Tea Robic

Artificial Intelligence · 2005

A researcher in the Science corpus: 1 paper; trusts 13 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Tea Robic trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

  1. Hussein A. Abbass
  2. Nateri K. Madavan
  3. Kalyanmoy Deb
  4. Eckart Zitzler
  5. Feng Xue
  6. Ruhul Amin Sarker
  7. Arthur C. Sanderson
  8. Samir Agrawal
  9. Lothar Thiele
  10. Charles S. Newton

Who trusts Tea Robic overall

  1. Xin-She Yang
  2. Michael T. M. Emmerich
  3. Saku Kukkonen
  4. Wenyin Gong
  5. Xing-yi Zhang
  6. André H. Deutz
  7. Suash Deb
  8. Jouni Lampinen
  9. Swagatam Das
  10. Zhihua Cai

Papers from Tea Robic's perspective

Ordered by how strongly the researchers Tea Robic trusts endorse each paper. Same corpus, a different order for every researcher.

  1. A fast and elitist multiobjective genetic algorithm: NSGA-II
  2. Comparison of Multiobjective Evolutionary Algorithms: Empirical Results (Evolutionary Computation)
  3. The self-adaptive Pareto differential evolution algorithm (Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600))
  4. Multiobjective optimization using a Pareto differential evolution approach (Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600))
  5. PDE: a Pareto-frontier differential evolution approach for multi-objective optimization problems (Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546))
  6. Pareto-based multi-objective differential evolution (The 2003 Congress on Evolutionary Computation, 2003. CEC '03.)
  7. Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy (Evolutionary Computation)
  8. Genetic Algorithms in Search Optimization and Machine Learning
  9. Simulated Binary Crossover for Continuous Search Space
  10. Evolutionary algorithms in theory and practice - evolution strategies, evolutionary programming, genetic algorithms

Their papers