Generalized Differential Evolution for Numerical and Evolutionary Optimization View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2017

AUTHORS

Saku Kukkonen , Carlos A. Coello Coello

ABSTRACT

This chapter is about Generalized Differential Evolution (GDE), which is a general purpose optimizer for global nonlinear optimization. It is based on Differential Evolution (DE), which has been gaining popularity because of its simplicity and good observed performance. GDE extends DE for problems with several objectives and constraints. The chapter concentrates on describing different development phases and performance of GDE but it also contains a brief listing of other multi-objective DE approaches. Ability to solve multi-objective problems is mainly discussed, but constraint handling and the effect of control parameters are also covered. It is found that the latest GDE version is effective and efficient for solving constrained multi-objective problems having different types of decision variables. More... »

PAGES

253-279

References to SciGraph publications

  • 2008. Multi-objective Optimization Using Differential Evolution: A Survey of the State-of-the-Art in ADVANCES IN DIFFERENTIAL EVOLUTION
  • 2007. Differential Evolution versus Genetic Algorithms in Multiobjective Optimization in EVOLUTIONARY MULTI-CRITERION OPTIMIZATION
  • 2006. A Multiobjective Differential Evolution Based on Decomposition for Multiobjective Optimization with Variable Linkages in PARALLEL PROBLEM SOLVING FROM NATURE - PPSN IX
  • 2005. DEMO: Differential Evolution for Multiobjective Optimization in EVOLUTIONARY MULTI-CRITERION OPTIMIZATION
  • 1998. Nonlinear Multiobjective Optimization in NONE
  • 2015. Comparison of Parameter Control Mechanisms in Multi-objective Differential Evolution in LEARNING AND INTELLIGENT OPTIMIZATION
  • 2004. Mechanical Component Design for Multiple Objectives Using Generalized Differential Evolution in ADAPTIVE COMPUTING IN DESIGN AND MANUFACTURE VI
  • 1997-12. Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces in JOURNAL OF GLOBAL OPTIMIZATION
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  • 2005. An EMO Algorithm Using the Hypervolume Measure as Selection Criterion in EVOLUTIONARY MULTI-CRITERION OPTIMIZATION
  • 2004. An Extension of Generalized Differential Evolution for Multi-objective Optimization with Constraints in PARALLEL PROBLEM SOLVING FROM NATURE - PPSN VIII
  • 2007. A Novel Differential Evolution Algorithm Based on ε-Domination and Orthogonal Design Method for Multiobjective Optimization in EVOLUTIONARY MULTI-CRITERION OPTIMIZATION
  • 2017-11. Gradient subspace approximation: a direct search method for memetic computing in SOFT COMPUTING
  • 2008. A Novel Opposition-Based Multi-objective Differential Evolution Algorithm for Multi-objective Optimization in ADVANCES IN COMPUTATION AND INTELLIGENCE
  • 2015. Geometric Differential Evolution in MOEA/D: A Preliminary Study in ADVANCES IN ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING
  • 2010. Optimum Design of Balanced SAW Filters Using Multi-Objective Differential Evolution in SIMULATED EVOLUTION AND LEARNING
  • 2004. Solving Rotated Multi-objective Optimization Problems Using Differential Evolution in AI 2004: ADVANCES IN ARTIFICIAL INTELLIGENCE
  • 2006. A Fast and Effective Method for Pruning of Non-dominated Solutions in Many-Objective Problems in PARALLEL PROBLEM SOLVING FROM NATURE - PPSN IX
  • 2009. Multiobjective Distinct Candidates Optimization (MODCO): A Cluster-Forming Differential Evolution Algorithm in EVOLUTIONARY MULTI-CRITERION OPTIMIZATION
  • 2010-02. Recent advances in differential evolution: a survey and experimental analysis in ARTIFICIAL INTELLIGENCE REVIEW
  • Book

    TITLE

    NEO 2015

    ISBN

    978-3-319-44002-6
    978-3-319-44003-3

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-44003-3_11

    DOI

    http://dx.doi.org/10.1007/978-3-319-44003-3_11

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1084843727


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