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Abdelrahman Ogail

Simulated annealing - Wikipedia, the free encyclopedia - 1 views

  • Simulated annealing (SA) is a generic probabilistic metaheuristic for the global optimization problem of applied mathematics, namely locating a good approximation to the global minimum of a given function in a large search space. It is often used when the search space is discrete (e.g., all tours that visit a given set of cities). For certain problems, simulated annealing may be more effective than exhaustive enumeration — provided that the goal is merely to find an acceptably good solution in a fixed amount of time, rather than the best possible solution. The name and inspiration come from annealing in metallurgy, a technique involving heating and controlled cooling of a material to increase the size of its crystals and reduce their defects. The heat causes the atoms to become unstuck from their initial positions (a local minimum of the internal energy) and wander randomly through states of higher energy; the slow cooling gives them more chances of finding configurations with lower internal energy than the initial one. By analogy with this physical process, each step of the SA algorithm replaces the current solution by a random "nearby" solution, chosen with a probability that depends on the difference between the corresponding function values and on a global parameter T (called the temperature), that is gradually decreased during the process. The dependency is such that the current solution changes almost randomly when T is large, but increasingly "downhill" as T goes to zero. The allowance for "uphill" moves saves the method from becoming stuck at local minima—which are the bane of greedier methods. The method was independently described by S. Kirkpatrick, C. D. Gelatt and M. P. Vecchi in 1983 [1], and by V. Černý in 1985 [2]. The method is an adaptation of the Metropolis-Hastings algorithm, a Monte Carlo method to generate sample states of a thermodynamic system, invented by N. Metropolis et al. in 1953 [3].
  • Simulated annealing (SA) is a generic probabilistic metaheuristic for the global optimization problem of applied mathematics, namely locating a good approximation to the global minimum of a given function in a large search space. It is often used when the search space is discrete (e.g., all tours that visit a given set of cities). For certain problems, simulated annealing may be more effective than exhaustive enumeration — provided that the goal is merely to find an acceptably good solution in a fixed amount of time, rather than the best possible solution. The name and inspiration come from annealing in metallurgy, a technique involving heating and controlled cooling of a material to increase the size of its crystals and reduce their defects. The heat causes the atoms to become unstuck from their initial positions (a local minimum of the internal energy) and wander randomly through states of higher energy; the slow cooling gives them more chances of finding configurations with lower internal energy than the initial one. By analogy with this physical process, each step of the SA algorithm replaces the current solution by a random "nearby" solution, chosen with a probability that depends on the difference between the corresponding function values and on a global parameter T (called the temperature), that is gradually decreased during the process. The dependency is such that the current solution changes almost randomly when T is large, but increasingly "downhill" as T goes to zero. The allowance for "uphill" moves saves the method from becoming stuck at local minima—which are the bane of greedier methods. The method was independently described by S. Kirkpatrick, C. D. Gelatt and M. P. Vecchi in 1983 [1], and by V. Černý in 1985 [2]. The method is an adaptation of the Metropolis-Hastings algorithm, a Monte Carlo method to generate sample states of a thermodynamic system, invented by N. Metropolis et al. in 1953 [3].
  • Simulated annealing (SA) is a generic probabilistic metaheuristic for the global optimization problem of applied mathematics, namely locating a good approximation to the global minimum of a given function in a large search space. It is often used when the search space is discrete (e.g., all tours that visit a given set of cities). For certain problems, simulated annealing may be more effective than exhaustive enumeration — provided that the goal is merely to find an acceptably good solution in a fixed amount of time, rather than the best possible solution. The name and inspiration come from annealing in metallurgy, a technique involving heating and controlled cooling of a material to increase the size of its crystals and reduce their defects. The heat causes the atoms to become unstuck from their initial positions (a local minimum of the internal energy) and wander randomly through states of higher energy; the slow cooling gives them more chances of finding configurations with lower internal energy than the initial one. By analogy with this physical process, each step of the SA algorithm replaces the current solution by a random "nearby" solution, chosen with a probability that depends on the difference between the corresponding function values and on a global parameter T (called the temperature), that is gradually decreased during the process. The dependency is such that the current solution changes almost randomly when T is large, but increasingly "downhill" as T goes to zero. The allowance for "uphill" moves saves the method from becoming stuck at local minima—which are the bane of greedier methods. The method was independently described by S. Kirkpatrick, C. D. Gelatt and M. P. Vecchi in 1983 [1], and by V. Černý in 1985 [2]. The method is an adaptation of the Metropolis-Hastings algorithm, a Monte Carlo method to generate sample states of a thermodynamic system, invented by N. Metropolis et al. in 1953 [3].
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  • Simulated annealing (SA) is a generic probabilistic metaheuristic for the global optimization problem of applied mathematics, namely locating a good approximation to the global minimum of a given function in a large search space. It is often used when the search space is discrete (e.g., all tours that visit a given set of cities). For certain problems, simulated annealing may be more effective than exhaustive enumeration — provided that the goal is merely to find an acceptably good solution in a fixed amount of time, rather than the best possible solution. The name and inspiration come from annealing in metallurgy, a technique involving heating and controlled cooling of a material to increase the size of its crystals and reduce their defects. The heat causes the atoms to become unstuck from their initial positions (a local minimum of the internal energy) and wander randomly through states of higher energy; the slow cooling gives them more chances of finding configurations with lower internal energy than the initial one. By analogy with this physical process, each step of the SA algorithm replaces the current solution by a random "nearby" solution, chosen with a probability that depends on the difference between the corresponding function values and on a global parameter T (called the temperature), that is gradually decreased during the process. The dependency is such that the current solution changes almost randomly when T is large, but increasingly "downhill" as T goes to zero. The allowance for "uphill" moves saves the method from becoming stuck at local minima—which are the bane of greedier methods. The method was independently described by S. Kirkpatrick, C. D. Gelatt and M. P. Vecchi in 1983 [1], and by V. Černý in 1985 [2]. The method is an adaptation of the Metropolis-Hastings algorithm, a Monte Carlo method to generate sample states of a thermodynamic system, invented by N. Metropolis et al. in 1953 [3].
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    Simulated annealing (SA) is a generic probabilistic metaheuristic for the global optimization problem of applied mathematics, namely locating a good approximation to the global minimum of a given function in a large search space. It is often used when the search space is discrete (e.g., all tours that visit a given set of cities). For certain problems, simulated annealing may be more effective than exhaustive enumeration - provided that the goal is merely to find an acceptably good solution in a fixed amount of time, rather than the best possible solution. The name and inspiration come from annealing in metallurgy, a technique involving heating and controlled cooling of a material to increase the size of its crystals and reduce their defects. The heat causes the atoms to become unstuck from their initial positions (a local minimum of the internal energy) and wander randomly through states of higher energy; the slow cooling gives them more chances of finding configurations with lower internal energy than the initial one. By analogy with this physical process, each step of the SA algorithm replaces the current solution by a random "nearby" solution, chosen with a probability that depends on the difference between the corresponding function values and on a global parameter T (called the temperature), that is gradually decreased during the process. The dependency is such that the current solution changes almost randomly when T is large, but increasingly "downhill" as T goes to zero. The allowance for "uphill" moves saves the method from becoming stuck at local minima-which are the bane of greedier methods. The method was independently described by S. Kirkpatrick, C. D. Gelatt and M. P. Vecchi in 1983 [1], and by V. Černý in 1985 [2]. The method is an adaptation of the Metropolis-Hastings algorithm, a Monte Carlo method to generate sample states of a thermodynamic system, invented by N. Metropolis et al. in 1953 [3].
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    A natural AI approach
Janos Haits

Citavi - Reference Management and Knowledge Organization - 0 views

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    "Search databases and library catalogs directly from within Citavi. Save results to your project with a click. Surf and save: when you find a book, article, or webpage online, use the Picker to quickly add its information to Citavi. Save copies of webpages as PDFs. Find and save all available PDF full text articles in Citavi. Everything in one place and always at hand."
Janos Haits

Safety Scanner - Windows Defender Security Intelligence - 0 views

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    "Microsoft Safety Scanner is a scan tool designed to find and remove malware from Windows computers. Simply download it and run a scan to find malware and try to reverse changes made by identified threats."
Janos Haits

webXray Privacy Search Engine - 0 views

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    "ACCELERATE THE PRIVACY TRANSITION. A transition to a privacy-centric online environment is now inevitable, but the complexity of finding privacy violations is slowing down change. At webXray we make it easy for anybody to find privacy violations anywhere on the web, thereby accelerating the privacy transition."
Janos Haits

Unriddle | Faster research - 0 views

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    "Read, write and find papers really quickly Quickly find info in research papers, simplify complex topics, write with AI and keep everything organized."
bar software

Miximising Profits While Keeping Costs Low - 3 views

We use H&L bar point of sale solution to manage wage costs and payroll across multiple venues and find it an effective tool. Our managers appreciate the ability to review staff costs on a dail...

bar point of sale software POS computer Programming

started by bar software on 06 Mar 12 no follow-up yet
Abdelrahman Ogail

Genetic programming - Wikipedia, the free encyclopedia - 0 views

  • In artificial intelligence, genetic programming (GP) is an evolutionary algorithm-based methodology inspired by biological evolution to find computer programs that perform a user-defined task. It is a specialization of genetic algorithms (GA) where each individual is a computer program. Therefore it is a machine learning technique used to optimize a population of computer programs according to a fitness landscape determined by a program's ability to perform a given computational task.
  • In artificial intelligence, genetic programming (GP) is an evolutionary algorithm-based methodology inspired by biological evolution to find computer programs that perform a user-defined task. It is a specialization of genetic algorithms (GA) where each individual is a computer program. Therefore it is a machine learning technique used to optimize a population of computer programs according to a fitness landscape determined by a program's ability to perform a given computational task.
Janos Haits

Home | Open Data Portal - 0 views

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    "The European Union Open Data Portal (EU ODP) gives you access to open data published by EU institutions and bodies. All the data you can find via this catalogue are free to use and reuse for commercial or non-commercial purposes."
Janos Haits

Freedom to Tinker - Research and expert commentary on digital technologies in public life - 0 views

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    "Freedom to Tinker is hosted by Princeton's Center for Information Technology Policy, a research center that studies digital technologies in public life. Here you'll find comment and analysis from the digital frontier, written by the Center's faculty, students, and friends."
Janos Haits

discuss.ipfs.io - 0 views

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    "These forums are the main place to ask questions, share information and find like-minded people who are using IPFS, libp2p, multiformats, orbit, orbit-db, IPLD, or any of the other libraries, tools and protocols created … read more"
Janos Haits

Knil | The best way to search and find when you're mobile - 0 views

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    "Knil structures the information of the web into actionable knowledge"
Janos Haits

Lunyr - 0 views

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    "Lunyr is an Ethereum-based decentralized crowdsourced encyclopedia which rewards users with app tokens for peer-reviewing and contributing information. We aim to be the starting point of the internet for finding reliable, accurate information. Our long-term vision is to develop a knowledge base API that developers can use to create next generation decentralized applications in Artificial Intelligence, Virtual Reality, Augmented Reality, and more."
Janos Haits

EDRi - Defending rights and freedoms online - 0 views

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    'European Digital Rights (EDRi) is an association of civil and human rights organisations from across Europe. We defend rights and freedoms in the digital environment. You can find our members here.'
Janos Haits

searchcode | source code search engine - 0 views

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    'Type in anything you want to find and you will be presented with the results that match with the relevant lines highlighted. Searches can filtered down using the filter panel. Some suggested search terms,'
Janos Haits

IPFS Distributions - 0 views

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    "This is the downloads website for all the official software distributions of the IPFS Project. You can find all the apps, binaries, and packages here. Every distribution has a section on this page with … the distribution name and a short description the current version number and release date"
Janos Haits

Quantiki | Quantum Information Portal and Wiki - 1 views

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    "The world's leading portal for everyone involved in quantum information science. No matter if you are a researcher, a student or an enthusiast of quantum theory, this is the place you are going to find useful and enjoyable! While here on Quantiki you can: browse our content, including fascinating and educative articles, then create your own account and log in to gain more editorial possibilities."
Janos Haits

System Pro | Search Reinvented for Research™ - 0 views

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    "Search reinvented for research™ Meet System Pro The fastest and most reliable way to find, synthesize, and contextualize scientific research - starting in health and life sciences."
Janos Haits

Elicit: The AI Research Assistant - 0 views

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    "Analyze research papers at superhuman speed Automate time-consuming research tasks like summarizing papers, extracting data, and synthesizing your findings."
Janos Haits

FindMyAITool - List of AI Tools - 0 views

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    "Discover AI Tools for Your Business! Streamline Your Workflow with Our List of AI tools. Find Your Perfect Solution."
Janos Haits

Consensus - Evidence-Based Answers, Faster - 0 views

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    "AI Search Engine for Research. Consensus is a search engine that uses AI to find insights in research papers"
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