H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 31 Citations 5,860 150 World Ranking 7709 National Ranking 3626

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Operating system
  • Algorithm

His primary areas of investigation include Artificial intelligence, Cellular automaton, Theoretical computer science, Computation and Task. In his research on the topic of Artificial intelligence, Fuzzy logic is strongly related with Machine learning. In his study, Self-replication and Computational model is inextricably linked to Von Neumann architecture, which falls within the broad field of Cellular automaton.

When carried out as part of a general Theoretical computer science research project, his work on Stochastic cellular automaton is frequently linked to work in Cellular model, therefore connecting diverse disciplines of study. His study in Task is interdisciplinary in nature, drawing from both Class, Field, Simplicity and Concurrent computing. His Evolutionary algorithm study integrates concerns from other disciplines, such as Genetic algorithm, Genetic programming and Endgame tablebase, Computer chess.

His most cited work include:

  • A fuzzy-genetic approach to breast cancer diagnosis. (311 citations)
  • Evolution of Parallel Cellular Machines: The Cellular Programming Approach (255 citations)
  • Evolution of Parallel Cellular Machines (220 citations)

What are the main themes of his work throughout his whole career to date?

Moshe Sipper spends much of his time researching Artificial intelligence, Genetic programming, Theoretical computer science, Cellular automaton and Evolutionary computation. His work investigates the relationship between Artificial intelligence and topics such as Machine learning that intersect with problems in Fuzzy logic. His Genetic programming research integrates issues from Heuristics and Crossover.

Moshe Sipper incorporates Theoretical computer science and Process in his research. Moshe Sipper interconnects Distributed computing, Random number generation, Artificial life, Mobile automaton and Computation in the investigation of issues within Cellular automaton. His Evolutionary computation study incorporates themes from Domain and Algorithm.

He most often published in these fields:

  • Artificial intelligence (44.24%)
  • Genetic programming (25.81%)
  • Theoretical computer science (20.28%)

What were the highlights of his more recent work (between 2014-2021)?

  • Artificial intelligence (44.24%)
  • Genetic programming (25.81%)
  • Evolutionary computation (19.35%)

In recent papers he was focusing on the following fields of study:

Artificial intelligence, Genetic programming, Evolutionary computation, Machine learning and Evolutionary algorithm are his primary areas of study. The Artificial intelligence study combines topics in areas such as Text mining and Turing machine. His research in Genetic programming intersects with topics in Mathematics education, Control theory and Trajectory.

He has researched Evolutionary computation in several fields, including Generative grammar and Computational intelligence. His work on Random forest, Boosting and Gradient boosting as part of general Machine learning research is often related to Coding, thus linking different fields of science. His research investigates the link between Evolutionary algorithm and topics such as Theoretical computer science that cross with problems in Encoding.

Between 2014 and 2021, his most popular works were:

  • Investigating the Parameter Space of Evolutionary Algorithms (34 citations)
  • Investigating the Parameter Space of Evolutionary Algorithms (34 citations)
  • EBIC: an evolutionary-based parallel biclustering algorithm for pattern discovery. (15 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Operating system
  • Algorithm

His main research concerns Evolutionary computation, Artificial intelligence, Genetic programming, Machine learning and Evolutionary algorithm. His work deals with themes such as Lead, Theoretical computer science and Data science, which intersect with Evolutionary computation. His Artificial intelligence study frequently draws parallels with other fields, such as Complex data type.

His Genetic programming study combines topics in areas such as Open source and Mathematical optimization. His Machine learning research focuses on Unsupervised learning in particular. His work on Evolutionary programming as part of general Evolutionary algorithm study is frequently linked to Parameter space, therefore connecting diverse disciplines of science.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Evolution of Parallel Cellular Machines

Moshe Sipper.
(1997)

549 Citations

A fuzzy-genetic approach to breast cancer diagnosis.

Carlos Andrés Peña-Reyes;Moshe Sipper.
Artificial Intelligence in Medicine (1999)

447 Citations

Evolution of Parallel Cellular Machines: The Cellular Programming Approach

Moshe Sipper.
(1997)

390 Citations

A phylogenetic, ontogenetic, and epigenetic view of bio-inspired hardware systems

M. Sipper;E. Sanchez;D. Mange;M. Tomassini.
IEEE Transactions on Evolutionary Computation (1997)

331 Citations

Toward robust integrated circuits: The embryonics approach

D. Mange;M. Sipper;A. Stauffer;G. Tempesti.
Proceedings of the IEEE (2000)

283 Citations

Fifty years of research on self-replication: an overview

Moshe Sipper.
Artificial Life (1998)

263 Citations

On the generation of high-quality random numbers by two-dimensional cellular automata

M. Tomassini;M. Sipper;M. Perrenoud.
IEEE Transactions on Computers (2000)

223 Citations

Design, observation, surprise! A test of emergence

Edmund M.A. Ronald;Moshe Sipper;Mathieu S. Capcarrère.
Artificial Life (1999)

218 Citations

The emergence of cellular computing

M. Sipper.
IEEE Computer (1999)

207 Citations

Evolutionary computation in medicine: an overview.

Carlos Andrés Peña-Reyes;Moshe Sipper.
Artificial Intelligence in Medicine (2000)

199 Citations

If you think any of the details on this page are incorrect, let us know.

Contact us

Best Scientists Citing Moshe Sipper

Larry Bull

Larry Bull

University of the West of England

Publications: 37

Andy M. Tyrrell

Andy M. Tyrrell

University of York

Publications: 34

William B. Langdon

William B. Langdon

University College London

Publications: 27

Marco Tomassini

Marco Tomassini

University of Lausanne

Publications: 25

Julian F. Miller

Julian F. Miller

University of York

Publications: 25

Mark Harman

Mark Harman

University College London

Publications: 22

Westley Weimer

Westley Weimer

University of Michigan–Ann Arbor

Publications: 19

Mengjie Zhang

Mengjie Zhang

Victoria University of Wellington

Publications: 17

Stephanie Forrest

Stephanie Forrest

University of New Mexico

Publications: 16

Gregory S. Chirikjian

Gregory S. Chirikjian

National University of Singapore

Publications: 16

Andrew Adamatzky

Andrew Adamatzky

University of the West of England

Publications: 15

Jason H. Moore

Jason H. Moore

University of Pennsylvania

Publications: 14

Pascal Bouvry

Pascal Bouvry

University of Luxembourg

Publications: 13

Ricard V. Solé

Ricard V. Solé

Pompeu Fabra University

Publications: 12

Leonardo Vanneschi

Leonardo Vanneschi

Universidade Nova de Lisboa

Publications: 12

Francisco Herrera

Francisco Herrera

University of Granada

Publications: 12

Something went wrong. Please try again later.