Genetic Programming and Evolvable Machines View Homepage


Ontology type: schema:Periodical     


Journal Info

START YEAR

2000

PUBLISHER

Springer US

LANGUAGE

en

HOMEPAGE

http://link.springer.com/journal/10710

Recent publications latest 20 shown

  • 2019-03-23 Local search in speciation-based bloat control for genetic programming
  • 2019-03-14 Stochastic synthesis of recursive functions made easy with bananas, lenses, envelopes and barbed wire
  • 2019-03-07 Hitoshi Iba: Evolutionary approach to machine learning and deep neural networks: neuro-evolution and gene regulatory networks
  • 2019-03-05 A genetic programming framework in the automatic design of combination models for salient object detection
  • 2019-03 Automated discovery of test statistics using genetic programming
  • 2019-03 Evolving continuous cellular automata for aesthetic objectives
  • 2019-03-01 A multi-level grammar approach to grammar-guided genetic programming: the case of scheduling in heterogeneous networks
  • 2019-03 Acknowledgment to reviewers
  • 2019-03 A genetic programming approach for delta hedging
  • 2019-03 DENSER: deep evolutionary network structured representation
  • 2019-03 Software review: DEAP (Distributed Evolutionary Algorithm in Python) library
  • 2019-03 Designing automatically a representation for grammatical evolution
  • 2019-03 Evelyne Lutton, Nathalie Perrot, Alberto Tonda: Evolutionary algorithms for food science and technology
  • 2019-03 Georgios N. Yannakakis and Julian Togelius: Artificial Intelligence and Games
  • 2019-03 Editorial introduction
  • 2019-02-27 Genetic programming and evolvable machines at 20
  • 2019-02-05 EvoParsons: design, implementation and preliminary evaluation of evolutionary Parsons puzzle
  • 2019-01-16 Vector quantization using the improved differential evolution algorithm for image compression
  • 2018-12 Ryan J. Urbanowicz, and Will N. Browne: Introduction to learning classifier systems
  • 2018-12 Alain P√©trowski and Sana Ben-Hamida: Evolutionary Algorithms
  • JSON-LD is the canonical representation for SciGraph data.

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