Speciation

[nleck]

Purpose of Speciation

The primary goal of speciation in the NEAT (NeuroEvolution of Augmenting Topologies) algorithm is to protect new structural innovations in neural networks. When a new mutation occurs—such as a new node or a new connection—it might not immediately result in a higher fitness. Without protection, these new mutations could be quickly eliminated by natural selection due to initial suboptimal performance. Speciation addresses this by grouping similar genomes into species, allowing these genomes to compete primarily within their own niches rather than with the entire population. This segregation helps to ensure that new innovations have enough time to optimize before they need to compete with the rest of the population.

How Speciation Works

Genetic Distance Calculation:
Each genome is compared to others based on a measure of "genetic distance". This distance takes into account the number of excess and disjoint genes between genomes (genes that do not match up in the innovation history of two genomes), as well as the average weight differences of matching genes. The genetic distance
𝑑
d between two genomes is calculated using the formula:
Mathematica

d = c1 * (E/N) + c2 * (D/N) + c3 * W
Here,
𝐸
E is the number of excess genes,
𝐷
D is the number of disjoint genes,
𝑊
W is the average weight difference of matching genes,
𝑁
N is the number of genes in the larger genome, and
𝑐
1
c1,
𝑐
2
c2, and
𝑐
3
c3 are coefficients that determine the importance of these factors.
Thresholding:
A threshold value is used to determine whether two genomes are part of the same species. If the genetic distance between a genome and the representative genome of a species is less than the threshold, the genome is considered part of that species.
Species Representative:
Each species has a representative genome that is randomly chosen from the species in each generation. This genome is used to measure genetic distance for new or mutated genomes in subsequent generations.
Reproduction Within Species:
Reproduction occurs mostly within species. This means that crossover and mutation are typically performed between members of the same species, which helps to maintain genetic diversity and gives newly introduced genes a better chance to establish themselves within a niche before facing off against the entire population.
Adjusting Fitness Based on Species Size:
To prevent any one species from dominating the population, NEAT adjusts the fitness of individuals based on the size of their species. This shared fitness approach encourages the maintenance of multiple species within the population, as it reduces the fitness advantage of any single large species over smaller, potentially more innovative species.
Benefits of Speciation
Protection of Innovation: By allowing new structures time to optimize, speciation helps protect and foster the development of innovative solutions that might initially be less competitive.
Preservation of Diversity: It maintains genetic diversity within the population, which is crucial for effective exploration of the solution space and helps avoid premature convergence on suboptimal solutions.
Niche Specialization: Different species can specialize in different parts of the problem space, potentially leading to more robust overall solutions that can perform well across a variety of scenarios or tasks.
Speciation thus plays a critical role in balancing the exploration and exploitation dynamics of the NEAT algorithm, making it more effective at evolving complex neural network architectures over time