Name: Towards AI Legal Name: Towards AI, Inc. Description: Towards AI is the world's leading artificial intelligence (AI) and technology publication. Read by thought-leaders and decision-makers around the world. Phone Number: +1-650-246-9381 Email: pub@towardsai.net
228 Park Avenue South New York, NY 10003 United States
Website: Publisher: https://towardsai.net/#publisher Diversity Policy: https://towardsai.net/about Ethics Policy: https://towardsai.net/about Masthead: https://towardsai.net/about
Name: Towards AI Legal Name: Towards AI, Inc. Description: Towards AI is the world's leading artificial intelligence (AI) and technology publication. Founders: Roberto Iriondo, , Job Title: Co-founder and Advisor Works for: Towards AI, Inc. Follow Roberto: X, LinkedIn, GitHub, Google Scholar, Towards AI Profile, Medium, ML@CMU, FreeCodeCamp, Crunchbase, Bloomberg, Roberto Iriondo, Generative AI Lab, Generative AI Lab Denis Piffaretti, Job Title: Co-founder Works for: Towards AI, Inc. Louie Peters, Job Title: Co-founder Works for: Towards AI, Inc. Louis-François Bouchard, Job Title: Co-founder Works for: Towards AI, Inc. Cover:
Towards AI Cover
Logo:
Towards AI Logo
Areas Served: Worldwide Alternate Name: Towards AI, Inc. Alternate Name: Towards AI Co. Alternate Name: towards ai Alternate Name: towardsai Alternate Name: towards.ai Alternate Name: tai Alternate Name: toward ai Alternate Name: toward.ai Alternate Name: Towards AI, Inc. Alternate Name: towardsai.net Alternate Name: pub.towardsai.net
5 stars – based on 497 reviews

Frequently Used, Contextual References

TODO: Remember to copy unique IDs whenever it needs used. i.e., URL: 304b2e42315e

Resources

Take our 85+ lesson From Beginner to Advanced LLM Developer Certification: From choosing a project to deploying a working product this is the most comprehensive and practical LLM course out there!

Publication

Genetic Algorithm Optimization
Latest   Machine Learning

Genetic Algorithm Optimization

Last Updated on July 25, 2023 by Editorial Team

Author(s): Chinmay Bhalerao

Originally published on Towards AI.

A detailed explanation of the evolutionary and nature-inspired optimization algorithm

Photo by Sangharsh Lohakare on Unsplash

“The environment selects those few mutations that enhance survival, resulting in a series of slow transformations of one lifeform into another, the origin of a new species.”- CARL SAGAN, 1934–1996

Edit: This article has been selected as Best Article of the week and has been featured in TowardsAI weekly newsletter.”

Evolution

The concept of natural selection and biological evolution changed the perspective of thinking about evolution theory. Evolution is always a slow and gradual process that takes many centuries to work. Millions of species present on earth today arose from a single original life form through a branching process called speciation. complex creatures evolve from more simplistic ancestors naturally over time. In a nutshell, as random genetic mutations occur within an organism’s genetic code, the beneficial mutations are preserved because they aid survival — a process known as “natural selection.”

Photo by Johannes Plenio on Unsplash

DNA changes with time with different mutations and a combination of random inheritance, which is a recombination of parental DNA and mutational behaviors. This is conveniently described using tools from probability theory and stochastic processes.

“Evolution is the aggregation of thousands of semi-random events and the natural pressure to reproduce or die”-Darwinian evolution

‘Simulating Natural Selection’: a youtube video by Primer

To understand evolution, there is a great example of the prey and predator system. Fox eats rabbits, and faster rabbits tend to save their lives, whereas slower one has more probability of getting caught. Given a population, Smarter and quicker individuals are less likely to be consumed by foxes. As a result, they can continue to reproduce, which is what rabbits do best. Some of the less intelligent and slower rabbits also make it by chance. As the remaining population begins to reproduce, a good combination of rabbit genetic material is produced.

Foxes and rabbits evolve with time [image by author created by Dall. E]

some slow rabbits breed with fast rabbits, some fast rabbits breed with fast rabbits, And on top of that, nature throws in a wild hare every once in a while by mutating some of the rabbit's genetic material. Because more parents who were quicker and smarter survived the foxes, the resulting rabbits (on average) are faster and smarter than those in the original group. The good thing is that the foxes are also undergoing a similar procedure. Otherwise, the rabbits would develop into creatures that are too quick and intelligent for the foxes to capture.

Genetic Algorithm optimization

GAs was first proposed by John Holland in the 1960s. GA incorporates methods proposed by and inspired by the natural selection process. As I mentioned in the above example, the fittest individual has more chance or probability to survive. The same in GA. From the pool of solutions, the one who has more fitness has more chance to survive. Let's start the actual understanding of the Genetic Algorithm. Let's understand basic terminologies.

Genetic Algorithm terminologies

Parent: The one from which offspring is produced. member of the current generation.

Offspring: Also known as a child. offspring is a member of the next generation

Population: Population is a set of all possible solutions or chromosomes exhibiting similar gene structure

Fitness: Fitness is a number assigned to an individual representing a measure of goodness. More fitter, the more chance of survival and reproduction.

Chromosome: Chromosome is a coded form of a possible set of solutions consisting of genes made of one of two or more versions of DNA sequence (alleles).

Crossover: Crossover is the phenomenon where generally two parents produce two offspring by gene exchange.

Mutation: Mutation is a random change of the value of a gene we flip a bit and change 0 to 1 and 1 to zero.

Generation: Generation is a successively created population. In Genetic Algorithms, it is also termed as “iterations”.

Outline of genetic algorithm

  • The genetic algorithm starts with defining a proper problem statement and creating a set of initial possible populations of solutions.
  • The population is randomly generated chromosomes. like the evolution procedure, the procedure of natural selection starts.
  • During successive generations, chromosomes in the population are rated for their fitness or rated for their chance to become the solution.
  • Now, based on the evaluation of their fitness value, the new set of Chromosomes forms using a selection operation followed by crossover and mutation.
The basic flow of Genetic Algorithm procedure [Image by Author]

Selection

The first important step in Genetic algorithm operations is selection. You might have a question here! what are we selecting? I will answer this question. The fittest solution or fittest offspring/Child is our aim. for that, obviously, we have to select a parent depending on its fitness. If we have population “X”, then selection creates an intermediate population of “ X’ ” [X_hash] with the copies of chromosomes of X. More fitter chromosomes will have more copies of it !!! After this, the selection mechanism starts.

The selection operation is carried out in two ways :

  1. Roulette wheel selection

You know the word Roulette wheel from casino or gambling, right? It's a much similar concept. In gambling, we have wheels, and we predict numbers. That is, the dice will land on that predicted number or not! In the GA roulette wheel selection, the wheel is the same. just a stop point is introduced at a fixed point. The chromosome takes the value on the pie or roulette wheel exactly equal to the fitness it has.

Chromosomes and their fitness values [Image by author]

It is obvious that a more physically fit individual has a larger pie on the wheel and a higher chance of landing in front of the fixed point when the wheel is revolved. As a result, an individual likelihood of selection is directly correlated with their fitness.

Roulette wheel selection procedure. The chromosome which has the highest fitness value tends to occupy more space on the pie and has more probability of getting selected [Image by author]
  • Calculate the total sum [S]of fitnesses.
  • Generate a random number between 0 and the total sum[S].
  • Calculate the partial sum of P.
  • The Chromosome for which P exceeds S is the chosen individual.

Sum = F1 + F2 +F3+F4 + F5

Selection = (F1+F2+F3+F4+ F5)/Sum <P < (F 1 +F 2 +F 3 +F 4 + F 5 )/Sum

2. Tournament selection

In the N-Way tournament, we randomly choose N people from the population, and we choose the best of these to become parents. The following parent is chosen using the same procedure as before. Due to its ability to function even when fitness values are negative, tournament selection is a very common literary device.

Tournament selection procedure [Image credit here ]

Crossover

Crossover is the operation where we combine the properties of both parents. features of two parent chromosomes are mixed in such a way that there can be the possibility of good chromosomes offsprings.

Crossover operators have a role in the balance between exploitation and exploration, which will allow the extraction of characteristics from both parents, and hope that the resulting offspring possess good characteristics from the parents (Gallard & Esquivel, 2001).

Crossover is usually applied in a GA with a high probability [pc]. According to the position of a crossover, crossovers are divided into various types:

and for other types of crossovers, refer THIS.

If you have a question about how to select type and crossover probability, then you can refer to this link's comprehensive paper on Crossover Probability.

Mutation

According to National Geographic, A mutation is a change in the structure of a gene, the unit of heredity. Genes are made of deoxyribonucleic acid (DNA), a long molecule composed of building blocks called nucleotides. Each nucleotide is built around one of four different subunits called bases. These bases are known as guanine, cytosine, adenine, and thymine. A gene carries information in the sequence of its nucleotides, just as a sentence carries information in the sequence of its letters.

In GA, the mutation is the step where we assure that the search space will never be zero. We know that in traditional optimization algorithms like gradient descent, there is always a probability that it will stuck at local maxima/minima and assume it as a final solution. To overcome this kind of scenario, this extra effort of mutation is taken, and it helps to avoid sticking on local bulges.

Mutation and mutating DNA [images by author created by Dall. E]

In essence, mutation probability measures the likelihood that unrelated random chromosomal fragments may flip over and become something different. If your chromosome is encoded as a binary string of length 100, for instance, and your mutation risk is 1%, this indicates that, on average, 1 out of every 100 bits chosen at random will be flipped. Crossover is typically performed in GAs in a variety of ways, essentially simulating sexual genetic recombination similarly to in human reproduction.

Termination

GA’s iteration process is repeated until a termination condition has been reached, like,

  • A user-defined threshold criterion is reached
  • The fixed number of iterations reached
  • exhausted with a maximum number of possible solutions
  • Maximum fitness reached
  • Computational power termination criteria

This was just a theoretical overview of GA, but I am planning to take a case study to implement GA on it still if you want to work on a simple mathematical solution of the problem by GA with code, then refer to THIS informative blog on GA by Niranjan Pramanik, Ph.D.

You can simulate the evolution process over HERE.

Anylogic simulated the supply chain distribution routing problem [ vehicle routing problem] and solved it using a Genetic algorithm.

simulation of the supply chain distribution routing problem [ vehicle routing problem] solution using a Genetic algorithm.[credits: HERE]

You can get this simulation HERE.

Anylogic cloud has a lot of different and interesting simulations based on real-life scenarios you can check it HERE.

If you have found this article insightful

If you found this article insightful, follow me on Linkedin and medium. you can also subscribe to get notified when I publish articles. Let’s create a community! Thanks for your support!

If you want to support me :

As Your following and clapping is the most important thing, but you can also support me by buying coffee. COFFEE.

You can also read my blogs related to

A Chatbot With the Least Number of Lines Of Code

Chatbot and NLP in the simplest form

pub.towardsai.net

An Introduction to Federated Learning

Data privacy and security with Federated learning

pub.towardsai.net

“To understand humans better”: Cognitive science and AI

“To understand humans better” ….

medium.com

OCR : The Incredible reading capability of Machine

What if you have thousands of paper documents and forms and you want to store it digitally! typing each word can help…

medium.com

References :

1] Real-Coded Genetic Algorithms

2] On Enhancing Genetic Algorithms Using New Crossovers

3] Optimised crossover genetic algorithm for capacitated vehicle routing problem

[image by author created by Dall. E]

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI

Feedback ↓

Sign Up for the Course
`; } else { console.error('Element with id="subscribe" not found within the page with class "home".'); } } }); // Remove duplicate text from articles /* Backup: 09/11/24 function removeDuplicateText() { const elements = document.querySelectorAll('h1, h2, h3, h4, h5, strong'); // Select the desired elements const seenTexts = new Set(); // A set to keep track of seen texts const tagCounters = {}; // Object to track instances of each tag elements.forEach(el => { const tagName = el.tagName.toLowerCase(); // Get the tag name (e.g., 'h1', 'h2', etc.) // Initialize a counter for each tag if not already done if (!tagCounters[tagName]) { tagCounters[tagName] = 0; } // Only process the first 10 elements of each tag type if (tagCounters[tagName] >= 2) { return; // Skip if the number of elements exceeds 10 } const text = el.textContent.trim(); // Get the text content const words = text.split(/\s+/); // Split the text into words if (words.length >= 4) { // Ensure at least 4 words const significantPart = words.slice(0, 5).join(' '); // Get first 5 words for matching // Check if the text (not the tag) has been seen before if (seenTexts.has(significantPart)) { // console.log('Duplicate found, removing:', el); // Log duplicate el.remove(); // Remove duplicate element } else { seenTexts.add(significantPart); // Add the text to the set } } tagCounters[tagName]++; // Increment the counter for this tag }); } removeDuplicateText(); */ // Remove duplicate text from articles function removeDuplicateText() { const elements = document.querySelectorAll('h1, h2, h3, h4, h5, strong'); // Select the desired elements const seenTexts = new Set(); // A set to keep track of seen texts const tagCounters = {}; // Object to track instances of each tag // List of classes to be excluded const excludedClasses = ['medium-author', 'post-widget-title']; elements.forEach(el => { // Skip elements with any of the excluded classes if (excludedClasses.some(cls => el.classList.contains(cls))) { return; // Skip this element if it has any of the excluded classes } const tagName = el.tagName.toLowerCase(); // Get the tag name (e.g., 'h1', 'h2', etc.) // Initialize a counter for each tag if not already done if (!tagCounters[tagName]) { tagCounters[tagName] = 0; } // Only process the first 10 elements of each tag type if (tagCounters[tagName] >= 10) { return; // Skip if the number of elements exceeds 10 } const text = el.textContent.trim(); // Get the text content const words = text.split(/\s+/); // Split the text into words if (words.length >= 4) { // Ensure at least 4 words const significantPart = words.slice(0, 5).join(' '); // Get first 5 words for matching // Check if the text (not the tag) has been seen before if (seenTexts.has(significantPart)) { // console.log('Duplicate found, removing:', el); // Log duplicate el.remove(); // Remove duplicate element } else { seenTexts.add(significantPart); // Add the text to the set } } tagCounters[tagName]++; // Increment the counter for this tag }); } removeDuplicateText(); //Remove unnecessary text in blog excerpts document.querySelectorAll('.blog p').forEach(function(paragraph) { // Replace the unwanted text pattern for each paragraph paragraph.innerHTML = paragraph.innerHTML .replace(/Author\(s\): [\w\s]+ Originally published on Towards AI\.?/g, '') // Removes 'Author(s): XYZ Originally published on Towards AI' .replace(/This member-only story is on us\. Upgrade to access all of Medium\./g, ''); // Removes 'This member-only story...' }); //Load ionic icons and cache them if ('localStorage' in window && window['localStorage'] !== null) { const cssLink = 'https://code.ionicframework.com/ionicons/2.0.1/css/ionicons.min.css'; const storedCss = localStorage.getItem('ionicons'); if (storedCss) { loadCSS(storedCss); } else { fetch(cssLink).then(response => response.text()).then(css => { localStorage.setItem('ionicons', css); loadCSS(css); }); } } function loadCSS(css) { const style = document.createElement('style'); style.innerHTML = css; document.head.appendChild(style); } //Remove elements from imported content automatically function removeStrongFromHeadings() { const elements = document.querySelectorAll('h1, h2, h3, h4, h5, h6, span'); elements.forEach(el => { const strongTags = el.querySelectorAll('strong'); strongTags.forEach(strongTag => { while (strongTag.firstChild) { strongTag.parentNode.insertBefore(strongTag.firstChild, strongTag); } strongTag.remove(); }); }); } removeStrongFromHeadings(); "use strict"; window.onload = () => { /* //This is an object for each category of subjects and in that there are kewords and link to the keywods let keywordsAndLinks = { //you can add more categories and define their keywords and add a link ds: { keywords: [ //you can add more keywords here they are detected and replaced with achor tag automatically 'data science', 'Data science', 'Data Science', 'data Science', 'DATA SCIENCE', ], //we will replace the linktext with the keyword later on in the code //you can easily change links for each category here //(include class="ml-link" and linktext) link: 'linktext', }, ml: { keywords: [ //Add more keywords 'machine learning', 'Machine learning', 'Machine Learning', 'machine Learning', 'MACHINE LEARNING', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, ai: { keywords: [ 'artificial intelligence', 'Artificial intelligence', 'Artificial Intelligence', 'artificial Intelligence', 'ARTIFICIAL INTELLIGENCE', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, nl: { keywords: [ 'NLP', 'nlp', 'natural language processing', 'Natural Language Processing', 'NATURAL LANGUAGE PROCESSING', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, des: { keywords: [ 'data engineering services', 'Data Engineering Services', 'DATA ENGINEERING SERVICES', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, td: { keywords: [ 'training data', 'Training Data', 'training Data', 'TRAINING DATA', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, ias: { keywords: [ 'image annotation services', 'Image annotation services', 'image Annotation services', 'image annotation Services', 'Image Annotation Services', 'IMAGE ANNOTATION SERVICES', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, l: { keywords: [ 'labeling', 'labelling', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, pbp: { keywords: [ 'previous blog posts', 'previous blog post', 'latest', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, mlc: { keywords: [ 'machine learning course', 'machine learning class', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, }; //Articles to skip let articleIdsToSkip = ['post-2651', 'post-3414', 'post-3540']; //keyword with its related achortag is recieved here along with article id function searchAndReplace(keyword, anchorTag, articleId) { //selects the h3 h4 and p tags that are inside of the article let content = document.querySelector(`#${articleId} .entry-content`); //replaces the "linktext" in achor tag with the keyword that will be searched and replaced let newLink = anchorTag.replace('linktext', keyword); //regular expression to search keyword var re = new RegExp('(' + keyword + ')', 'g'); //this replaces the keywords in h3 h4 and p tags content with achor tag content.innerHTML = content.innerHTML.replace(re, newLink); } function articleFilter(keyword, anchorTag) { //gets all the articles var articles = document.querySelectorAll('article'); //if its zero or less then there are no articles if (articles.length > 0) { for (let x = 0; x < articles.length; x++) { //articles to skip is an array in which there are ids of articles which should not get effected //if the current article's id is also in that array then do not call search and replace with its data if (!articleIdsToSkip.includes(articles[x].id)) { //search and replace is called on articles which should get effected searchAndReplace(keyword, anchorTag, articles[x].id, key); } else { console.log( `Cannot replace the keywords in article with id ${articles[x].id}` ); } } } else { console.log('No articles found.'); } } let key; //not part of script, added for (key in keywordsAndLinks) { //key is the object in keywords and links object i.e ds, ml, ai for (let i = 0; i < keywordsAndLinks[key].keywords.length; i++) { //keywordsAndLinks[key].keywords is the array of keywords for key (ds, ml, ai) //keywordsAndLinks[key].keywords[i] is the keyword and keywordsAndLinks[key].link is the link //keyword and link is sent to searchreplace where it is then replaced using regular expression and replace function articleFilter( keywordsAndLinks[key].keywords[i], keywordsAndLinks[key].link ); } } function cleanLinks() { // (making smal functions is for DRY) this function gets the links and only keeps the first 2 and from the rest removes the anchor tag and replaces it with its text function removeLinks(links) { if (links.length > 1) { for (let i = 2; i < links.length; i++) { links[i].outerHTML = links[i].textContent; } } } //arrays which will contain all the achor tags found with the class (ds-link, ml-link, ailink) in each article inserted using search and replace let dslinks; let mllinks; let ailinks; let nllinks; let deslinks; let tdlinks; let iaslinks; let llinks; let pbplinks; let mlclinks; const content = document.querySelectorAll('article'); //all articles content.forEach((c) => { //to skip the articles with specific ids if (!articleIdsToSkip.includes(c.id)) { //getting all the anchor tags in each article one by one dslinks = document.querySelectorAll(`#${c.id} .entry-content a.ds-link`); mllinks = document.querySelectorAll(`#${c.id} .entry-content a.ml-link`); ailinks = document.querySelectorAll(`#${c.id} .entry-content a.ai-link`); nllinks = document.querySelectorAll(`#${c.id} .entry-content a.ntrl-link`); deslinks = document.querySelectorAll(`#${c.id} .entry-content a.des-link`); tdlinks = document.querySelectorAll(`#${c.id} .entry-content a.td-link`); iaslinks = document.querySelectorAll(`#${c.id} .entry-content a.ias-link`); mlclinks = document.querySelectorAll(`#${c.id} .entry-content a.mlc-link`); llinks = document.querySelectorAll(`#${c.id} .entry-content a.l-link`); pbplinks = document.querySelectorAll(`#${c.id} .entry-content a.pbp-link`); //sending the anchor tags list of each article one by one to remove extra anchor tags removeLinks(dslinks); removeLinks(mllinks); removeLinks(ailinks); removeLinks(nllinks); removeLinks(deslinks); removeLinks(tdlinks); removeLinks(iaslinks); removeLinks(mlclinks); removeLinks(llinks); removeLinks(pbplinks); } }); } //To remove extra achor tags of each category (ds, ml, ai) and only have 2 of each category per article cleanLinks(); */ //Recommended Articles var ctaLinks = [ /* ' ' + '

Subscribe to our AI newsletter!

' + */ '

Take our 85+ lesson From Beginner to Advanced LLM Developer Certification: From choosing a project to deploying a working product this is the most comprehensive and practical LLM course out there!

'+ '

Towards AI has published Building LLMs for Production—our 470+ page guide to mastering LLMs with practical projects and expert insights!

' + '
' + '' + '' + '

Note: Content contains the views of the contributing authors and not Towards AI.
Disclosure: This website may contain sponsored content and affiliate links.

' + 'Discover Your Dream AI Career at Towards AI Jobs' + '

Towards AI has built a jobs board tailored specifically to Machine Learning and Data Science Jobs and Skills. Our software searches for live AI jobs each hour, labels and categorises them and makes them easily searchable. Explore over 10,000 live jobs today with Towards AI Jobs!

' + '
' + '

🔥 Recommended Articles 🔥

' + 'Why Become an LLM Developer? Launching Towards AI’s New One-Stop Conversion Course'+ 'Testing Launchpad.sh: A Container-based GPU Cloud for Inference and Fine-tuning'+ 'The Top 13 AI-Powered CRM Platforms
' + 'Top 11 AI Call Center Software for 2024
' + 'Learn Prompting 101—Prompt Engineering Course
' + 'Explore Leading Cloud Providers for GPU-Powered LLM Training
' + 'Best AI Communities for Artificial Intelligence Enthusiasts
' + 'Best Workstations for Deep Learning
' + 'Best Laptops for Deep Learning
' + 'Best Machine Learning Books
' + 'Machine Learning Algorithms
' + 'Neural Networks Tutorial
' + 'Best Public Datasets for Machine Learning
' + 'Neural Network Types
' + 'NLP Tutorial
' + 'Best Data Science Books
' + 'Monte Carlo Simulation Tutorial
' + 'Recommender System Tutorial
' + 'Linear Algebra for Deep Learning Tutorial
' + 'Google Colab Introduction
' + 'Decision Trees in Machine Learning
' + 'Principal Component Analysis (PCA) Tutorial
' + 'Linear Regression from Zero to Hero
'+ '

', /* + '

Join thousands of data leaders on the AI newsletter. It’s free, we don’t spam, and we never share your email address. Keep up to date with the latest work in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

',*/ ]; var replaceText = { '': '', '': '', '
': '
' + ctaLinks + '
', }; Object.keys(replaceText).forEach((txtorig) => { //txtorig is the key in replacetext object const txtnew = replaceText[txtorig]; //txtnew is the value of the key in replacetext object let entryFooter = document.querySelector('article .entry-footer'); if (document.querySelectorAll('.single-post').length > 0) { //console.log('Article found.'); const text = entryFooter.innerHTML; entryFooter.innerHTML = text.replace(txtorig, txtnew); } else { // console.log('Article not found.'); //removing comment 09/04/24 } }); var css = document.createElement('style'); css.type = 'text/css'; css.innerHTML = '.post-tags { display:none !important } .article-cta a { font-size: 18px; }'; document.body.appendChild(css); //Extra //This function adds some accessibility needs to the site. function addAlly() { // In this function JQuery is replaced with vanilla javascript functions const imgCont = document.querySelector('.uw-imgcont'); imgCont.setAttribute('aria-label', 'AI news, latest developments'); imgCont.title = 'AI news, latest developments'; imgCont.rel = 'noopener'; document.querySelector('.page-mobile-menu-logo a').title = 'Towards AI Home'; document.querySelector('a.social-link').rel = 'noopener'; document.querySelector('a.uw-text').rel = 'noopener'; document.querySelector('a.uw-w-branding').rel = 'noopener'; document.querySelector('.blog h2.heading').innerHTML = 'Publication'; const popupSearch = document.querySelector$('a.btn-open-popup-search'); popupSearch.setAttribute('role', 'button'); popupSearch.title = 'Search'; const searchClose = document.querySelector('a.popup-search-close'); searchClose.setAttribute('role', 'button'); searchClose.title = 'Close search page'; // document // .querySelector('a.btn-open-popup-search') // .setAttribute( // 'href', // 'https://medium.com/towards-artificial-intelligence/search' // ); } // Add external attributes to 302 sticky and editorial links function extLink() { // Sticky 302 links, this fuction opens the link we send to Medium on a new tab and adds a "noopener" rel to them var stickyLinks = document.querySelectorAll('.grid-item.sticky a'); for (var i = 0; i < stickyLinks.length; i++) { /* stickyLinks[i].setAttribute('target', '_blank'); stickyLinks[i].setAttribute('rel', 'noopener'); */ } // Editorial 302 links, same here var editLinks = document.querySelectorAll( '.grid-item.category-editorial a' ); for (var i = 0; i < editLinks.length; i++) { editLinks[i].setAttribute('target', '_blank'); editLinks[i].setAttribute('rel', 'noopener'); } } // Add current year to copyright notices document.getElementById( 'js-current-year' ).textContent = new Date().getFullYear(); // Call functions after page load extLink(); //addAlly(); setTimeout(function() { //addAlly(); //ideally we should only need to run it once ↑ }, 5000); }; function closeCookieDialog (){ document.getElementById("cookie-consent").style.display = "none"; return false; } setTimeout ( function () { closeCookieDialog(); }, 15000); console.log(`%c 🚀🚀🚀 ███ █████ ███████ █████████ ███████████ █████████████ ███████████████ ███████ ███████ ███████ ┌───────────────────────────────────────────────────────────────────┐ │ │ │ Towards AI is looking for contributors! │ │ Join us in creating awesome AI content. │ │ Let's build the future of AI together → │ │ https://towardsai.net/contribute │ │ │ └───────────────────────────────────────────────────────────────────┘ `, `background: ; color: #00adff; font-size: large`); //Remove latest category across site document.querySelectorAll('a[rel="category tag"]').forEach(function(el) { if (el.textContent.trim() === 'Latest') { // Remove the two consecutive spaces (  ) if (el.nextSibling && el.nextSibling.nodeValue.includes('\u00A0\u00A0')) { el.nextSibling.nodeValue = ''; // Remove the spaces } el.style.display = 'none'; // Hide the element } }); // Add cross-domain measurement, anonymize IPs 'use strict'; //var ga = gtag; ga('config', 'G-9D3HKKFV1Q', 'auto', { /*'allowLinker': true,*/ 'anonymize_ip': true/*, 'linker': { 'domains': [ 'medium.com/towards-artificial-intelligence', 'datasets.towardsai.net', 'rss.towardsai.net', 'feed.towardsai.net', 'contribute.towardsai.net', 'members.towardsai.net', 'pub.towardsai.net', 'news.towardsai.net' ] } */ }); ga('send', 'pageview'); -->