In adaptive genetic simulation theory, commonly termed genetic algorithms, the simulation of sexual reproduction is a superset of crossover.
Simulated genetic evolution is typically as follows.
- Initialize population — corresponding in biology to a stable population placed under a new stress
- Replication — corresponding in biology to the creation of gametes across the population
- Crossover and mutation — corresponding in biology to imperfect chromosome unwinding, separation, alignment, splicing, bonding, mirroring, and rewinding
- Migration — corresponding in biology to geometric clustering of individuals and interchange between the clusters
- Evaluation — corresponding in biology to genetic expression governing growth and life function
- Elimination — corresponding in biology to reproductive termination of individuals in the population through injury or fatality
- Test of convergence to decide whether to replicate again or exit, returning results
Reproduction includes both replication, crossover, and mutation, not just crossover. This fact is not always obvious because replication in procedural programming languages with operator overload and collections support is often little more than an assignment operator or method call. Also, crossover is sometimes thought of as including mutation, which is not technically correct in either biology or AI.
Both are stochastic, but crossover is an exchange of data between two sequences at random splicing locations, whereas mutation is the replacement of data with random data at random locations. Because of the general acceptance of symbiogenisis as a factor in speciation and biological adaptivity, there is a need for further research into a third stochastic factor of crossover or the addition of data from other species.
Genetic Algorithms as Function Optimizers, D. Bethka, 1978
An Overview of Standard and Parallel Genetic Algorithms, Abtin Hassani, Jonatan Treijs, 1975
Cognitive Systems Based on Adaptive Algorithms, 1978, John H. Holland, Judith S. Reitman