Effective fitness

[1] When a population moves away from homogeneity a higher effective fitness is reached for the recessive genotype.

Strategies like reinforcement learning[5] and NEAT neuroevolution[6] are creating a fitness landscape which describes the reproductive success of cellular automata.

[11] The difference is important for designing fitness functions with algorithms like novelty search in which the objective of the agents is unknown.

[12][13] In the case of bacteria effective fitness could include production of toxins and rate of mutation of different plasmids, which are mostly stochastically determined[14] When evolutionary equations of the studied population dynamics are available, one can algorithmically compute the effective fitness of a given population.

[15] Other models could determine effective protein engineering and works towards finding novel or heightened biochemistry.