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Name
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Description
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TRSCustomGeneticComponent
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Represents the base class for including genetic algorithms (GA) or genetic programs (GP) in your programs.
Genetic algorithms seek to solve optimization or search problems. They are inspired by evolutionary biology and approach the search problem as a task of evolving a group or population of candidate individuals through successive generations, selecting fitter (or better) child individuals for each generation, until a solution is found. It uses evolutionary biology techniques such as inheritance, mutation, selection, and crossover (also called recombination).
The TRSCustomGeneticComponent class contains a population of individuals ( RSGeneticBase.TRSPopulation), which contain "DNA", chromosomes for genetic algorithms (Bits property) and genetic program trees for genetic programs.
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TRSIndividual
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The TRSIndividual class is the base individual class for the library. It represents one individual, or candidate solution, in the population of solutions in a genetic algorithm or genetic programming problem. With genetic components, there is a population of individuals ( RSGeneticBase.TRSPopulation), which contain "dna", chromosomes for genetic algorithms or a program tree for genetic programming.
Genetic components 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 the 2 parents together to make a child (called Crossover) |
| • | Optionally Mutating and Inverting the "dna" 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, the genetic component looks at the new population to see if any of the individuals solve the problem. 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.
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TRSPopulation
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The TRSPopulation class is the base population class for the library. It represents the entire population (the collection of TRSIndividual items) in a Genetic Algorithm or Programming problem.
Genetic components 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 the 2 parents together to make a child (called Crossover) |
| • | Optionally Mutating and Inverting the "dna" 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, the genetic component looks at the new population to see if any of the individuals solve the problem. 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.
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