Motivations from Biological Evolution via Natural Selection: Population-based Diversity and Randomness-based Adaptation
The following order regarding a set of EC-related Python libraries makes no sense (just roughly from a download statistic perspective). We are actively updating this interesting list as we believe new libraries will be generated in the future.
- cmaes: A Python library for CMA-ES (
).
- DEAP: Distributed EAs in Python (> 2000 Citations +
).
- Fortin, F.A., De Rainville, F.M., Gardner, M.A.G., Parizeau, M. and Gagné, C., 2012. DEAP: Evolutionary algorithms made easy. Journal of Machine Learning Research, 13(1), pp.2171-2175.
- pycma: Python implementation of CMA-ES (
).
- pymoo: Multi-objective Optimization in Python (
).
- TPOT: A Python AutoML tool that optimizes ML pipelines using GP (
).
- nevergrad: A Python toolbox for performing gradient-free optimization (
).
- PyBrain: The Python ML Library (
).
- Schaul, T., Bayer, J., Wierstra, D., Sun, Y., Felder, M., Sehnke, F., Rückstieß, T. and Schmidhuber, J., 2010. PyBrain. Journal of Machine Learning Research, 11, pp.743-746.
- PySwarms: A research toolkit for PSO in Python (
).
- "NOTICE: The author of this library is not actively maintaining this open-source repository anymore."
- pygmo2: A scientific Python library for massively parallel optimization (
).
- PySR: High-Performance Symbolic Regression in Python and Julia (
).
- Gradient-Free-Optimizers: Simple and reliable optimization with local, global, population-based and sequential techniques in numerical discrete search spaces (
).
- PINTS: Probabilistic Inference on Noisy Time Series (
). [CMA-ES + XNES + SNES + PSO]
- evosax: ES in JAX (
).
- PyPop7: A Pure-Python Library for POPulation-based Black-Box Optimization (BBO), especially their Large-Scale versions/variants (
).
- EvoJAX: Hardware-Accelerated Neuroevolution (
).
- Platypus: A framework for EC in Python with a focus on Multi-Objective EAs (
).
- Vega: AutoML tools chain (
).
- LEAP: A general purpose Library for EAs in Python (
).
- EvolutionaryForest: A Python library for automated feature engineering based on GP (
)
- EvoTorch : Advanced EC library built directly on top of PyTorch, created at NNAISENSE (
).
- EC-KitY: A Python library for doing EC compatible with scikit-learn (
).
- QDax: A Python tool to accelerate QD and neuro-evolution algorithms through hardware accelerators and massive parallelization (
).
- Chalumeau, F., Lim, B., Boige, R., Allard, M., Grillotti, L., Flageat, M., Macé, V., Richard, G., Flajolet, A., Pierrot, T. and Cully, A., 2024. QDax: A library for quality-diversity and population-based algorithms with hardware acceleration. Journal of Machine Learning Research, 25(108), pp.1-16.
- pyribs: A Python library for QD optimization (
).
- paradiseo: An EC framework to (automatically) build fast parallel stochastic optimization solvers.
- Aeronautics&Astronautics
- Design Nonsymmetric Satellite Constellations: [Georgia Institute of Technology]
- Astronomy&Astrophysics
- Black Hole Parameter Estimation: [Harvard&Smithsonian + Harvard University + Max-Planck-Institut für Radioastronomie + Columbia University + University College London]
- Calibration of Dark Energy Model for Observational Cosmology: [DES Collaboration: Jet Propulsion Laboratory, California Institute of Technology + Fermi National Accelerator Laboratory + University College London + National Center for Supercomputing Applications + University of Illinois at Urbana-Champaign + University of Wisconsin-Madison + University of Michigan + University of Chicago + Stanford University + SLAC National Accelerator Laboratory + Ludwig-Maximilians-Universität + Harvard & Smithsonian + University of Cambridge + Princeton University + Oak Ridge National Laboratory + etc.] (with a 10-dimensional parameter space)
- Telescope Schedulers: [Princeton University + University of Washington]
- Global Optimization of Transit modelling: [European Space Agency etc.]
- Biology [e.g., Biophysics, Computational Biology]
- Optimization of mathematical modeling of Caenorhabditis elegans (based on GA): [Sherman&Harel, PNAS, 2024] from Weizmann Institute of Science
- Linguistic Diversity Simulation: [Dediu et al., 2019, Nature Human Behaviour]
- Stochastic Model Optimization for Microtubule Motors: [ETH Zürich + University of Zurich]
- Control&Robotics
- Exoskeleton Assistance for Walking: [Stanford University]
- Multi-Robot Navigation in Narrow Hallways: [University of Texas at Austin + George Mason University + Everyday Robots + Army Research Laboratory + Sony AI]
- Renderer Model Calibration to Minimize Sim2Real Gap: [Carnegie Mellon University + New York University]
- Direct Loss Minimization of Inverse Optimal Control: [University Stuttgart + Max Planck Institute + University of Southern California]
- Real2Sim2Real for Planar Robot Casting: [UC Berkeley + Toyota Research Institute]
- Discovering Multiple Algorithm Configurations: [Carnegie Mellon University]
- Learning to School: [Harvard University + SPACEX + ETH Zürich + etc.] ( Swimming + Swimmer + Swimmers + Undulatory Swimmers + Swimming Schools | Collective Behavior )
- Global Optimization of Continuous-Thrust Trajectories: [German Aerospace Center (DLR) etc.]
- Optimization of Modular Robot Composition: [Technical University of Munich]
- Informative Path Planning: [University of Bonn + University of Oxford + Lamarr Institute for Machine Learning and Artificial Intelligence]
- Computer Vision
- Textual Inversion: [Meituan Inc.]
- Operations Research
- Flight Scheduling and Fleet Assignment Model Optimization: [Birolini et al., 2021, Transportation Research Part B: Methodological] (using real-world data for a major European airline Alitalia)
- Planning of Airport Airside Expansion Projects: [University of Illinois at Urbana-Champaign] (a real-life case study of an airport airside expansion project at San Diego International Airport)
- Engineering
- Physics-supervised DL–based Optimization: [Li et al., 2023, PNAS]
- Medicine
- PharmacoKinetic and PharmacoDynamic Modelling Optimization for Model-Informed Precision Dosing: [University of Oxford + University of Exeter + F. Hoffmann-La Roche AG]
- Physical Science: e.g., in cosmology
- Chemical Science
- AutoDock: [PNAS, 2024] (based on Lamarckian genetic algorithm)
- Crystal structure prediction: e.g. [Yang et al., 2023, Science], [Mannix et al., 2015, Science], [Zhang et al., 2013, Science] ([Insa, 2013, Science]).
- Constructing first-principles phase diagrams of amorphous LixSi
- Materials Science
- Granular materials: A joint team from California Institute of Technology and ETH Zurich.
- Grain boundaries in 2D materials
- Energy-efficient 4D printing: A joint team from Singapore Institute of Manufacturing Technology, City University of Hong Kong, and Pennsylvania State University.
- Control
- NeuroScience: A joint team from Harvard Medical School and Washington University, St. Louis (PNAS, 2023)
- Designs of Computer Architectures:
- Evolution of Circuits for ML: [Chen et al., 2020, Nature]
- Geneva
- Analog Circuit Design and Optimization: [Zhou et al., TCAD, 2022]
- Quantumn Computing
- Quantum optimal control
- Global optimization of model fitting: [Villeneuve et al., 2017, Science]
- State preparation on quantum computers: Physics Letters A
- Hyper-Parameter Optimization (HPO): e.g., for Reinforcement Learning, Environmental Research
- Search-Based Software Engineering (SBSE) and Adversarial ML: https://evademl.org/
- AI for Infectious Diseases
- Scientific Computing
- Environmental and Energy Science
- Designing Diversified Renewable Energy Systems: [Gonzalez et al., 2023, Nature Sustainability]
- Wind turbine locations
- Fuel cell and nuclear reactor design: A joint team from Massachusetts Institute of Technology, University of Michigan (Ann Arbor), and Shanghai Jiao Tong University
- Kinetics model optimization of fuel-rich methane/NG oxidation with ozone addition
- Data mining: [KDD-2023]
Although we have given many problem instances where EAs showed satisfactory (not necessarily optimal) performance, NOT all problems could be best solved by EAs: e.g., [1], just to name a few. We believe that the amount of problem instances tackled effectively by EA will still keep increasing in the future.
- [Wang et al., 2010, TOG]: A team from University of Toronto.
- [Wampler&Popović, 2009, TOG]: A team from University of Washington. ("To automatically synthesize more physically and energetically realistic motions")
- [Auslander et al., 1995, TOG]:
- Koonin, E.V., 2011. The logic of chance: The nature and origin of biological evolution. FT Press.
- Fogel, D.B., 1998. Unearthing a fossil from the history of evolutionary computation. Fundamenta Informaticae, 35(1-4), pp.1-16. [ Fogel, D.B.: IEEE Evolutionary Computation Pioneer Award 2008 ]
- Fogel, D.B., 1998. Evolutionary computation: The fossil record. IEEE Press. [ Fogel, D.B.: IEEE Evolutionary Computation Pioneer Award 2008 ]
- Bremermann, H.J., 1962. Optimization through evolution and recombination. Self-Organizing Systems, 93, p.106. [ UC Berkeley + Hans J. Bremermann: A pioneer in mathematical biology ]
- Copeland, B.J., 2023. Early AI in Britain: Turing et al.. IEEE Annals of the History of Computing, (01), pp.1-19.
- Copeland, B.J., Bowen, J., Sprevak, M. and Wilson, R., 2017. The Turing guide. Oxford University Press.
- Fogel, D.B., 2006. Evolutionary computation: Toward a new philosophy of machine intelligence. John Wiley & Sons. [ Fogel, D.B.: IEEE Evolutionary Computation Pioneer Award 2008 ]
- McCarthy, J., Minsky, M.L., Rochester, N. and Shannon, C.E., 2006. A proposal for the dartmouth summer research project on artificial intelligence, august 31, 1955. AI Magazine, 27(4), pp.12-12.
- Copeland, B.J. and Proudfoot, D., 1999. Alan Turing’s forgotten ideas in computer science. Scientific American, 280(4), pp.98-103.
- Genesereth, M.R. and Nilsson, N.J., 1987. Logical foundations of artificial intelligence. Morgan Kaufmann.
- Newell, A. and Simon, H.A., 1975. Computer science as empirical inquiry: Symbols and search. In ACM Turing Award Lectures (p. 1975).
- Wright, S., 1932. The roles of mutation, inbreeding, crossbreeding and selection in evolution. In Proceedings of Sixth International Congress of Genetics (Vol. 1, pp. 356-366).
- Berry, A. and Browne, J., 2022. Mendel and Darwin. Proceedings of the National Academy of Sciences, 119(30), p.e2122144119.
- Eiben, A.E. and Smith, J.E., 2015. Introduction to evolutionary computing. Springer-Verlag Berlin Heidelberg.
- De Jong, K.A., 2006. Evolutionary computation: A unified approach. MIT Press.
- Fogel, D.B., 2006. Evolutionary computation: Toward a new philosophy of machine intelligence. John Wiley & Sons.
- Fogel, D.B., 1998. Evolutionary computation: The fossil record. IEEE Press.
- Wright, S., 1931. Evolution in Mendelian populations. Genetics, 16(2), p.97.
- [1997] No Free Lunch Theorems for Optimization [IEEE-TEVC+IBM+SFI+Wolpert]
- [2013] Analyzing Evolutionary Algorithms - The Computer Science Perspective [Jansen]
- [2020] Evolutionary Computation - A Unified Approach [GECCO+DeJong]
- [2015] The Five Tribes of Machine Learning [Domingos]
- [2015] The Master Algorithm - How the Quest for the Ultimate Learning Machine Will Remake Our World [Domingos]
- [2019] Exploratory landscape analysis [GECCO]
- [2009] Distilling Free-Form Natural Laws from Experimental Data [Science+Cornell+Lipson]
- [2017] Human-in-the-Loop Optimization of Exoskeleton Assistance During Walking [Science+CMU]
- [2015] From Evolutionary Computation to the Evolution of Things [Nature]
- [2019] Evolving Embodied Intelligence from Materials to Machines [NatureMachineIntelligence+Eiben+Mouret]
- [2007] Self-Organization, Embodiment, and Biologically Inspired Robotics [Science+Pfeifer+Iida]
- [2006] Resilient Machines through Continuous Self-Modeling [Science+Cornell+Bongard+Lipson]
- [2021] From Individual Robots to Robot Societies [ScienceRobotics+Floreano+Lipson]
- [2019] Particle Robotics Based on Statistical Mechanics of Loosely Coupled Components [Nature+MIT+Columbia+Cornell+Harvard+Lipson]
- [2003] The Evolutionary Origin of Complex Features [Nature]
- [2020] Classification with a Disordered Dopant-Atom Network in Silicon [Nature]
- [2020] Evolution of Circuits for Machine Learning [Nature+MIT]