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How can you assess the quality of any solution without a measure of quality, which, in the context of genetic algorithms, is known as fitness function? The term fitness function is due to the well-known phrase "Survival of the Fittest", which is often used to describe the Darwinian theory of natural selection (which genetic algorithms are based on)....


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You do not always need an explictly coded fitness function to perform genetic algorithm searches. The more general need is for a selection process that favours individuals that perform better at the core tasks in an environment (i.e. that are "more fit"). One way of assessing performance is to award a numerical score, but other approaches are ...


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You can define different measures in this way: Maximum distance of the new point with all points of the configuration ($M$) Minimum distance of the new point with all points of the configuration ($N$) $\frac{M}{\text{Maximum distance between two points of the configuration(}D)}$: normalize (1) measure $\frac{N}{D}$:normalize (2) measure You can get more ...


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