• case-insensitive prefix search: default
  e.g., sig matches "SIGIR" as well as "signal"
• exact word search: append dollar sign ($) to word
  e.g., graph$ matches "graph", but not "graphics"
• boolean and: separate words by space
  e.g., codd model
• boolean or: connect words by pipe symbol (|)
  e.g., graph|network

Update May 7, 2017: Please note that we had to disable the phrase search operator (.) and the boolean not operator (-) due to technical problems. For the time being, phrase search queries will yield regular prefix search result, and search terms preceded by a minus will be interpreted as regular (positive) search terms.

found 673 matches

• 2023
• 
  Bekir Afsar, Johanna M. Silvennoinen, Kaisa Miettinen:
  A Systematic Way of Structuring Real-World Multiobjective Optimization Problems. EMO 2023: 593-605
• 
  Divyam Aggarwal, Dhish Kumar Saxena, Thomas Bäck, Michael Emmerich:
  Real-World Airline Crew Pairing Optimization: Customized Genetic Algorithm Versus Column Generation Method. EMO 2023: 518-531
• 
  Drishti Bhasin, Sajag Swami, Sarthak Sharma, Saumya Sah, Dhish Kumar Saxena, Kalyanmoy Deb:
  Investigating Innovized Progress Operators with Different Machine Learning Methods. EMO 2023: 134-146
• 
  Paramita Biswas, Anirban Mukhopadhyay:
  A Multi-objective Evolutionary Framework for Identifying Dengue Stage-Specific Differentially Co-expressed and Functionally Enriched Gene Modules. EMO 2023: 504-517
• 
  Renzhi Chen, Ke Li:
  Data-Driven Evolutionary Multi-objective Optimization Based on Multiple-Gradient Descent for Disconnected Pareto Fronts. EMO 2023: 56-70
• 
  Kalyanmoy Deb, Aryan Gondkar, Anirudh Suresh:
  Learning to Predict Pareto-Optimal Solutions from Pseudo-weights. EMO 2023: 191-204
• 
  Timo M. Deist, Monika Grewal, Frank J. W. M. Dankers, Tanja Alderliesten, Peter A. N. Bosman:
  Multi-objective Learning Using HV Maximization. EMO 2023: 103-117
• 
  André H. Deutz, Michael Emmerich, Hao Wang:
  The Hypervolume Indicator Hessian Matrix: Analytical Expression, Computational Time Complexity, and Sparsity. EMO 2023: 405-418
• 
  António Gaspar-Cunha, Paulo Costa, Francisco José Monaco, Alexandre C. B. Delbem:
  Scalability of Multi-objective Evolutionary Algorithms for Solving Real-World Complex Optimization Problems. EMO 2023: 86-100
• 
  Abhiroop Ghosh, Kalyanmoy Deb, Ronald C. Averill, Erik D. Goodman:
  IK-EMOViz: An Interactive Knowledge-Based Evolutionary Multi-objective Optimization Framework. EMO 2023: 606-619
• 
  Ritam Guha, Kalyanmoy Deb:
  RegEMO: Sacrificing Pareto-Optimality for Regularity in Multi-objective Problem-Solving. EMO 2023: 29-42
• 
  Ping Guo, Qingfu Zhang, Xi Lin:
  Approximation of a Pareto Set Segment Using a Linear Model with Sharing Variables. EMO 2023: 247-259
• 
  Hao Hao, Aimin Zhou:
  A Relation Surrogate Model for Expensive Multiobjective Continuous and Combinatorial Optimization. EMO 2023: 205-217
• 
  Linjun He, Yang Nan, Hisao Ishibuchi, Dipti Srinivasan:
  Preference-Based Nonlinear Normalization for Multiobjective Optimization. EMO 2023: 563-577
• 
  Julia Heise, Sanaz Mostaghim:
  Online Learning Hyper-Heuristics in Multi-Objective Evolutionary Algorithms. EMO 2023: 162-175
• 
  Steve Huntsman:
  Diversity Enhancement via Magnitude. EMO 2023: 377-390
• 
  Hisao Ishibuchi, Yang Nan, Lie Meng Pang:
  Performance Evaluation of Multi-objective Evolutionary Algorithms Using Artificial and Real-world Problems. EMO 2023: 333-347
• 
  Victoria Johnson, João A. Duro, Visakan Kadirkamanathan, Robin C. Purshouse:
  A Scalable Test Suite for Bi-objective Multidisciplinary Optimization. EMO 2023: 319-332
• 
  Angus Kenny, Tapabrata Ray, Hemant Kumar Singh, Xiaodong Li:
  A Test Suite for Multi-objective Multi-fidelity Optimization. EMO 2023: 361-373
• 
  Giomara Lárraga, Bhupinder Singh Saini, Kaisa Miettinen:
  Incorporating Preference Information Interactively in NSGA-III by the Adaptation of Reference Vectors. EMO 2023: 578-592
• 
  Clément Legrand, Diego Cattaruzza, Laetitia Jourdan, Marie-Eléonore Kessaci:
  Improving MOEA/D with Knowledge Discovery. Application to a Bi-objective Routing Problem. EMO 2023: 462-475
• 
  Arnaud Liefooghe, Sébastien Vérel, Tinkle Chugh, Jonathan E. Fieldsend, Richard Allmendinger, Kaisa Miettinen:
  Feature-Based Benchmarking of Distance-Based Multi/Many-objective Optimisation Problems: A Machine Learning Perspective. EMO 2023: 260-273
• 
  Shiqing Liu, Xueming Yan, Yaochu Jin:
  End-to-End Pareto Set Prediction with Graph Neural Networks for Multi-objective Facility Location. EMO 2023: 147-161
• 
  Fei Liu, Qingfu Zhang:
  A Two-Stage Algorithm for Integer Multiobjective Simulation Optimization. EMO 2023: 17-28
• 
  Sebastian Mai, Tobias Benecke, Sanaz Mostaghim:
  MACO: A Real-World Inspired Benchmark for Multi-objective Evolutionary Algorithms. EMO 2023: 305-318
• 
  Krzysztof Michalak:
  Evolutionary Algorithms with Machine Learning Models for Multiobjective Optimization in Epidemics Control. EMO 2023: 435-448
• 
  Krzysztof Michalak, Mario Giacobini:
  Multiobjective Optimization of Evolutionary Neural Networks for Animal Trade Movements Prediction. EMO 2023: 532-545
• 
  Yang Nan, Hisao Ishibuchi, Tianye Shu, Ke Shang:
  Two-Stage Greedy Approximated Hypervolume Subset Selection for Large-Scale Problems. EMO 2023: 391-404
• 
  Lie Meng Pang, Yang Nan, Hisao Ishibuchi:
  Partially Degenerate Multi-objective Test Problems. EMO 2023: 277-290
• 
  Ksenia Pereverdieva, Michael Emmerich, André H. Deutz, Tessa Ezendam, Thomas Bäck, Hèrm Hofmeyer:
  The Prism-Net Search Space Representation for Multi-objective Building Spatial Design. EMO 2023: 476-489

skipping 643 more matches