Antonio J. Nebro

Artificial Intelligence · 2008–2021

A researcher in the Science corpus: 6 papers; trusts 42 researchers and is trusted by 36 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 92 citations into it; #5,568 of 35,345 by trust here, #12,974 by citations
• Computational Theory and Mathematics — 80 citations into it; #1,053 of 12,182 by trust here, #2,298 by citations
• Industrial and Manufacturing Engineering — 11 citations into it
• Computer Networks and Communications — 8 citations into it
• Evaluation & Benchmarks — 8 citations into it
• Control and Systems Engineering — 6 citations into it

Whom Antonio J. Nebro trusts overall

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

1. Kalyanmoy Deb
2. Eckart Zitzler
3. Carlos Artemio Coello Coello
4. Lothar Thiele
5. Joshua D. Knowles
6. Marco Laumanns
7. Margarita Reyes Sierra
8. Simon Huband
9. Stefan Bleuler
10. John N. Hooker

Who trusts Antonio J. Nebro overall

1. Juan José Durillo
2. Ye Tian
3. Antonin Ponsich
4. Xing-yi Zhang
5. Heiner Zille
6. Yuan Yuan
7. Hisao Ishibuchi
8. Yaochu Jin
9. Yue-jiao Gong
10. Ran Cheng

Papers from Antonio J. Nebro's perspective

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

1. Multi-objective optimization using evolutionary algorithms
2. Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy (Evolutionary Computation)
3. Comparison of Multiobjective Evolutionary Algorithms: Empirical Results (Evolutionary Computation)
4. Genetic Algorithms in Search
5. Genetic Algorithms in Search Optimization and Machine Learning
6. A fast and elitist multiobjective genetic algorithm: NSGA-II
7. Scalable Test Problems for Evolutionary Multiobjective Optimization (Evolutionary Multiobjective Optimization)
8. Indicator-Based Selection in Multiobjective Search (Parallel Problem Solving from Nature)
9. SPEA2: Improving the strength pareto evolutionary algorithm
10. Evolutionary Algorithms for Solving Multi-Objective Problems

Their papers

• jMetal: A Java framework for multi-objective optimization (2011) — 1,165 citations
• SMPSO: A new PSO-based metaheuristic for multi-objective optimization (2009) — 653 citations
• MOCell: A cellular genetic algorithm for multiobjective optimization (2009) — 309 citations
• AbYSS: Adapting Scatter Search to Multiobjective Optimization (2008) — 302 citations
• The jMetal framework for multi-objective optimization: Design and architecture (2010) — 295 citations
• A Tutorial On the design, experimentation and application of metaheuristic algorithms to real-World optimization problems (2021) — 357 citations