippled animals that are much less effective and do not scale up as a result. Handdesigned controllers are also less effective, especially on an obstacle terrain. These results suggest that the modular approach is effective for designing robust locomotion controllers for multilegged robots.
Optimal implementations of UPGMA and other common clustering algorithms
, 2007
"... In this work we consider hierarchical clustering algorithms, such as UPGMA, which follow the closestpair joining scheme. We study optimal O(n 2)time implementations of such algorithms which use a ‘locally closest ’ joining scheme, and specify conditions under which this relaxed joining scheme is e ..."
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Cited by 4 (1 self)
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In this work we consider hierarchical clustering algorithms, such as UPGMA, which follow the closestpair joining scheme. We study optimal O(n 2)time implementations of such algorithms which use a ‘locally closest ’ joining scheme, and specify conditions under which this relaxed joining scheme is equivalent to the original one (i.e. ‘globally closest’). Key Words: Hierarchical clustering, UPGMA, design of algorithms, inputoutput specification, computational complexity
Information Preserving MultiObjective Feature Selection for Unsupervised Learning
 In Proc. of the Genetic and Evolutionary Computation Conference (GECCO ’06
, 2006
"... In this work we propose a novel, sound framework for evolutionary feature selection in unsupervised machine learning problems. We show that unsupervised feature selection is inherently multiobjective and behaves di#erently from supervised feature selection in that the number of features must be max ..."
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Cited by 3 (1 self)
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In this work we propose a novel, sound framework for evolutionary feature selection in unsupervised machine learning problems. We show that unsupervised feature selection is inherently multiobjective and behaves di#erently from supervised feature selection in that the number of features must be maximized instead of being minimized. Although this might sound surprising from a supervised learning point of view, we exemplify this relationship on the problem of data clustering and show that existing approaches do not pose the optimization problem in an appropriate way. Another important consequence of this paradigm change is a method which segments the Pareto sets produced by our approach. Inspecting only prototypical points from these segments drastically reduces the amount of work for selecting a final solution. We compare our methods against existing approaches on eight data sets.
Linear Grouping Using Orthogonal Regression
, 2004
"... This paper proposes a new method, called linear grouping algorithm (LGA), to detect different linear structures in a data set. LGA is useful for investigating potential linear patterns in datasets, that is, subsets that follow different linear relationships. LGA combines ideas from principal compon ..."
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Cited by 2 (0 self)
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This paper proposes a new method, called linear grouping algorithm (LGA), to detect different linear structures in a data set. LGA is useful for investigating potential linear patterns in datasets, that is, subsets that follow different linear relationships. LGA combines ideas from principal components, clustering methods and resampling algorithms. It can detect several different linear relations at once. We also propose methods to determine the number of groups in the data and diagnostic tools to investigate the results obtained from LGA. It is shown how LGA can be extended to detect groups characterized by lower dimensional hyperplanes as well. Some applications illustrate the usefulness of LGA in practice. Key words: Linear grouping, orthogonal regression. 1 Introduction and
Segmenting Markets by Bagged Clustering Segmenting Markets by Bagged Clustering
"... We introduce bagged clustering as a new approach in the field of post hoc market segmentation research and illustrate the managerial advantages over both hierarchical and partitioning algorithms, especially with large binary data sets. The most important improvements are enhanced stability and inter ..."
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We introduce bagged clustering as a new approach in the field of post hoc market segmentation research and illustrate the managerial advantages over both hierarchical and partitioning algorithms, especially with large binary data sets. The most important improvements are enhanced stability and interpretability of segments based on binary data. One of the main goals of the procedure is to complement more traditional techniques as an exploratory segment analysis tool. The merits of the approach are illustrated using a tourism marketing application.
STUDIES
, 2013
"... c ○ Stephen Ingram, 2013ing. To address the case of costly distances, we develop an algorithm framework, Glint, which efficiently manages the number of distance function calculations for the Multidimensional Scaling class of DR algorithms. We then show that Glint implementations of Multidimensional ..."
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c ○ Stephen Ingram, 2013ing. To address the case of costly distances, we develop an algorithm framework, Glint, which efficiently manages the number of distance function calculations for the Multidimensional Scaling class of DR algorithms. We then show that Glint implementations of Multidimensional Scaling algorithms achieve substantial speed improvements or remove the need for human monitoring. iii Preface Parts of this thesis have appeared in publications and journal submissions. Most of Chapter 3 is based on the following published conference paper: