Effective fitness
Encyclopedia
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
. That is effective fitness can be thought of as the fitness that the schema would need to have in order to increase or decrease as a fraction of the population as it actually does with crossover and mutation present but as if they were not.
Evolution
Evolution is any change across successive generations in the heritable characteristics of biological populations. Evolutionary processes give rise to diversity at every level of biological organisation, including species, individual organisms and molecules such as DNA and proteins.Life on Earth...
and artificial evolution (e.g. artificial life
Artificial life
Artificial life is a field of study and an associated art form which examine systems related to life, its processes, and its evolution through simulations using computer models, robotics, and biochemistry. The discipline was named by Christopher Langton, an American computer scientist, in 1986...
and evolutionary computation
Evolutionary computation
In computer science, evolutionary computation is a subfield of artificial intelligence that involves combinatorial optimization problems....
) the fitness
Fitness (biology)
Fitness is a central idea in evolutionary theory. It can be defined either with respect to a genotype or to a phenotype in a given environment...
(or performance or objective measure) of a schema
Schema (genetic algorithms)
A schema is a template in computer science used in the field of genetic algorithms that identifies a subset of strings with similarities at certain string positions. Schemata are a special case of cylinder sets; and so form a topological space.- Description :...
is rescaled to give its effective fitness which takes into account crossover
Crossover (genetic algorithm)
In genetic algorithms, crossover is a genetic operator used to vary the programming of a chromosome or chromosomes from one generation to the next. It is analogous to reproduction and biological crossover, upon which genetic algorithms are based...
and mutation
Mutation (genetic algorithm)
In genetic algorithms of computing, mutation is a genetic operator used to maintain genetic diversity from one generation of a population of algorithm chromosomes to the next...
. That is effective fitness can be thought of as the fitness that the schema would need to have in order to increase or decrease as a fraction of the population as it actually does with crossover and mutation present but as if they were not.