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352 results on '"Kirley, Michael"'

1. FireRisk: A Remote Sensing Dataset for Fire Risk Assessment with Benchmarks Using Supervised and Self-supervised Learning

Author

Shen, Shuchang, Seneviratne, Sachith, Wanyan, Xinye, and Kirley, Michael

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

In recent decades, wildfires, as widespread and extremely destructive natural disasters, have caused tremendous property losses and fatalities, as well as extensive damage to forest ecosystems. Many fire risk assessment projects have been proposed to prevent wildfires, but GIS-based methods are inherently challenging to scale to different geographic areas due to variations in data collection and local conditions. Inspired by the abundance of publicly available remote sensing projects and the burgeoning development of deep learning in computer vision, our research focuses on assessing fire risk using remote sensing imagery. In this work, we propose a novel remote sensing dataset, FireRisk, consisting of 7 fire risk classes with a total of 91872 labelled images for fire risk assessment. This remote sensing dataset is labelled with the fire risk classes supplied by the Wildfire Hazard Potential (WHP) raster dataset, and remote sensing images are collected using the National Agriculture Imagery Program (NAIP), a high-resolution remote sensing imagery program. On FireRisk, we present benchmark performance for supervised and self-supervised representations, with Masked Autoencoders (MAE) pre-trained on ImageNet1k achieving the highest classification accuracy, 65.29%. This remote sensing dataset, FireRisk, provides a new direction for fire risk assessment, and we make it publicly available on https://github.com/CharmonyShen/FireRisk., Comment: 10 pages, 6 figures, 1 table, 1 equation

Published
2023

2. Extending global-local view alignment for self-supervised learning with remote sensing imagery

Author

Wanyan, Xinye, Seneviratne, Sachith, Shen, Shuchang, and Kirley, Michael

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Since large number of high-quality remote sensing images are readily accessible, exploiting the corpus of images with less manual annotation draws increasing attention. Self-supervised models acquire general feature representations by formulating a pretext task that generates pseudo-labels for massive unlabeled data to provide supervision for training. While prior studies have explored multiple self-supervised learning techniques in remote sensing domain, pretext tasks based on local-global view alignment remain underexplored, despite achieving state-of-the-art results on natural imagery. Inspired by DINO, which employs an effective representation learning structure with knowledge distillation based on global-local view alignment, we formulate two pretext tasks for self-supervised learning on remote sensing imagery (SSLRS). Using these tasks, we explore the effectiveness of positive temporal contrast as well as multi-sized views on SSLRS. We extend DINO and propose DINO-MC which uses local views of various sized crops instead of a single fixed size in order to alleviate the limited variation in object size observed in remote sensing imagery. Our experiments demonstrate that even when pre-trained on only 10% of the dataset, DINO-MC performs on par or better than existing state-of-the-art SSLRS methods on multiple remote sensing tasks, while using less computational resources. All codes, models, and results are released at https://github.com/WennyXY/DINO-MC.

Published
2023

3. Dynamic Landscape Analysis for Constrained Multiobjective Optimization Problems

Author

Alsouly, Hanan, Kirley, Michael, Muñoz, Mario Andrés, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Liu, Tongliang, editor, Webb, Geoff, editor, Yue, Lin, editor, and Wang, Dadong, editor

Published
2024
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4. Cooperative Multi-Agent Reinforcement Learning with Dynamic Target Localization: A Reward Sharing Approach

Author

Wickramaarachchi, Helani, Kirley, Michael, Geard, Nicholas, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Liu, Tongliang, editor, Webb, Geoff, editor, Yue, Lin, editor, and Wang, Dadong, editor

Published
2024
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5. Reproducibility and Baseline Reporting for Dynamic Multi-objective Benchmark Problems

Author

Herring, Daniel, Kirley, Michael, and Yao, Xin

Subjects
Computer Science - Neural and Evolutionary Computing
Abstract

Dynamic multi-objective optimization problems (DMOPs) are widely accepted to be more challenging than stationary problems due to the time-dependent nature of the objective functions and/or constraints. Evaluation of purpose-built algorithms for DMOPs is often performed on narrow selections of dynamic instances with differing change magnitude and frequency or a limited selection of problems. In this paper, we focus on the reproducibility of simulation experiments for parameters of DMOPs. Our framework is based on an extension of PlatEMO, allowing for the reproduction of results and performance measurements across a range of dynamic settings and problems. A baseline schema for dynamic algorithm evaluation is introduced, which provides a mechanism to interrogate performance and optimization behaviours of well-known evolutionary algorithms that were not designed specifically for DMOPs. Importantly, by determining the maximum capability of non-dynamic multi-objective evolutionary algorithms, we can establish the minimum capability required of purpose-built dynamic algorithms to be useful. The simplest modifications to manage dynamic changes introduce diversity. Allowing non-dynamic algorithms to incorporate mutated/random solutions after change events determines the improvement possible with minor algorithm modifications. Future expansion to include current dynamic algorithms will enable reproduction of their results and verification of their abilities and performance across DMOP benchmark space., Comment: Accepted for publication in "Proceedings of the Genetic and Evolutionary Computation Conference (GECCO) 2022"

Published
2022

6. An Instance Space Analysis of Constrained Multi-Objective Optimization Problems

Author

Alsouly, Hanan, Kirley, Michael, and Muñoz, Mario Andrés

Subjects
Computer Science - Neural and Evolutionary Computing
Abstract

Multi-objective optimization problems with constraints (CMOPs) are generally considered more challenging than those without constraints. This in part can be attributed to the creation of infeasible regions generated by the constraint functions, and/or the interaction between constraints and objectives. In this paper, we explore the relationship between constrained multi-objective evolutionary algorithms (CMOEAs) performance and CMOP instances characteristics using Instance Space Analysis (ISA). To do this, we extend recent work focused on the use of Landscape Analysis features to characterise CMOP. Specifically, we scrutinise the multi-objective landscape and introduce new features to describe the multi-objective-violation landscape, formed by the interaction between constraint violation and multi-objective fitness. Detailed evaluation of problem-algorithm footprints spanning six CMOP benchmark suites and fifteen CMOEAs, illustrates that ISA can effectively capture the strength and weakness of the CMOEAs. We conclude that two key characteristics, the isolation of non-dominate set and the correlation between constraints and objectives evolvability, have the greatest impact on algorithm performance. However, the current benchmarks problems do not provide enough diversity to fully reveal the efficacy of CMOEAs evaluated.

Published
2022
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10. Machine learning with incomplete datasets using multi-objective optimization models

Author

Khorshidi, Hadi A., Kirley, Michael, and Aickelin, Uwe

Subjects
Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing
Abstract

Machine learning techniques have been developed to learn from complete data. When missing values exist in a dataset, the incomplete data should be preprocessed separately by removing data points with missing values or imputation. In this paper, we propose an online approach to handle missing values while a classification model is learnt. To reach this goal, we develop a multi-objective optimization model with two objective functions for imputation and model selection. We also propose three formulations for imputation objective function. We use an evolutionary algorithm based on NSGA II to find the optimal solutions as the Pareto solutions. We investigate the reliability and robustness of the proposed model using experiments by defining several scenarios in dealing with missing values and classification. We also describe how the proposed model can contribute to medical informatics. We compare the performance of three different formulations via experimental results. The proposed model results get validated by comparing with a comparable literature., Comment: 2020 International Joint Conference on Neural Networks (IJCNN)

Published
2020

11. Boosting Ant Colony Optimization via Solution Prediction and Machine Learning

Author

Sun, Yuan, Wang, Sheng, Shen, Yunzhuang, Li, Xiaodong, Ernst, Andreas T., and Kirley, Michael

Subjects
Computer Science - Neural and Evolutionary Computing, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

This paper introduces an enhanced meta-heuristic (ML-ACO) that combines machine learning (ML) and ant colony optimization (ACO) to solve combinatorial optimization problems. To illustrate the underlying mechanism of our ML-ACO algorithm, we start by describing a test problem, the orienteering problem. In this problem, the objective is to find a route that visits a subset of vertices in a graph within a time budget to maximize the collected score. In the first phase of our ML-ACO algorithm, an ML model is trained using a set of small problem instances where the optimal solution is known. Specifically, classification models are used to classify an edge as being part of the optimal route, or not, using problem-specific features and statistical measures. The trained model is then used to predict the probability that an edge in the graph of a test problem instance belongs to the corresponding optimal route. In the second phase, we incorporate the predicted probabilities into the ACO component of our algorithm, i.e., using the probability values as heuristic weights or to warm start the pheromone matrix. Here, the probability values bias sampling towards favoring those predicted high-quality edges when constructing feasible routes. We have tested multiple classification models including graph neural networks, logistic regression and support vector machines, and the experimental results show that our solution prediction approach consistently boosts the performance of ACO. Further, we empirically show that our ML model trained on small synthetic instances generalizes well to large synthetic and real-world instances. Our approach integrating ML with a meta-heuristic is generic and can be applied to a wide range of optimization problems.

Published
2020

13. Dynamic Multi-objective Optimization of the Travelling Thief Problem

Author

Herring, Daniel, Kirley, Michael, and Yao, Xin

Subjects
Computer Science - Neural and Evolutionary Computing
Abstract

Investigation of detailed and complex optimisation problem formulations that reflect realistic scenarios is a burgeoning field of research. A growing body of work exists for the Travelling Thief Problem, including multi-objective formulations and comparisons of exact and approximate methods to solve it. However, as many realistic scenarios are non-static in time, dynamic formulations have yet to be considered for the TTP. Definition of dynamics within three areas of the TTP problem are addressed; in the city locations, availability map and item values. Based on the elucidation of solution conservation between initial sets and obtained non-dominated sets, we define a range of initialisation mechanisms using solutions generated via solvers, greedily and randomly. These are then deployed to seed the population after a change and the performance in terms of hypervolume and spread is presented for comparison. Across a range of problems with varying TSP-component and KP-component sizes, we observe interesting trends in line with existing conclusions; there is little benefit to using randomisation as a strategy for initialisation of solution populations when the optimal TSP and KP component solutions can be exploited. Whilst these separate optima don't guarantee good TTP solutions, when combined, provide better initial performance and therefore in some examined instances, provides the best response to dynamic changes. A combined approach that mixes solution generation methods to provide a composite population in response to dynamic changes provides improved performance in some instances for the different dynamic TTP formulations. Potential for further development of a more cooperative combined method are realised to more cohesively exploit known information about the problems.

Published
2020

14. Analysing Multiobjective Optimization Using Evolutionary Path Length Correlation

Author

Herring, Daniel, Pakravan, Dean, Kirley, Michael, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Long, Guodong, editor, Yu, Xinghuo, editor, and Wang, Sen, editor

Published
2022
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17. Characterising Deception in AI: A Survey

Author

Masters, Peta, Smith, Wally, Sonenberg, Liz, Kirley, Michael, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Sarkadi, Stefan, editor, Wright, Benjamin, editor, Masters, Peta, editor, and McBurney, Peter, editor

Published
2021
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20. Multi-Objective Problem Solving With Offspring on Enterprise Clouds

Author

Vecchiola, Christian, Kirley, Michael, and Buyya, Rajkumar

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing, C.2.4
Abstract

In this paper, we present a distributed implementation of a network based multi-objective evolutionary algorithm, called EMO, by using Offspring. Network based evolutionary algorithms have proven to be effective for multi-objective problem solving. They feature a network of connections between individuals that drives the evolution of the algorithm. Unfortunately, they require large populations to be effective and a distributed implementation can leverage the computation time. Most of the existing frameworks are limited to providing solutions that are basic or specific to a given algorithm. Our Offspring framework is a plug-in based software environment that allows rapid deployment and execution of evolutionary algorithms on distributed computing environments such as Enterprise Clouds. Its features and benefits are presented by describing the distributed implementation of EMO., Comment: 8 pages

Published
2009

22. A Memetic Cooperative Co-evolution Model for Large Scale Continuous Optimization

Author

Sun, Yuan, Kirley, Michael, Halgamuge, Saman K., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Wagner, Markus, editor, Li, Xiaodong, editor, and Hendtlass, Tim, editor

Published
2017
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23. Generation and Exploration of Architectural Form Using a Composite Cellular Automata

Author

Cruz, Camilo, Kirley, Michael, Karakiewicz, Justyna, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Wagner, Markus, editor, Li, Xiaodong, editor, and Hendtlass, Tim, editor

Published
2017
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29. Towards a Middleware for Context-Aware Health Monitoring

Author

Oliveira, Eduardo A., Koch, Fernando, Kirley, Michael, dos Passos Barros, Carlos Victor G., Diniz Junqueira Barbosa, Simone, Series editor, Chen, Phoebe, Series editor, Du, Xiaoyong, Series editor, Filipe, Joaquim, Series editor, Kara, Orhun, Series editor, Kotenko, Igor, Series editor, Liu, Ting, Series editor, Sivalingam, Krishna M., Series editor, Washio, Takashi, Series editor, Koch, Fernando, editor, Guttmann, Christian, editor, and Busquets, Didac, editor

Published
2015
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30. Heterogeneous Payoffs and Social Diversity in the Spatial Prisoner’s Dilemma game

Author

Rezaei, Golriz, Kirley, Michael, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Li, Xiaodong, editor, Kirley, Michael, editor, Zhang, Mengjie, editor, Green, David, editor, Ciesielski, Vic, editor, Abbass, Hussein, editor, Michalewicz, Zbigniew, editor, Hendtlass, Tim, editor, Deb, Kalyanmoy, editor, Tan, Kay Chen, editor, Branke, Jürgen, editor, and Shi, Yuhui, editor

Published
2008
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31. Phylogeny Inference Using a Multi-objective Evolutionary Algorithm with Indirect Representation

Author

Hassan, Md. Rafiul, Hossain, M. Maruf, Karmakar, C. K., Kirley, Michael, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Li, Xiaodong, editor, Kirley, Michael, editor, Zhang, Mengjie, editor, Green, David, editor, Ciesielski, Vic, editor, Abbass, Hussein, editor, Michalewicz, Zbigniew, editor, Hendtlass, Tim, editor, Deb, Kalyanmoy, editor, Tan, Kay Chen, editor, Branke, Jürgen, editor, and Shi, Yuhui, editor

Published
2008
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35. Adaptive Risk Aversion in Social Dilemmas

Author

Kirley, Michael, von der Osten, Friedrich Burkhard, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Dick, Grant, editor, Browne, Will N., editor, Whigham, Peter, editor, Zhang, Mengjie, editor, Bui, Lam Thu, editor, Ishibuchi, Hisao, editor, Jin, Yaochu, editor, Li, Xiaodong, editor, Shi, Yuhui, editor, Singh, Pramod, editor, Tan, Kay Chen, editor, and Tang, Ke, editor

Published
2014
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37. A Multi-agent Based Migration Model for Evolving Cooperation in the Spatial N-Player Snowdrift Game

Author

Chiong, Raymond, Kirley, Michael, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Boella, Guido, editor, Elkind, Edith, editor, Savarimuthu, Bastin Tony Roy, editor, Dignum, Frank, editor, and Purvis, Martin K., editor

Published
2013
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38. An Agent-Based Model of Stereotype Communication

Author

Pfau, Jens, Kirley, Michael, Kashima, Yoshihisa, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Cranefield, Stephen, editor, and Song, Insu, editor

Published
2012
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39. A Meta-learning Prediction Model of Algorithm Performance for Continuous Optimization Problems

Author

Muñoz, Mario A., Kirley, Michael, Halgamuge, Saman K., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Coello, Carlos A. Coello, editor, Cutello, Vincenzo, editor, Deb, Kalyanmoy, editor, Forrest, Stephanie, editor, Nicosia, Giuseppe, editor, and Pavone, Mario, editor

Published
2012
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40. DINO-MC: Self-supervised Contrastive Learning for Remote Sensing Imagery with Multi-sized Local Crops

Author

Wanyan, Xinye, Seneviratne, Sachith, Shen, Shuchang, and Kirley, Michael

Subjects
FOS: Computer and information sciences, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition
Abstract

Due to the costly nature of remote sensing image labeling and the large volume of available unlabeled imagery, self-supervised methods that can learn feature representations without manual annotation have received great attention. While prior works have explored self-supervised learning in remote sensing tasks, pretext tasks based on local-global view alignment remain underexplored. Inspired by DINO, which employs an effective representation learning structure with knowledge distillation based on global-local view alignment, we formulate two pretext tasks for use in self-supervised learning on remote sensing imagery (SSLRS). Using these tasks, we explore the effectiveness of positive temporal contrast as well as multi-sized views on SSLRS. Moreover, we extend DINO and propose DINO-MC which uses local views of various sized crops instead of a single fixed size. Our experiments demonstrate that even when pre-trained on only 10% of the dataset, DINO-MC performs on par or better than existing state of the art SSLRS methods on multiple remote sensing tasks, while using less computational resources. All codes, models and results are available at https://github.com/WennyXY/DINO-MC.

Published
2023

41. Supplemental Material - Development and evaluation of probabilistic forecasting methods for small area populations

Author

Grossman, Irina, Bandara, Kasun, Wilson, Tom, and Kirley, Michael

Subjects
FOS: Social and economic geography, Geography, 120599 Urban and Regional Planning not elsewhere classified
Abstract

Supplemental Material for Development and evaluation of probabilistic forecasting methods for small area populations by Irina Grossman, Kasun Bandara, Tom Wilson, and Michael Kirley in Environment and Planning B: Urban Analytics and City Science

Published
2023
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42. TATM: A Trust Mechanism for Social Traders in Double Auctions

Author

Dumesny, Jacob, Miller, Tim, Kirley, Michael, Sonenberg, Liz, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Wang, Dianhui, editor, and Reynolds, Mark, editor

Published
2011
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43. Guided Rule Discovery in XCS for High-Dimensional Classification Problems

Author

Abedini, Mani, Kirley, Michael, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Wang, Dianhui, editor, and Reynolds, Mark, editor

Published
2011
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44. Evolving Cooperation in the Spatial N-player Snowdrift Game

Author

Chiong, Raymond, Kirley, Michael, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, and Li, Jiuyong, editor

Published
2011
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45. CoXCS: A Coevolutionary Learning Classifier Based on Feature Space Partitioning

Author

Abedini, Mani, Kirley, Michael, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Nicholson, Ann, editor, and Li, Xiaodong, editor

Published
2009
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46. Evolving Cooperation in the N-player Prisoner’s Dilemma: A Social Network Model

Author

Rezaei, Golriz, Kirley, Michael, Pfau, Jens, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Korb, Kevin, editor, Randall, Marcus, editor, and Hendtlass, Tim, editor

Published
2009
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47. Co-evolutionary Learning in the N-player Iterated Prisoner’s Dilemma with a Structured Environment

Author

Chiong, Raymond, Kirley, Michael, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Korb, Kevin, editor, Randall, Marcus, editor, and Hendtlass, Tim, editor

Published
2009
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50. Multiobjective Evolutionary Algorithms on Complex Networks

Author

Kirley, Michael, Stewart, Robert, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Rangan, C. Pandu, editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Obayashi, Shigeru, editor, Deb, Kalyanmoy, editor, Poloni, Carlo, editor, Hiroyasu, Tomoyuki, editor, and Murata, Tadahiko, editor

Published
2007
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