Editing Neuroevolution (section)

==Direct and indirect encoding==

Evolutionary algorithms operate on a population of [[genotype]]s (also referred to as [[genome]]s). In neuroevolution, a genotype is mapped to a neural network [[phenotype]] that is evaluated on some task to derive its [[fitness function|fitness]].

In ''direct'' encoding schemes the genotype directly maps to the phenotype. That is, every neuron and connection in the neural network is specified directly and explicitly in the genotype. In contrast, in ''indirect'' encoding schemes the genotype specifies indirectly how that network should be generated.<ref name="cgegecco">{{Citation |last1=Kassahun|first1=Yohannes|last2=Sommer|first2=Gerald|last3=Edgington|first3=Mark|last4=Metzen|first4=Jan Hendrik|last5=Kirchner|first5=Frank|date=2007|contribution=Common genetic encoding for both direct and indirect encodings of networks|title=Genetic and Evolutionary Computation Conference |publisher=ACM Press|pages=1029–1036|citeseerx=10.1.1.159.705}}</ref>

Indirect encodings are often used to achieve several aims:<ref name="cgegecco"/><ref name=hyperneat>{{citation|last=Gauci |first= Stanley |contribution=Generating Large-Scale Neural Networks Through Discovering Geometric Regularities |title=Genetic and Evolutionary Computation Conference|year=2007 |location=New York, NY |publisher=ACM |contribution-url=https://eplex.cs.ucf.edu/papers/gauci_gecco07.pdf}}</ref><ref name=gruau94>{{Cite book|title=Neural Network Synthesis Using Cellular Encoding And The Genetic Algorithm.|last1=Gruau|first1=Frédéric|last2=I|first2=L'universite Claude Bernard-lyon|last3=Doctorat|first3=Of A. Diplome De|last4=Demongeot|first4=M. Jacques|last5=Cosnard|first5=Examinators M. Michel|last6=Mazoyer|first6=M. Jacques|last7=Peretto|first7=M. Pierre|last8=Whitley|first8=M. Darell|date=1994|citeseerx = 10.1.1.29.5939}}</ref><ref>{{Cite journal|last1=Clune|first1=J.|last2=Stanley|first2=Kenneth O.|last3=Pennock|first3=R. T.|last4=Ofria|first4=C.|date=June 2011|title=On the Performance of Indirect Encoding Across the Continuum of Regularity|journal=IEEE Transactions on Evolutionary Computation|volume=15|issue=3|pages=346–367|doi=10.1109/TEVC.2010.2104157|issn=1089-778X|citeseerx=10.1.1.375.6731|s2cid=3008628}}</ref><ref name=eshyperalife>{{cite journal |last1=Risi |first1=Sebastian |last2=Stanley |first2=Kenneth O. |title=An Enhanced Hypercube-Based Encoding for Evolving the Placement, Density, and Connectivity of Neurons |journal=Artificial Life |date=October 2012 |volume=18 |issue=4 |pages=331–363 |doi=10.1162/ARTL_a_00071 |pmid=22938563 |s2cid=3256786 |url=https://stars.library.ucf.edu/cgi/viewcontent.cgi?article=4196&context=facultybib2010 |doi-access=free }}</ref>
* modularity and other regularities;
* compression of phenotype to a smaller genotype, providing a smaller search space;
* mapping the search space (genome) to the problem domain.

===Taxonomy of embryogenic systems for indirect encoding===

Traditionally indirect encodings that employ artificial [[embryology|embryogeny]] (also known as [[artificial development]]) have been categorised along the lines of a ''grammatical approach'' versus a ''cell chemistry approach''.<ref name=taxae>{{cite journal |last1=Stanley |first1=Kenneth O. |last2=Miikkulainen |first2=Risto |title=A Taxonomy for Artificial Embryogeny |journal=Artificial Life |date=April 2003 |volume=9 |issue=2 |pages=93–130 |doi=10.1162/106454603322221487 |pmid=12906725 |s2cid=2124332 }}</ref> The former evolves sets of rules in the form of grammatical rewrite systems. The latter attempts to mimic how physical structures emerge in biology through gene expression. Indirect encoding systems often use aspects of both approaches.

Stanley and Miikkulainen<ref name=taxae /> propose a taxonomy for embryogenic systems that is intended to reflect their underlying properties. The taxonomy identifies five continuous dimensions, along which any embryogenic system can be placed:
* Cell (neuron) fate''':''' the final characteristics and role of the cell in the mature phenotype. This dimension counts the number of methods used for determining the fate of a cell.
* Targeting''':''' the method by which connections are directed from source cells to target cells. This ranges from specific targeting (source and target are explicitly identified) to relative targeting (e.g., based on locations of cells relative to each other).
* Heterochrony''':''' the timing and ordering of events during embryogeny. Counts the number of mechanisms for changing the timing of events.
* Canalization''':''' how tolerant the genome is to mutations (brittleness). Ranges from requiring precise genotypic instructions to a high tolerance of imprecise mutation.
* Complexification''':''' the ability of the system (including evolutionary algorithm and genotype to phenotype mapping) to allow complexification of the genome (and hence phenotype) over time. Ranges from allowing only fixed-size genomes to allowing highly variable length genomes.