Genetic-Algorithms-(GA).md

Genetic Algorithms (GAs)

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"GAs can be a powerful tool for solving difficult problems and for simulating natural systems in a wide variety of scientific fields."

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Parallel GA

Some Representative Applications of GA

• Evolving images for visual neurons: A joint team from Harvard Medical School, Harvard University, Boston Children’s Hospital, and Washington University, St. Louis.
• Therapeutics: A joint team from Harvard University, Georgia Institute of Technology, Massachusetts Institute of Technology, Carnegie Mellon University, Stanford University, IQVIA, and University of Illinois at Urbana-Champaign (NeurIPS, 2021).
  • "Graph-GA dominates the leaderboard in terms of optimization ability, while a simple SMILES LSTM ranked behind. The SOAT ML models that reported excellent performances in unlimited trivial oracles cannot beat virtual screening when allowing less than 5,000 oracle calls. This result questions the utility of the current ML SOTA methods and calls for a shift of focus on the current ML molecular generation communities to consider realistic constraints during evaluation."
• Magnetic soft continuum robots: A joint team from Massachusetts Institute of Technology, Southern University of Science and Technology, Massachusetts General Hospital, and Harvard Medical School.
• Rare earth–free magnetic materials: A joint team from Iowa State University, University of Texas at Austin, University of Tokyo, University of Nebraska, Guangdong University of Technology, Yantai University, and Zhejiang Agriculture and Forestry University.
• Travelling salesman problem (TSP): Cavendish Laboratory (Nature, 1985)

Theoretical Advances of GA

Schema Theorem and Building Block Hypothesis (BBH)

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"From the perspective of function optimization, the schema theorem does not provide any guarantee of convergence to, or divergence from, any optimum solution. This is a significant weakness of the theorem." (from Beyer, 1997, BioSystems)

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implicit parallelism

Markov Chains Models of GA

Reference

• Mitchell, M., 1998. An introduction to genetic algorithms. MIT Press.
• Beyer, H.G., 1997. An alternative explanation for the manner in which genetic algorithms operate. BioSystems, 41(1), pp.1-15.
• Whitley, D., 1994. A genetic algorithm tutorial. Statistics and Computing, 4(2), pp.65-85.
• Rudolph, G., 1994. Convergence analysis of canonical genetic algorithms. IEEE Transactions on Neural Networks, 5(1), pp.96-101.
• Mühlenbein, H. and Schlierkamp-Voosen, D., 1993. The science of breeding and its application to the breeder genetic algorithm (BGA). Evolutionary Computation, 1(4), pp.335-360.
• Davis, T.E. and Principe, J.C., 1993. A Markov chain framework for the simple genetic algorithm. Evolutionary Computation, 1(3), pp.269-288.
• Bertoni, A. and Dorigo, M., 1993. Implicit parallelism in genetic algorithms. Artificial Intelligence, 61(2), pp.307-314.