Tournament selection is a method of selecting an individual from a population of individuals in a genetic algorithm. Tournament selection involves running several "tournaments" among a few individuals (or "chromosomes") chosen at random from the population. The winner of each tournament (the one with the best fitness) is selected for crossover. Selection pressure, a probabilistic measure of a chromosome's likelihood of participation in the tournament based on the participant selection pool size, is easily adjusted by changing the tournament size, the reason is that if the tournament size is larger, weak individuals have a smaller chance to be selected, because, if a weak individual is selected to be in a tournament, there is a higher probability that a stronger individual is also in that tournament. The tournament selection method may be described in pseudo code: choose k (the tournament size) individuals from the population at randomchoose the best individual from the tournament with probability pchoose the second best individual with probability p*(1-p)choose the third best individual with probability p*((1-p)^2)and so on Deterministic tournament selection selects the best individual (when p = 1) in any tournament. A 1-way tournament (k = 1) selection is equivalent to random selection. There are two variants of the selection: with and without replacement. The variant without replacement guarantees that when selecting N individuals from a population of N elements, each individual participates in exactly k tournaments. An algorithm is proposed in. Note that depending on the number of elements selected, selection without replacement does not guarantee that no individual is selected more than once. It just guarantees that each individual has an equal chance of participating in the same number of tournaments. In comparison with the (stochastic) fitness proportionate selection method, tournament selection is often implemented in practice due to its lack of stochastic noise. Tournament selection has several benefits over alternative selection methods for genetic algorithms (for example, fitness proportionate selection and reward-based selection): it is efficient to code, works on parallel architectures and allows the selection pressure to be easily adjusted. Tournament selection has also been shown to be independent of the scaling of the genetic algorithm fitness function (or 'objective function') in some classifier systems. (Wikipedia).
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From playlist Working with Combinatorics
A team selection number theory problem.
We present a solution to a nice number theory probelm from the USA TSTST 2017 (IMO team selection). Please Subscribe: https://www.youtube.com/michaelpennmath?sub_confirmation=1 Merch: https://teespring.com/stores/michael-penn-math Personal Website: http://www.michael-penn.net Randolph Co
From playlist Math Contest Problems
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(New Version Available) Introduction to Voting Theory and Preference Tables
Updated Version: https://youtu.be/WdtH_8lAqQo This video introduces voting theory and explains how to make a preference table from voting ballots. Site: http://mathispower4u.com
From playlist Voting Theory
This presentation discusses the selection sort algorithm. Before writing code students should be able to sort an array on paper and show how the array is reorganized after each iteration of the selection sort algorithm. See my web link below. – – – – – – – – – – – – – – – –
From playlist Java Programming
From playlist Mathematics of Voting
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From playlist Center for Applied Cybersecurity Research (CACR)
How To Build A Tournament Web App | Session 04 | React.js
Don't Forget To Hit The Subscribe Button! This project series will help you build a web app system that is used by tournament and league organizers to manage their events. All the work involved in running a tournament like adding teams, creating groups, managing matches, will be done thro
From playlist Build A Toranment WebApp in React.js
How To Build Football Tournament Web app In React.Js | Session 04 | #programming
Don’t forget to subscribe! This project series is about building a football tournament web app in React.Js In this project, we are going to build a web-based system that is used by tournament and league organizers to manage their events. All the work involved in running a tournament like
From playlist Build Football Tournament Web app In React.Js
my first attempt at a genetic algorithm -- Watch live at https://www.twitch.tv/simuleios
From playlist Genetic Algorithms!
(April 2, 2010) Robert Sapolsky continues his two-part series on evolution focusing on individual and kin selection, behavioral logic, competitive infanticide, male/female animal hierarchies, sex-ratio fluctuation, intersexual competition, imprinted genes, sperm competition, inbred-founder
From playlist Lecture Collection | Human Behavioral Biology
How To Build A Tournament Web App | Session 03 | React.js
Don't Forget To Hit The Subscribe Button! This project series will help you build a web app system that is used by tournament and league organizers to manage their events. All the work involved in running a tournament like adding teams, creating groups, managing matches, will be done thro
From playlist Build A Toranment WebApp in React.js
How To Build Football Tournament Web app In React.Js | Session 03 | #programming
Don’t forget to subscribe! This project series is about building a football tournament web app in React.Js In this project, we are going to build a web-based system that is used by tournament and league organizers to manage their events. All the work involved in running a tournament like
From playlist Build Football Tournament Web app In React.Js
This video is about tournaments and some of their basic properties.
From playlist Basics: Graph Theory
RubyConf 2015 - Beating Go thanks to the power of randomness by Tobias Pfeiffer
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From playlist RubyConf 2015
(March 31, 2010) Stanford professor Robert Sapolsky lectures on the biology of behavioral evolution and thoroughly discusses examples such as The Prisoner's Dilemma. Stanford University http://www.stanford.edu Stanford Department of Biology http://biology.stanford.edu/ Stanford Universi
From playlist Lecture Collection | Human Behavioral Biology
Extremal Combinatorics with Po-Shen Loh - 05/01 Fri
Carnegie Mellon University is protecting the community from the COVID-19 pandemic by running courses online for the Spring 2020 semester. This is the video stream for Po-Shen Loh’s PhD-level course 21-738 Extremal Combinatorics. Professor Loh will not be able to respond to questions or com
From playlist CMU PhD-Level Course 21-738 Extremal Combinatorics
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From playlist Newest Clips | National Geographic