Editing Neuroevolution (section)

==Examples==

Examples of neuroevolution methods (those with direct encodings are necessarily non-embryogenic):
{| class="wikitable" border="1"
|-
! Method
! Encoding
! Evolutionary algorithm
! Aspects evolved
|-
| Neuro-genetic evolution by E. Ronald, 1994<ref>{{citation |contribution=Genetic Lander: An experiment in accurate neuro-genetic control |first1=Edmund |last1=Ronald |first2= March |last2=Schoenauer |title=PPSN III 1994 Parallel Programming Solving from Nature |pages=452–461 |citeseerx=10.1.1.56.3139 |year=1994 }}</ref>
| Direct
| [[Genetic algorithm]]
| Network Weights
|-
| Cellular Encoding (CE) by F. Gruau, 1994<ref name=gruau94/>
| Indirect, embryogenic (grammar tree using [[S-expression]]s)
| [[Genetic programming]]
| Structure and parameters (simultaneous, complexification)
|-
| GNARL by Angeline et al., 1994<ref>{{cite journal |last1=Angeline |first1=P.J. |last2=Saunders |first2=G.M. |last3=Pollack |first3=J.B. |title=An evolutionary algorithm that constructs recurrent neural networks |journal=IEEE Transactions on Neural Networks |date=January 1994 |volume=5 |issue=1 |pages=54–65 |doi=10.1109/72.265960 |pmid=18267779 |citeseerx=10.1.1.64.1853 |s2cid=44767 }}</ref>
| Direct
| [[Evolutionary programming]]
| Structure and parameters (simultaneous, complexification)
|-
| EPNet by Yao and Liu, 1997<ref>{{cite journal |last1=Yao |first1=X. |last2=Liu |first2=Y. |title=A new evolutionary system for evolving artificial neural networks |journal=IEEE Transactions on Neural Networks |date=May 1997 |volume=8 |issue=3 |pages=694–713 |doi=10.1109/72.572107 |pmid=18255671 }}</ref>
| Direct
| [[Evolutionary programming]] (combined with [[backpropagation]] and [[simulated annealing]])
| Structure and parameters (mixed, complexification and simplification)
|-
| [[NeuroEvolution of Augmenting Topologies]] (NEAT) by Stanley and Miikkulainen, 2002<ref name=autogenerated1>{{cite web|title=Real-Time Neuroevolution in the NERO Video Game|first1=Kenneth O. |last1=Stanley |first2=Bobby D. |last2=Bryant |first3=Risto |last3=Miikkulainen|url=https://nn.cs.utexas.edu/downloads/papers/stanley.ieeetec05.pdf |date=December 2005 }}</ref><ref>{{cite journal |last1=Stanley |first1=Kenneth O. |last2=Miikkulainen |first2=Risto |title=Evolving Neural Networks through Augmenting Topologies |journal=Evolutionary Computation |date=June 2002 |volume=10 |issue=2 |pages=99–127 |doi=10.1162/106365602320169811 |pmid=12180173 |citeseerx=10.1.1.638.3910 |s2cid=498161 }}</ref>
| Direct
| [[Genetic algorithm]]. Tracks genes with historical markings to allow crossover between different topologies, protects innovation via speciation.
| Structure and parameters
|-
| [[HyperNEAT|Hypercube-based NeuroEvolution of Augmenting Topologies]] (HyperNEAT) by Stanley, D'Ambrosio, Gauci, 2008<ref name=hyperneat />
| Indirect, non-embryogenic (spatial patterns generated by a [[Compositional pattern-producing network]] (CPPN) within a [[hypercube]] are interpreted as connectivity patterns in a lower-dimensional space)
| [[Genetic algorithm]]. The NEAT algorithm (above) is used to evolve the CPPN.
| Parameters, structure fixed (functionally fully connected)
|-
| [[ES-HyperNEAT|Evolvable Substrate Hypercube-based NeuroEvolution of Augmenting Topologies]] (ES-HyperNEAT) by Risi, Stanley 2012<ref name=eshyperalife/>
| Indirect, non-embryogenic (spatial patterns generated by a [[Compositional pattern-producing network]] (CPPN) within a [[hypercube]] are interpreted as connectivity patterns in a lower-dimensional space)
| [[Genetic algorithm]]. The NEAT algorithm (above) is used to evolve the CPPN.
| Parameters and network structure
|-
| [[Evolutionary Acquisition of Neural Topologies]] (EANT/EANT2) by Kassahun and Sommer, 2005<ref>{{citation|first1=Yohannes |last1=Kassahun |first2=Gerald |last2=Sommer|contribution=Efficient reinforcement learning through evolutionary acquisition of neural topologies|title=13th European Symposium on Artificial Neural Networks |pages= 259–266|location= Bruges, Belgium |date=April 2005 |contribution-url=https://ks.informatik.uni-kiel.de/~yk/ESANN2005EANT.pdf}}</ref> / Siebel and Sommer, 2007<ref>{{cite journal |last1=Siebel |first1=Nils T. |last2=Sommer |first2=Gerald |title=Evolutionary reinforcement learning of artificial neural networks |journal=International Journal of Hybrid Intelligent Systems |date=17 October 2007 |volume=4 |issue=3 |pages=171–183 |doi=10.3233/his-2007-4304 }}</ref>
| Direct and indirect, potentially embryogenic (Common Genetic Encoding<ref name=cgegecco />)
| [[Evolutionary programming]]/[[Evolution strategies]]
| Structure and parameters (separately, complexification)
|-
| [[Interactively Constrained Neuro-Evolution]] (ICONE) by Rempis, 2012<ref>{{cite thesis |last1=Rempis |first1=Christian Wilhelm |title=Evolving Complex Neuro-Controllers with Interactively Constrained Neuro-Evolution |date=2012 |url=https://osnadocs.ub.uni-osnabrueck.de/handle/urn:nbn:de:gbv:700-2012101710370 }}</ref>
| Direct, includes constraint masks to restrict the search to specific topology / parameter manifolds.
| [[Evolutionary algorithm]]. Uses constraint masks to drastically reduce the search space through exploiting [[domain knowledge]].
| Structure and parameters (separately, complexification, interactive)
|-
| [[Deus Ex Neural Network]] (DXNN) by Gene Sher, 2012<ref>{{cite book |doi=10.1007/978-1-4614-4463-3 |title=Handbook of Neuroevolution Through Erlang |year=2013 |last1=Sher |first1=Gene I. |isbn=978-1-4614-4462-6 |s2cid=21777855 }}</ref>
| Direct/Indirect, includes constraints, local tuning, and allows for evolution to integrate new sensors and actuators.
| [[Memetic algorithm]]. Evolves network structure and parameters on different time-scales.  
| Structure and parameters (separately, complexification, interactive)
|-
| [[Spectrum-diverse Unified Neuroevolution Architecture]] (SUNA) by Danilo Vasconcellos Vargas, Junichi Murata<ref>{{cite journal|first1=Danilo Vasconcellos |last1=Vargas |first2=Junichi |last2=Murata |journal=IEEE Transactions on Neural Networks and Learning Systems |volume=28 |issue=8 |pages=1759–1773 |title=Spectrum-Diverse Neuroevolution With Unified Neural Models|doi=10.1109/TNNLS.2016.2551748 |pmid=28113564 |year=2019 |arxiv=1902.06703 |bibcode=2019arXiv190206703V |s2cid=206757620 }}</ref> ([https://github.com/zweifel/Physis-Shard Download code])
| Direct, introduces the [[Unified Neural Representation]] (representation integrating most of the neural network features from the literature).
| Genetic Algorithm with a diversity preserving mechanism called [[Spectrum-diversity]] that scales well with chromosome size, is problem independent and focus more on obtaining diversity of high level behaviours/approaches. To achieve this diversity the concept of [[chromosome Spectrum]] is introduced and used together with a [[Novelty Map Population]].   
| Structure and parameters (mixed, complexification and simplification)
|-
| [[Modular Agent-Based Evolver]] (MABE) by Clifford Bohm, Arend Hintze, and others.<ref>{{cite journal|first1=Jeffrey |last1=Edlund |first2=Nicolas |last2=Chaumont |first3=Arend |last3=Hintze |first4=Christof |last4=Koch |first5=Giulio 
|last5=Tononi |first6=Christoph |last6=Adami |journal=PLOS Computational Biology |volume=7 |issue=10 |pages=e1002236 |title=Integrated Information Increases with Fitness in the Evolution of Animats|doi=10.1371/journal.pcbi.1002236 |pmid=22028639 |pmc=3197648 |year=2011 |arxiv=1103.1791 |bibcode=2011PLSCB...7E2236E |doi-access=free }}</ref> ([https://github.com/Hintzelab/MABE Download code])
| Direct or indirect encoding of [[Markov network]]s, Neural Networks, genetic programming, and other arbitrarily customizable controllers.
| Provides evolutionary algorithms, genetic programming algorithms, and allows customized algorithms, along with specification of arbitrary constraints.   
| Evolvable aspects include the neural model and allows for the evolution of morphology and sexual selection among others.
|-
|Covariance Matrix Adaptation with Hypervolume Sorted Adaptive Grid Algorithm (CMA-HAGA) by Shahin Rostami, and others.<ref>{{cite journal |last1=Rostami |first1=Shahin |last2=Neri |first2=Ferrante |title=A fast hypervolume driven selection mechanism for many-objective optimisation problems |journal=Swarm and Evolutionary Computation |date=June 2017 |volume=34 |pages=50–67 |doi=10.1016/j.swevo.2016.12.002 |hdl=2086/13102 |hdl-access=free }}</ref><ref>{{cite book |doi=10.1109/CIBCB.2017.8058553 |chapter=Multi-objective evolution of artificial neural networks in multi-class medical diagnosis problems with class imbalance |title=2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) |year=2017 |last1=Shenfield |first1=Alex |last2=Rostami |first2=Shahin |pages=1–8 |isbn=978-1-4673-8988-4 |s2cid=22674515 |chapter-url=https://eprints.bournemouth.ac.uk/29999/1/CIBCB2017_fetal.pdf }}</ref>
|Direct, includes an [[atavism]] feature which enables traits to disappear and re-appear at different generations.
|Multi-Objective [[Evolution strategy|Evolution Strategy]] with [[Preference Articulation]] ([[Computational Steering]])
|Structure, weights, and biases.
|-
|GACNN evolutionary pressure-driven by Di Biasi et al,  <ref>{{Cite book |last1=Di Biasi |first1=Luigi |last2=De Marco |first2=Fabiola |last3=Auriemma Citarella |first3=Alessia |last4=Barra |first4=Paola |last5=Piotto Piotto |first5=Stefano |last6=Tortora |first6=Genoveffa |chapter=Hybrid Approach for the Design of CNNS Using Genetic Algorithms for Melanoma Classification |series=Lecture Notes in Computer Science |date=2023 |volume=13643 |editor-last=Rousseau |editor-first=Jean-Jacques |editor2-last=Kapralos |editor2-first=Bill |title=Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges |chapter-url=https://link.springer.com/chapter/10.1007/978-3-031-37660-3_36 |language=en |location=Cham |publisher=Springer Nature Switzerland |pages=514–528 |doi=10.1007/978-3-031-37660-3_36 |isbn=978-3-031-37660-3}}</ref>
|Direct
|[[Genetic algorithm]], Single-Objective Evolution Strategy, specialized for Convolutional Neural Network 
|Structure
|-
|Fast-DENSER by Assunção et al<ref>{{Cite journal |last1=Assunção |first1=Filipe |last2=Lourenço |first2=Nuno |last3=Ribeiro |first3=Bernardete |last4=Machado |first4=Penousal |date=June 2021 |title=Fast-DENSER: Fast Deep Evolutionary Network Structured Representation |url=https://linkinghub.elsevier.com/retrieve/pii/S235271102100039X |journal=SoftwareX |language=en |volume=14 |pages=100694 |doi=10.1016/j.softx.2021.100694|bibcode=2021SoftX..1400694A |hdl=10316/100856 |hdl-access=free }}</ref> and others<ref>{{Citation |last1=Vinhas |first1=Adriano |title=Towards evolution of Deep Neural Networks through contrastive Self-Supervised learning |date=2024-06-20 |arxiv=2406.14525 |last2=Correia |first2=João |last3=Machado |first3=Penousal}}</ref><ref>{{Citation |last1=Cortês |first1=Gabriel |title=Towards Physical Plausibility in Neuroevolution Systems |date=2024 |work=Applications of Evolutionary Computation |volume=14635 |pages=76–90 |editor-last=Smith |editor-first=Stephen |url=https://link.springer.com/10.1007/978-3-031-56855-8_5 |access-date=2024-06-21 |place=Cham |publisher=Springer Nature Switzerland |language=en |doi=10.1007/978-3-031-56855-8_5 |isbn=978-3-031-56854-1 |last2=Lourenço |first2=Nuno |last3=Machado |first3=Penousal |editor2-last=Correia |editor2-first=João |editor3-last=Cintrano |editor3-first=Christian|url-access=subscription }}</ref>
|Indirect
|[[Grammatical evolution]] (Dynamic Structured Grammar Evolution)<ref>{{Citation |last1=Lourenço |first1=Nuno |title=Structured Grammatical Evolution: A Dynamic Approach |date=2018 |work=Handbook of Grammatical Evolution |pages=137–161 |editor-last=Ryan |editor-first=Conor |url=https://doi.org/10.1007/978-3-319-78717-6_6 |access-date=2024-06-21 |place=Cham |publisher=Springer International Publishing |language=en |doi=10.1007/978-3-319-78717-6_6 |isbn=978-3-319-78717-6 |last2=Assunção |first2=Filipe |last3=Pereira |first3=Francisco B. |last4=Costa |first4=Ernesto |last5=Machado |first5=Penousal |editor2-last=O'Neill |editor2-first=Michael |editor3-last=Collins |editor3-first=JJ|url-access=subscription }}</ref>
|Structure and optimiser used for training
|}