Corpus ID: 60771202

The Nature of Niching: Genetic Algorithms and the Evolution of Optimal

  title={The Nature of Niching: Genetic Algorithms and the Evolution of Optimal},
  author={J. Horn},
  • J. Horn
  • Published 1997
  • Biology, Computer Science
  • Genetic algorithms (GAs) with "fitness sharing" have been analyzed and successfully applied to problems in search and optimization, while GAs using various types of "resource sharing" have been incorporated into classifiers, immune system models, artificial ecologies, artificial economies, etc. Both types of sharing are based on the same observation of nature: dividing a finite resource among competing organisms limits the size of populations dependent on that resource. If multiple resources… CONTINUE READING
    118 Citations
    A Novel Type of Niching Methods Based on Steady-State Genetic Algorithm
    • 6
    • Highly Influenced
    Controlling the cooperative-competitive boundary in niched genetic algorithms
    • 1
    • PDF
    An investigation on niching multiple species based on population replacement strategies for multimodal functions optimization
    • 8
    • Highly Influenced
    Dynamics of fitness sharing evolutionary algorithms for coevolution of multiple species
    • 5
    • Highly Influenced
    Resource-Based Fitness Sharing
    • 10
    Spatially-structured niching methods for evolutionary algorithms
    • 1
    Neuroevolution: Randomness is the Simplest Thing?
    A population ecology inspired parent selection strategy for numerical constrained optimization problems
    • 1
    • PDF
    Scalability of niche PSO
    • 49
    • PDF


    Searching for Diverse, Cooperative Populations with Genetic Algorithms
    • 307
    • PDF
    Population Size and Genetic Drift in Fitness Sharing
    • 67
    Niching methods for genetic algorithms
    • 999
    • PDF
    Are Genetic Algorithms Function Optimizers?
    • 217
    • PDF
    A Sequential Niche Technique for Multimodal Function Optimization
    • 522
    • PDF
    A Markov Chain Analysis of Genetic Algorithms with a State Dependent Fitness Function
    • H. Dawid
    • Computer Science, Mathematics
    • Complex Syst.
    • 1994
    • 35
    • PDF
    Genetic Algorithms Are NOT Function Optimizers
    • 179
    • PDF
    Implicit Niching in a Learning Classifier System: Nature's Way
    • 155
    An Overview of Evolutionary Algorithms in Multiobjective Optimization
    • 2,333
    • PDF