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A Cooperative Coevolution Framework for Parallel Learning to Rank

Shuaiqiang Wang, Yun Wu, Byron J. Gao, Ke Wang, Hady W. Lauw, Jun Ma
2015 IEEE Transactions on Knowledge and Data Engineering  
We propose CCRank, the first parallel framework for learning to rank based on evolutionary algorithms (EA), aiming to significantly improve learning efficiency while maintaining accuracy.  ...  CCRank is based on cooperative coevolution (CC), a divide-and-conquer framework that has demonstrated high promise in function optimization for problems with large search space and complex structures.  ...  ACKNOWLEDGMENTS The authors would like to thank the editor and the anonymous reviewers for their constructive comments and suggestions.  ... 
doi:10.1109/tkde.2015.2453952 fatcat:gltmjpyaencx5dnulatxppwyyy

Parallel learning to rank for information retrieval

Shuaiqiang Wang, Byron J. Gao, Ke Wang, Hady W. Lauw
2011 Proceedings of the 34th international ACM SIGIR conference on Research and development in Information - SIGIR '11  
Learning to rank represents a category of effective ranking methods for information retrieval.  ...  In this paper, we investigate parallel learning to rank, targeting simultaneous improvement in accuracy and efficiency.  ...  We also discuss other ways of achieving parallelization for learning to rank, such as MapReduce [3] . PARALLEL LEARNING TO RANK The CCRank Framework Overview.  ... 
doi:10.1145/2009916.2010060 dblp:conf/sigir/WangGWL11 fatcat:5555rz35pnhy3gkxzqnmk2bcim

A Novel Method for Grouping Variables in Cooperative Coevolution for Large-scale Global Optimization Problems

Alexey Vakhnin, Evgenii Sopov
2018 Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics  
In this study, we have proposed a novel method of grouping variables for the cooperative coevolution (CC) framework (random adaptive grouping (RAG))).  ...  The RAG method is based on the following idea: after some predefined number of fitness evaluations in cooperative coevolution, a half of subcomponents with the worst fitness values randomly mixes indices  ...  coevolution framework to solve LSGO problems.  ... 
doi:10.5220/0006903102710278 dblp:conf/icinco/VakhninS18 fatcat:ehxofkfh3nbtnev5g4ronfxhhu

Integrating Instance Selection, Instance Weighting, and Feature Weighting for Nearest Neighbor Classifiers by Coevolutionary Algorithms

J. Derrac, I. Triguero, S. Garcia, F. Herrera
2012 IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)  
Cooperative coevolution is a successful trend of evolutionary computation which allows us to define partitions of the domain of a given problem, or to integrate several related techniques into one, by  ...  It is possible to apply it to the development of advanced classification methods, which integrate several machine learning techniques into a single proposal.  ...  To accomplish these tasks, three populations (one for each process) are defined within a cooperative framework.  ... 
doi:10.1109/tsmcb.2012.2191953 pmid:22531787 fatcat:quu2dw3gife6xnxpqcoj4gtfoy

Recent Advances in Particle Swarm Optimization for Large Scale Problems

Danping Yan, Yongzhong Lu
2018 Journal of Autonomous Intelligence  
Up to date, how to develop new optimization algorithms for these large scale problems and how to expand the scalability of existing optimization algorithms have posed further challenges in the domain of  ...  As a branch of the swarm intelligence based algorithms, particle swarm optimization (PSO) for coping with large scale problems and its expansively diverse applications have been in rapid development over  ...  Acknowledgments This work is in part supported by the Fundamental Research Funds for the Central Universities in China (HUST: 2016YXMS105).  ... 
doi:10.32629/jai.v1i1.15 fatcat:rqtausu6dbg4jksq62hwbhr3tu

Reference sharing: a new collaboration model for cooperative coevolution

Min Shi, Shang Gao
2017 Journal of Heuristics  
Cooperative coevolutionary algorithms have been a popular and effective learning approach to solve optimization problems through problem decomposition.  ...  In the paper, we aim to design a collaboration model that can be successfully applied to a wide range of problems.  ...  the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.  ... 
doi:10.1007/s10732-016-9322-9 fatcat:57azwyabqbg2pa3m7mjb4xv7gy

Investigation of Improved Cooperative Coevolution for Large-Scale Global Optimization Problems

Aleksei Vakhnin, Evgenii Sopov
2021 Algorithms  
Cooperative Coevolution (CC) is a high-performed framework for performing the decomposition of large-scale problems into smaller and easier subproblems by grouping objective variables.  ...  The SHADE algorithm is used as a subcomponent optimizer.  ...  Related Work Cooperative Coevolution CC is an effective framework for solving many hard optimization problems.  ... 
doi:10.3390/a14050146 fatcat:sau7yjmmdjgtjmjjn5eb6kvmvq

Society–nature coevolution: interdisciplinary concept for sustainability

Helga Weisz, Eric Clark
2011 Geografiska Annaler. Series B. Human Geography  
A brief historical background to the currently ascending interest in evolutionary and coevolutionary theory is sketched, and the concept of society-nature coevolution is positioned in this broader field  ...  The outlook these articles provide suggests that research into society-nature coevolution should play a key role in identifying physically, biologically and socially accessible pathways to sustainability  ...  It is in this latter sense that Sieferle uses the term to specify culture as a system sui generis. Notes See  ... 
doi:10.1111/j.1468-0467.2011.00382.x fatcat:3arjpuappvexlb3xaniiftynee

Symbiotic coevolutionary genetic programming: a benchmarking study under large attribute spaces

John A. Doucette, Andrew R. McIntyre, Peter Lichodzijewski, Malcolm I. Heywood
2011 Genetic Programming and Evolvable Machines  
Specifically, competitive coevolution provides the basis for scaling the algorithm to data sets with large instance counts; whereas cooperative coevolution provides a framework for problem decomposition  ...  Classification under large attribute spaces represents a dual learning problem in which attribute subspaces need to be identified at the same time as the classifier design is established.  ...  Symbiosis as a metaphor for Cooperative Coevolution Cooperative models of coevolution assume explicit support for a divide and conquer approach to collaborative problem solving, thus multiple individuals  ... 
doi:10.1007/s10710-011-9151-4 fatcat:fpngrehicng6bggiuswesed6dm

Managing team-based problem solving with symbiotic bid-based genetic programming

Peter Lichodzijewski, Malcolm I. Heywood
2008 Proceedings of the 10th annual conference on Genetic and evolutionary computation - GECCO '08  
This paper proposes a symbiotic relationship that continues to support the cooperative bid-based process for problem decomposition while making the credit assignment process much clearer.  ...  Bid-based Genetic Programming (GP) provides an elegant mechanism for facilitating cooperative problem decomposition without an a priori specification of the number of team members.  ...  INTRODUCTION The team-based metaphor for problem decomposition under GP is a cooperative coevolutionary model in which the goal is to evolve a set of individuals that learn to interact without any additional  ... 
doi:10.1145/1389095.1389162 dblp:conf/gecco/LichodzijewskiH08 fatcat:ldunu4rkffbx5aomuagj3pq6hi

Cooperative Problem Decomposition in Pareto Competitive Classifier Models of Coevolution [chapter]

Andrew R. McIntyre, Malcolm I. Heywood
2008 Lecture Notes in Computer Science  
Pareto competitive models of coevolution have the potential to provide a number of distinct advantages over the canonical approach to training under the Genetic Programming (GP) classifier domain.  ...  That is to say, which models are used when, and what are the implications for solution modularity as it relates, for example, to the assignment of exemplars to solution participants.  ...  In the following, models for Pareto competitive coevolutionary and Pareto cooperative-competitive models of GP coevolution are introduced, Section 2.  ... 
doi:10.1007/978-3-540-78671-9_25 fatcat:4zcvpth4abb5hpf6jdfnhlywgq

Discrete and Continuous Optimization Based on Hierarchical Artificial Bee Colony Optimizer

Lianbo Ma, Kunyuan Hu, Yunlong Zhu, Ben Niu, Hanning Chen, Maowei He
2014 Journal of Applied Mathematics  
In the bottom level, each subpopulation employing the canonical ABC method searches the part-dimensional optimum in parallel, which can be constructed into a complete solution for the upper level.  ...  At the same time, the comprehensive learning method with crossover and mutation operator is applied to enhance the global search ability between species.  ...  Peng and Lu [26] presented a similar PSO framework, in which several swarms evolve in parallel with mutation operator.  ... 
doi:10.1155/2014/402616 fatcat:zvqucvyjizazvntghxk2ou4h6a

Three-cornered coevolution learning classifier systems for classification tasks

Syahaneim Marzukhi, Will N. Browne, Mengjie Zhang
2014 Proceedings of the 2014 conference on Genetic and evolutionary computation - GECCO '14  
agent and two classification agents) learn and adapt to the changes of the problems without human involvement.  ...  This thesis introduces a Three-Cornered Coevolution System that is capable of addressing classification tasks through coevolution (coadaptive evolution) where three different agents (i.e. a generation  ...  For instance, the Three-Cornered Coevolution System can be further enhanced to become a new cooperative coevolution system.  ... 
doi:10.1145/2576768.2598235 dblp:conf/gecco/MarzukhiBZ14 fatcat:6fy5jzcd6fbjjf5kdosuxbocta

From residue coevolution to protein conformational ensembles and functional dynamics

Ludovico Sutto, Simone Marsili, Alfonso Valencia, Francesco Luigi Gervasio
2015 Proceedings of the National Academy of Sciences of the United States of America  
and to the cooperativity of the coevolving pairs.  ...  As a result, a protein family is free to explore a large space of possible sequences while at the same time preserving a common structural framework.  ... 
doi:10.1073/pnas.1508584112 pmid:26487681 pmcid:PMC4640757 fatcat:6trycgdihndtdjgf4qmmw5kz7y

Special issue on evolutionary computing and complex systems

Alexandru-Adrian Tantar, Emilia Tantar, Pascal Bouvry, Oliver Schütze, Carlos A. Coello Coello, Pierre Del Moral
2013 Soft Computing - A Fusion of Foundations, Methodologies and Applications  
The approach relies on a cooperative coevolution Artificial Bee Colony (ABC) algorithm with orthogonal experimental design.  ...  The algorithm is compared to several cooperative coevolution paradigms, i.e. a standard ABC approach as well as differential evolution and particle swarm variants.  ... 
doi:10.1007/s00500-013-1049-z fatcat:6p6pspmhh5g5pnk7pigacobxeu
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