From 09601d1795aca12ae198505d90b576255960e281 Mon Sep 17 00:00:00 2001 From: ZviRosenfeld Date: Sat, 19 Dec 2020 19:35:45 +0200 Subject: [PATCH] Small updates --- GeneticAlgorithm/GeneticAlgorithm.nuspec | 2 +- README.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/GeneticAlgorithm/GeneticAlgorithm.nuspec b/GeneticAlgorithm/GeneticAlgorithm.nuspec index c465d4e..4ab4797 100644 --- a/GeneticAlgorithm/GeneticAlgorithm.nuspec +++ b/GeneticAlgorithm/GeneticAlgorithm.nuspec @@ -2,7 +2,7 @@ GeneticAlgorithm - 1.3.5 + 1.3.6 A parallel Genetic Algorithm Engine A parallel Genetic Algorithm Engine. GeneticAlgorithm was created to be easily customized and simple to use. Zvi Rosenfeld diff --git a/README.md b/README.md index c19532a..beeaa13 100644 --- a/README.md +++ b/README.md @@ -248,7 +248,7 @@ Existing SelectionStrategies: - [RouletteWheelSelection](/GeneticAlgorithm/SelectionStrategies/RouletteWheelSelection.cs): With RouletteWheelSelection, the chance of choosing a chromosome is equal to the chromosome's fitness divided by the total fitness. In other words, if we have two chromosomes, A and B, where A.Evaluation == 6 and B.Evaluation == 4, there's a 60% change of choosing A, and a 40% change of choosing B. - [TournamentSelection](/GeneticAlgorithm/SelectionStrategies/TournamentSelection.cs): With TournamentSelection, we choose a random n chromosomes from the population, and of then select the chromosome with the highest evaluation. In TournamentSelection, selection pressure will grow as the tournament size grows. See [this](https://en.wikipedia.org/wiki/Tournament_selection) link for more information. - [StochasticUniversalSampling](/GeneticAlgorithm/SelectionStrategies/StochasticUniversalSampling.cs): StochasticUniversalSampling (SUS) is very similar to RouletteWheelSelection. For more information look [here](https://en.wikipedia.org/wiki/Stochastic_universal_sampling). -- [RankSelection](/GeneticAlgorithm/SelectionStrategies/RankSelection.cs): RankSelection firsts ranks the chromosomes based on their evaluation. The worst will have fitness 1, second worst 2 etc. and the best will have fitness N (number of chromosomes in population). RankSelection is very similar to RouletteWheelSelection, but can lead to slower convergence, because the best chromosomes do not differ so much from other ones. +- [RankSelection](/GeneticAlgorithm/SelectionStrategies/RankSelection.cs): RankSelection firsts ranks the chromosomes based on their evaluation. The worst will have fitness 1, second worst 2 etc. and the best will have fitness N (number of chromosomes in population). RankSelection is very similar to RouletteWheelSelection, but can lead to slower convergence, because the best chromosomes do not differ so much from other ones. (Available since 1.3.6.) You can find examples of ISelectionStrategies [here](/GeneticAlgorithm/SelectionStrategies).