Information about Effective Fitness
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.
References
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Artificial Life, (commonly Alife or alife) is a field of study and associated art form which examine systems related to life, its processes and its evolution through simulations using computer models, robotics, and biochemistry[1].
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In computer science evolutionary computation is a subfield of artificial intelligence (more particularly computational intelligence) that involves combinatorial optimization problems.
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Fitness (often denoted in population genetics models) is a central concept in evolutionary theory. It describes the capability of an individual of certain genotype to reproduce, and usually is equal to the proportion of the individual's genes in all the genes of the next generation.
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A schema in general is a specific, well-documented, and consistent plan. The related word, scheme means a loosely described plan.
The word schema comes from the Greek word "σχήμα" (skhēma), which means shape
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The word schema comes from the Greek word "σχήμα" (skhēma), which means shape
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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.
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In genetic algorithms, mutation is a genetic operator used to maintain genetic diversity from one generation of a population of chromosomes to the next. It is analogous to biological mutation.
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