Xiaodong Li


Ontology type: schema:Person     


Person Info

NAME

Xiaodong

SURNAME

Li

Publications in SciGraph latest 50 shown

  • 2018-08-22 A Probabilistic Tree-Based Representation for Non-convex Minimum Cost Flow Problems in PARALLEL PROBLEM SOLVING FROM NATURE – PPSN XV
  • 2018-08-22 Conditional Preference Learning for Personalized and Context-Aware Journey Planning in PARALLEL PROBLEM SOLVING FROM NATURE – PPSN XV
  • 2018-08-21 Workshops at PPSN 2018 in PARALLEL PROBLEM SOLVING FROM NATURE – PPSN XV
  • 2017-12 ANTS 2016 special issue: Editorial in SWARM INTELLIGENCE
  • 2017-10-14 An Evolutionary Approach for Learning Conditional Preference Networks from Inconsistent Examples in ADVANCED DATA MINING AND APPLICATIONS
  • 2017 Preliminary Study on Solving Coal Processing and Blending Problems Using Lexicographic Ordering in AI 2017: ADVANCES IN ARTIFICIAL INTELLIGENCE
  • 2017 An Evolutionary Multi-criteria Journey Planning Algorithm for Multimodal Transportation Networks in ARTIFICIAL LIFE AND COMPUTATIONAL INTELLIGENCE
  • 2017 Extending the Delaunay Triangulation Based Density Measurement to Many-Objective Optimization in ARTIFICIAL LIFE AND COMPUTATIONAL INTELLIGENCE
  • 2017 Estimating Passenger Preferences Using Implicit Relevance Feedback for Personalized Journey Planning in ARTIFICIAL LIFE AND COMPUTATIONAL INTELLIGENCE
  • 2017 Surrogate-Assisted Multi-swarm Particle Swarm Optimization of Morphing Airfoils in ARTIFICIAL LIFE AND COMPUTATIONAL INTELLIGENCE
  • 2016-01 On investigation of interdependence between sub-problems of the Travelling Thief Problem in SOFT COMPUTING
  • 2016 A Delaunay Triangulation Based Density Measurement for Evolutionary Multi-objective Optimization in ARTIFICIAL LIFE AND COMPUTATIONAL INTELLIGENCE
  • 2016 A Hybrid Imperialist Competitive Algorithm for the Flexible Job Shop Problem in ARTIFICIAL LIFE AND COMPUTATIONAL INTELLIGENCE
  • 2015 Learning Options for an MDP from Demonstrations in ARTIFICIAL LIFE AND COMPUTATIONAL INTELLIGENCE
  • 2015 Preference-Based Multiobjective Particle Swarm Optimization for Airfoil Design in SPRINGER HANDBOOK OF COMPUTATIONAL INTELLIGENCE
  • 2014-08 An analysis of the velocity updating rule of the particle swarm optimization algorithm in JOURNAL OF HEURISTICS
  • 2014 Improving Efficiency of Heuristics for the Large Scale Traveling Thief Problem in SIMULATED EVOLUTION AND LEARNING
  • 2014 Why Advanced Population Initialization Techniques Perform Poorly in High Dimension? in SIMULATED EVOLUTION AND LEARNING
  • 2014 A Multi-Objective A* Search Based on Non-dominated Sorting in SIMULATED EVOLUTION AND LEARNING
  • 2014 Scaling Up Solutions to Storage Location Assignment Problems by Genetic Programming in SIMULATED EVOLUTION AND LEARNING
  • 2013-01 Time series forecasting by evolving artificial neural networks with genetic algorithms, differential evolution and estimation of distribution algorithm in NEURAL COMPUTING AND APPLICATIONS
  • 2012-08 A new discrete electromagnetism-based meta-heuristic for solving the multidimensional knapsack problem using genetic operators in OPERATIONAL RESEARCH
  • 2011-09 Guest Editorial: special issue on evolutionary optimisation and learning in SOFT COMPUTING
  • 2011-09 Improving the performance and scalability of Differential Evolution on problems exhibiting parameter interactions in SOFT COMPUTING
  • 2011-09 A framework for generating tunable test functions for multimodal optimization in SOFT COMPUTING
  • 2011 Atavistic Strategy for Genetic Algorithm in ADVANCES IN SWARM INTELLIGENCE
  • 2011 Developing Niching Algorithms in Particle Swarm Optimization in HANDBOOK OF SWARM INTELLIGENCE
  • 2010 Improving Local Convergence in Particle Swarms by Fitness Approximation Using Regression in COMPUTATIONAL INTELLIGENCE IN EXPENSIVE OPTIMIZATION PROBLEMS
  • 2010 A Comparative Study of CMA-ES on Large Scale Global Optimisation in AI 2010: ADVANCES IN ARTIFICIAL INTELLIGENCE
  • 2009-12 Special issue on simulated evolution and learning in EVOLUTIONARY INTELLIGENCE
  • 2009 A Distance Metric for Evolutionary Many-Objective Optimization Algorithms Using User-Preferences in AI 2009: ADVANCES IN ARTIFICIAL INTELLIGENCE
  • 2008-06 Theoretical foundations of evolutionary computation in GENETIC PROGRAMMING AND EVOLVABLE MACHINES
  • 2008 Choosing Leaders for Multi-objective PSO Algorithms Using Differential Evolution in SIMULATED EVOLUTION AND LEARNING
  • 2008 A Generator for Multimodal Test Functions with Multiple Global Optima in SIMULATED EVOLUTION AND LEARNING
  • 2008 Reference Point-Based Particle Swarm Optimization Using a Steady-State Approach in SIMULATED EVOLUTION AND LEARNING
  • 2008 Swarm Intelligence in Optimization in SWARM INTELLIGENCE
  • 2008 Improving the Performance and Scalability of Differential Evolution in SIMULATED EVOLUTION AND LEARNING
  • 2008 Particle Swarms for Dynamic Optimization Problems in SWARM INTELLIGENCE
  • 2006 Rotationally Invariant Crossover Operators in Evolutionary Multi-objective Optimization in SIMULATED EVOLUTION AND LEARNING
  • 2004 A Cooperative Coevolutionary Multiobjective Algorithm Using Non-dominated Sorting in GENETIC AND EVOLUTIONARY COMPUTATION – GECCO 2004
  • 2004 Improving Generalisation Performance Through Multiobjective Parsimony Enforcement in GENETIC AND EVOLUTIONARY COMPUTATION – GECCO 2004
  • 2004 Adaptively Choosing Neighbourhood Bests Using Species in a Particle Swarm Optimizer for Multimodal Function Optimization in GENETIC AND EVOLUTIONARY COMPUTATION – GECCO 2004
  • 2004 Better Spread and Convergence: Particle Swarm Multiobjective Optimization Using the Maximin Fitness Function in GENETIC AND EVOLUTIONARY COMPUTATION – GECCO 2004
  • 2004 Solving Rotated Multi-objective Optimization Problems Using Differential Evolution in AI 2004: ADVANCES IN ARTIFICIAL INTELLIGENCE
  • 2003-06-18 A Non-dominated Sorting Particle Swarm Optimizer for Multiobjective Optimization in GENETIC AND EVOLUTIONARY COMPUTATION — GECCO 2003
  • 2003 A Real-Coded Predator-Prey Genetic Algorithm for Multiobjective Optimization in EVOLUTIONARY MULTI-CRITERION OPTIMIZATION
  • 2002-10-04 Parameter Control within a Co-operative Co-evolutionary Genetic Algorithm in PARALLEL PROBLEM SOLVING FROM NATURE — PPSN VII
  • JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "affiliation": [
          {
            "affiliation": {
              "id": "https://www.grid.ac/institutes/grid.1017.7", 
              "type": "Organization"
            }, 
            "isCurrent": true, 
            "type": "OrganizationRole"
          }, 
          {
            "id": "https://www.grid.ac/institutes/grid.29980.3a", 
            "type": "Organization"
          }
        ], 
        "familyName": "Li", 
        "givenName": "Xiaodong", 
        "id": "sg:person.010004731721.09", 
        "identifier": {
          "name": "orcid_id", 
          "type": "PropertyValue", 
          "value": [
            "0000-0003-0346-1526"
          ]
        }, 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010004731721.09", 
          "https://orcid.org/0000-0003-0346-1526"
        ], 
        "sdDataset": "persons", 
        "sdDatePublished": "2019-03-07T13:46", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-uberresearch-data-dimensions-researchers-20181010/20181011/dim_researchers/base/researchers_1554.json", 
        "type": "Person"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/person.010004731721.09'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/person.010004731721.09'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/person.010004731721.09'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/person.010004731721.09'


     




    Preview window. Press ESC to close (or click here)


    ...