Represents one individual, or candidate solution, in the Population of solutions in a genetic algorithm problem. In genetic algorithms ( RSGeneticAlgorithm.TRSGeneticAlgorithm component), there is a population of individuals ( RSGeneticAlgorithm.TRSGAPopulation ), which contain chromosomes (
Bits property). These chromosomes are used to abstractly represent the solution to the search problem. They represent through their bits the DNA of each candidate solution (or individual), represented by a TRSGAIndividual class. The chromosomes are created by taking the parameters or factors (integers, booleans, floats, and enumerations) of your search problem and concatenating them together into a sequence of bits.Genetic Algorithms then work by evolving your population towards the solution of the problem. Parents are selected from the current generation to reproduce the children of the next generation. Evolving a new generation involves:
| • | Selecting 2 parents (Individuals) to reproduce |
| • | Splicing the chromosomes of the 2 parents together to make a child (the child inherits the parents chromosomes) |
| • | Optionally Mutating and Inverting the chromosome of the child to provide randomness |
| • | Repeating the above steps until the population of the new generation has been produced. |
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After the new generation is bred, genetic algorithms looks at the new population to see if any of the individuals solve the problem. In genetic algorithms terms, this usually means evaluating the "fitness" of each individual (a numeric score that measures the ability of the individual). If any individual is "fit enough", the evolutionary search stops.
Namespace: RSGeneticAlgorithm
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Name
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Description
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AdjustedFitness
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Represents property AdjustedFitness. (Inherited from RSGeneticBase.TRSIndividual.)
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Bits
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Represents the individual's chromosome, or DNA. These bits are created by taking the parameters or factors (integers, booleans, floats, and enumerations) of your search problem and concatenating them together into a sequence of bits to create the chromosome. The Genes of the RSGeneticAlgorithm.TRSGeneticAlgorithm component represent the encoding of the bits for all the individuals.
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Collection
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Returns the population that this individual belongs to.
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Fitness
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The Fitness property is a floating point value that specifies the correctness of the individual DNA. Fitness is used by the genetic components to decide which individuals are better than others. Fitness is a score you define for every individual using an OnEvaluateFitness event handler. It may be any floating point number. However, fitness must have relative meaning among the entire range of assigned fitness numbers, e.g., a fitness of -10 must be worse than a fitness of -3 which must be worse than a fitness of 5, etc (or vice versa, the FitnessMethod property tells the genetic component whether lower or higher numbers are desirable). The NormalizedFitness value is the fitness of the individual normalized over the entire population's fitness values so that it is a value between 0 and 1 (where 0 will be the individual with the lowest fitness and 1 will be the individual with the highest fitness).
Using each individual's calculated fitness and the FitnessMethod, the genetic component will seek to either maximize the solution (e.g., keep evolving for individuals whose fitness are greater than other individuals in the population) or to minimize the solution (e.g., find the individuals whose fitness are less than other individuals).
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Name
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Allows you to provide a descriptive name for the individual. The genetics components do not use this property. (Inherited from RSGeneticBase.TRSIndividual.)
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NeedFitness
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Represents property NeedFitness. (Inherited from RSGeneticBase.TRSIndividual.)
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NormalizedFitness
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Returns the Fitness of the individual normalized over the entire population's fitness values so that it is a value between 0 and 1 (where 0 will be the individual with the lowest fitness and 1 will be the individual with the highest fitness).
(Inherited from RSGeneticBase.TRSIndividual.)
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NormalizedWeigthedFitness
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Returns the WeightedFitness of the individual normalized over the entire population's weighted fitness values so that it is a value between 0 and 1 (where 0 will be the individual with the lowest weighted fitness and 1 will be the individual with the highest weighted fitness). (Inherited from RSGeneticBase.TRSIndividual.)
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WeightedFitness
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Returns the Fitness value weighted by some function. Use the RSGeneticBase.TRSCustomGeneticComponent.OnEvaluateWeightedFitness event to specify the weighting.
The genetic components can guide the search for the best individual by using the weighted fitness instead of the regular fitness. The weighted fitness allows optimization for something other than the pure fitness. For example, with genetic programming, the weighted fitness can be used to weight the fitness for smaller programs. The FitnessMethod specifies whether the Fitness or the WeightedFitness is used in the search.
Note that the Fitness is always used for determining if we have found the "best" individual for termination purposes.
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