World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
42
Citations
7290
World Ranking
8401
National Ranking
3597

Overview

Lee Spector is affiliated with Hampshire College in the United States and has a significant research presence in the fields of Computer Science and Biochemistry, Genetics and Molecular Biology. Their work spans multiple subfields, primarily focusing on Artificial Intelligence and Molecular Biology, along with areas such as Computer Science Applications, Genetics, and Information Systems.

Their main research topics include Evolutionary Algorithms and Applications, Metaheuristic Optimization Algorithms Research, and Teaching and Learning Programming. Other areas of interest encompassed Reinforcement Learning in Robotics, Viral Infectious Diseases and Gene Expression in Insects, Machine Learning in Bioinformatics, and Evolution and Genetic Dynamics.

Lee Spector's recent publications reflect these interests, including:

  • On the importance of specialists for lexicase selection, 2020, Genetic Programming and Evolvable Machines
  • Evolutionary quantum architecture search for parametrized quantum circuits, 2022, Proceedings of the Genetic and Evolutionary Computation Conference Companion
  • Multi-Objective Evolutionary Architecture Search for Parameterized Quantum Circuits, 2023, Entropy
  • Lexicase selection at scale, 2022, Proceedings of the Genetic and Evolutionary Computation Conference Companion
  • Informed Down-Sampled Lexicase Selection: Identifying Productive Training Cases for Efficient Problem Solving, 2024, Evolutionary Computation

Lee Spector frequently collaborates with other researchers in their field. Notable coauthors include Thomas Helmuth, Ryan Boldi, Li Ding, Edward Pantridge, and Alexander Lalejini. These collaborations have contributed to their publications in various academic venues.

Regarding publication venues, Lee Spector has a strong record of contributions to:

  • arXiv (Cornell University)
  • Proceedings of the Genetic and Evolutionary Computation Conference Companion
  • Genetic Programming and Evolvable Machines
  • Proceedings of the Genetic and Evolutionary Computation Conference
  • Entropy

Best Publications

  • Wolf-pack (Canis lupus) hunting strategies emerge from simple rules in computational simulations

    C. Muro;R. Escobedo;L. Spector;R.P. Coppinger

  • Ontology-based Web agents

    Sean Luke;Lee Spector;David Rager;James Hendler

  • Genetic Programming and Autoconstructive Evolution with the Push Programming Language

    Lee Spector;Alan Robinson

  • Evolving teamwork and coordination with genetic programming

    Sean Luke;Lee Spector

  • Solving Uncompromising Problems With Lexicase Selection

    Thomas Helmuth;Lee Spector;James Matheson

  • Emergence of Collective Behavior in Evolving Populations of Flying Agents

    Lee Spector;Jon Klein;Chris Perry;Mark Feinstein

  • Automatic Quantum Computer Programming: A Genetic Programming Approach

    Lee C. Spector

  • General Program Synthesis Benchmark Suite

    Thomas Helmuth;Lee Spector

  • The Push3 execution stack and the evolution of control

    Lee Spector;Jon Klein;Maarten Keijzer

  • A Comparison of Crossover and Mutation in Genetic Programming

    Sean Luke;Lee Spector

  • Assessment of problem modality by differential performance of lexicase selection in genetic programming: a preliminary report

    Lee Spector

  • Epsilon-Lexicase Selection for Regression

    William La Cava;Lee Spector;Kourosh Danai

  • Quantum computing applications of genetic programming

    Lee Spector;Howard Barnum;Herbert J. Bernstein;Nikhil Swamy

  • Open-ended evolution: Perspectives from the oee workshop in york

    Tim Taylor;Mark Bedau;Alastair Channon;David Ackley

  • Finding a better-than-classical quantum AND/OR algorithm using genetic programming

    L. Spector;H. Barnum;H.J. Bernstein;N. Swamy

  • Ontology-Based Knowledge Discovery on the World-Wide Web

    Sean Luke;Lee Spector;David Rager

  • Defining and simulating open-ended novelty: requirements, guidelines, and challenges.

    Wolfgang Banzhaf;Bert Baumgaertner;Guillaume Beslon;René Doursat

  • Autoconstructive Evolution: Push, PushGP, and Pushpop

    Lee Spector

  • Evolution of artificial intelligence

    Lee Spector

  • Simultaneous evolution of programs and their control structures

    Lee Spector

  • Criticism, culture, and the automatic generation of artworks

    Lee Spector;Adam Alpern

  • Genetic and Evolutionary Computation – GECCO 2004

    Unknown

  • Multi-type, Self-adaptive Genetic Programming as an Agent Creation Tool

    Lee Spector

Frequent Co-Authors

James A. Hendler
James A. Hendler Rensselaer Polytechnic Institute
Jason H. Moore
Jason H. Moore University of Pennsylvania
Sean Luke
Sean Luke George Mason University
Sara Silva
Sara Silva University of Lisbon
Leonardo Vanneschi
Leonardo Vanneschi Universidade Nova de Lisboa
Andrew G. Barto
Andrew G. Barto University of Massachusetts Amherst
Wolfgang Banzhaf
Wolfgang Banzhaf Michigan State University
Paul Fleming
Paul Fleming National Renewable Energy Laboratory
Jordan Grafman
Jordan Grafman Northwestern University

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