Biomimetic algorithms

December 22, 2015 — October 3, 2022

Figure 1

Nature inspired algorithms for computers for problems without obvious “normal” solutions. (If you want to use computer-inspired algorithms for nature, that is the dual to this, bio-computing.)

The problem to be solved is usually a search/optimisation one. Normally evolutionary algorithms are in here too. (ant colonies, particle swarms, that one based on choirs… harmony search?), Typically these are attractive because they are simple to explain, although often less simple to analyse.

Is this is a real field separate from all the things that looks similar to it? Often they are asymptotically the same as a conventional stochastic method. e.g. particle swarms and particle systems, or evolution and stochastic gradient descent. Somewhere in this mix is artificial chemistry, where you use a simplified model of a natural process as a simplified model for computing about other natural processes, or showing that natural processes might be computing, or something like that.

…and quorum sensing? How about that? Multi-agent systems?

Points of contact between classical neural nets and artificial neural nets are always entertaining. Beniaguev, Segev, and London (2021) is one. See Fruit Fly Brain Hacked For Language Processing for a articial-neural-networks-meeting-their-ancestors moment.

Figure 2

1 Genetic programming

See genetic programming.

2 Neuron-like neural networks

See Neural neural networks.

3 References

Beniaguev, Segev, and London. 2021. Single Cortical Neurons as Deep Artificial Neural Networks.” Neuron.
Dabagia, Papadimitriou, and Vempala. 2023. Computation with Sequences in the Brain.”
Dabagia, Vempala, and Papadimitriou. 2022. Assemblies of Neurons Learn to Classify Well-Separated Distributions.” In Proceedings of Thirty Fifth Conference on Learning Theory.
Eberhart, and Kennedy. 1995. A New Optimizer Using Particle Swarm Theory.” In Proceedings of the Sixth International Symposium on Micro Machine and Human Science.
Floreano, and Mattiussi. 2008. Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies (Intelligent Robotics and Autonomous Agents).
Hasson, Nastase, and Goldstein. 2020. Direct Fit to Nature: An Evolutionary Perspective on Biological and Artificial Neural Networks.” Neuron.
Hinton. n.d. The Forward-Forward Algorithm: Some Preliminary Investigations.”
Kennedy, and Eberhart. 1995. Particle Swarm Optimization.” In.
Mitropolsky, Collins, and Papadimitriou. 2021. A Biologically Plausible Parser.”
Ororbia, and Mali. 2023. The Predictive Forward-Forward Algorithm.”
Papadimitriou, and Vempala. 2018. Random Projection in the Brain and Computation with Assemblies of Neurons.” In 10th Innovations in Theoretical Computer Science Conference (ITCS 2019). Leibniz International Proceedings in Informatics (LIPIcs).
Papadimitriou, Vempala, Mitropolsky, et al. 2020. Brain computation by assemblies of neurons.” Proceedings of the National Academy of Sciences of the United States of America.
Porr, and Miller. 2020. Forward Propagation Closed Loop Learning.” Adaptive Behavior.
Ren, Kornblith, Liao, et al. 2022. Scaling Forward Gradient With Local Losses.”
Straszak, and Vishnoi. 2016. IRLS and Slime Mold: Equivalence and Convergence.” arXiv:1601.02712 [Cs, Math, Stat].
Vanchurin, Wolf, Katsnelson, et al. 2021. Towards a Theory of Evolution as Multilevel Learning.”
Venter. 2002. “Particle Swarm Optimization.” In.