Английская Википедия:Effective fitness
Шаблон:Short description Шаблон:Evolutionary algorithms In natural evolution and artificial evolution (e.g. artificial life and evolutionary computation) the fitness (or performance or objective measure) of a schema is rescaled to give its effective fitness which takes into account crossover and mutation.
Effective fitness is used in Evolutionary Computation to understand population dynamics.[1] While a biological fitness function only looks at reproductive success, an effective fitness function tries to encompass things that are needed to be fulfilled for survival on population level.[2] In homogeneous populations, reproductive fitness and effective fitness are equal.[1] When a population moves away from homogeneity a higher effective fitness is reached for the recessive genotype. This advantage will decrease while the population moves toward an equilibrium.[1] The deviation from this equilibrium displays how close the population is to achieving a steady state.[1] When this equilibrium is reached, the maximum effective fitness of the population is achieved.[3]
Problem solving with evolutionary computation is realized with a cost function.[4] If cost functions are applied to swarm optimization they are called a fitness function. Strategies like reinforcement learning[5] and NEAT neuroevolution[6] are creating a fitness landscape which describes the reproductive success of cellular automata.[7][8]
The effective fitness function models the number of fit offspring[1] and is used in calculations that include evolutionary processes, such as mutation and crossover, important on the population level.[9]
The effective fitness model is superior to its predecessor, the standard reproductive fitness model. It advances in the qualitatively and quantitatively understanding of evolutionary concepts like bloat, self-adaptation, and evolutionary robustness.[3] While reproductive fitness only looks at pure selection, effective fitness describes the flow of a population and natural selection by taking genetic operators into account.[1][3]
A normal fitness function fits to a problem,[10] while an effective fitness function is an assumption if the objective was reached.[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]
Applications
When evolutionary equations of the studied population dynamics are available, one can algorithmically compute the effective fitness of a given population. Though the perfect effective fitness model is yet to be found, it is already known to be a good framework to the better understanding of the moving of the genotype-phenotype map, population dynamics, and the flow on fitness landscapes.[1][3]
Models using a combination of Darwinian fitness functions and effective functions are better at predicting population trends. Effective models could be used to determine therapeutic outcomes of disease treatment.[15] Other models could determine effective protein engineering and works towards finding novel or heightened biochemistry.[16]
References
External links
- ↑ 1,0 1,1 1,2 1,3 1,4 1,5 1,6 Шаблон:Cite book
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- ↑ 3,0 3,1 3,2 3,3 Шаблон:Cite journal
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- ↑ Шаблон:Cite arXiv
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