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Gene expression programming : Evolutionary algorithms use populations of individuals, select individuals according to fitness, and introduce genetic variation using one or more genetic operators. Their use in artificial computational systems dates back to the 1950s where they were used to solve optimization problems (e... |
Gene expression programming : The genome of gene expression programming consists of a linear, symbolic string or chromosome of fixed length composed of one or more genes of equal size. These genes, despite their fixed length, code for expression trees of different sizes and shapes. An example of a chromosome with two g... |
Gene expression programming : As shown above, the genes of gene expression programming have all the same size. However, these fixed length strings code for expression trees of different sizes. This means that the size of the coding regions varies from gene to gene, allowing for adaptation and evolution to occur smoothl... |
Gene expression programming : The k-expressions of gene expression programming correspond to the region of genes that gets expressed. This means that there might be sequences in the genes that are not expressed, which is indeed true for most genes. The reason for these noncoding regions is to provide a buffer of termin... |
Gene expression programming : The chromosomes of gene expression programming are usually composed of more than one gene of equal length. Each gene codes for a sub-expression tree (sub-ET) or sub-program. Then the sub-ETs can interact with one another in different ways, forming a more complex program. The figure shows a... |
Gene expression programming : In gene expression programming, homeotic genes control the interactions of the different sub-ETs or modules of the main program. The expression of such genes results in different main programs or cells, that is, they determine which genes are expressed in each cell and how the sub-ETs of e... |
Gene expression programming : The head/tail domain of GEP genes (both normal and homeotic) is the basic building block of all GEP algorithms. However, gene expression programming also explores other chromosomal organizations that are more complex than the head/tail structure. Essentially these complex structures consis... |
Gene expression programming : The fundamental steps of the basic gene expression algorithm are listed below in pseudocode: Select function set; Select terminal set; Load dataset for fitness evaluation; Create chromosomes of initial population randomly; For each program in population: Verify stop condition; Select progr... |
Gene expression programming : Numerical constants are essential elements of mathematical and statistical models and therefore it is important to allow their integration in the models designed by evolutionary algorithms. Gene expression programming solves this problem very elegantly through the use of an extra gene doma... |
Gene expression programming : An artificial neural network (ANN or NN) is a computational device that consists of many simple connected units or neurons. The connections between the units are usually weighted by real-valued weights. These weights are the primary means of learning in neural networks and a learning algor... |
Gene expression programming : Decision trees (DT) are classification models where a series of questions and answers are mapped using nodes and directed edges. Decision trees have three types of nodes: a root node, internal nodes, and leaf or terminal nodes. The root node and all internal nodes represent test conditions... |
Gene expression programming : GEP has been criticized for not being a major improvement over other genetic programming techniques. In many experiments, it did not perform better than existing methods. |
Gene expression programming : Ferreira, C. (2006). Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence. Springer-Verlag. ISBN 3-540-32796-7. Ferreira, C. (2002). Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence. Portugal: Angra do Heroismo. ISBN 972-95890-5-4... |
Gene expression programming : Symbolic Regression Artificial intelligence Decision trees Evolutionary algorithms Genetic algorithms Genetic programming Grammatical evolution Linear genetic programming GeneXproTools Machine learning Multi expression programming Neural networks |
Gene expression programming : GEP home page, maintained by the inventor of gene expression programming. GeneXproTools, commercial GEP software. |
Emma Hart (computer scientist) : Professor Emma Hart, FRSE (born 1967) is an English computer scientist known for her work in artificial immune systems (AIS), evolutionary computation and optimisation. She is a professor of computational intelligence at Edinburgh Napier University, editor-in-chief of the Journal of Evo... |
Emma Hart (computer scientist) : Hart was born in Middlesbrough, England in 1967. In 1990 she graduated from the University of Oxford with a first class BA(Hons) in Chemistry. She then continued her studies at the University of Edinburgh, graduating with an MSc in Artificial Intelligence in 1994, followed by a PhD that... |
Emma Hart (computer scientist) : In 2000 Hart took a position as a lecturer at Edinburgh Napier University, and was promoted to a Reader, Professor, and in 2008 Chair in Natural Computation. She is now director of the Centre of Algorithms, Visualisation and Evolving Systems (CAVES) group in the School of Computing. She... |
Emma Hart (computer scientist) : 2016, Featured article on Lifelong Learning in Optimisation, IFORS newsletter 2016, "A Combined Generative and Selective Hyper-heuristic for the Vehicle Routing Problem" presented at GECCO 2016 (Denver, USA), ACM 2016, "A Hybrid Parameter Control Approach Applied to a Diversity-based Mu... |
Emma Hart (computer scientist) : Emma Hart publications indexed by Google Scholar |
Human-based evolutionary computation : Human-based evolutionary computation (HBEC) is a set of evolutionary computation techniques that rely on human innovation. |
Human-based evolutionary computation : Human-based evolutionary computation techniques can be classified into three more specific classes analogous to ones in evolutionary computation. There are three basic types of innovation: initialization, mutation, and recombination. Here is a table illustrating which type of huma... |
Human-based evolutionary computation : Incrementalism – Adding to a project via many small changes instead of fewer large changes Interactive evolutionary computation – methods of evolutionary computation that use human evaluationPages displaying wikidata descriptions as a fallback == References == |
Interactive evolutionary computation : Interactive evolutionary computation (IEC) or aesthetic selection is a general term for methods of evolutionary computation that use human evaluation. Usually human evaluation is necessary when the form of fitness function is not known (for example, visual appeal or attractiveness... |
Interactive evolutionary computation : The number of evaluations that IEC can receive from one human user is limited by user fatigue which was reported by many researchers as a major problem. In addition, human evaluations are slow and expensive as compared to fitness function computation. Hence, one-user IEC methods s... |
Interactive evolutionary computation : IEC methods include interactive evolution strategy, interactive genetic algorithm, interactive genetic programming, and human-based genetic algorithm., |
Interactive evolutionary computation : Evolutionary art Human-based evolutionary computation Human-based genetic algorithm Human–computer interaction Karl Sims Electric Sheep SCM-Synthetic Curriculum Modeling User review |
Interactive evolutionary computation : Banzhaf, W. (1997), Interactive Evolution, Entry C2.9, in: Handbook of Evolutionary Computation, Oxford University Press, ISBN 978-0750308953 |
Interactive evolutionary computation : "EndlessForms.com, Collaborative interactive evolution allowing you to evolve 3D objects and have them 3D printed". Archived from the original on 2018-11-14. Retrieved 2011-06-18. "Art by Evolution on the Web Interactive Art Generator". Archived from the original on 2018-04-15. Re... |
Java Evolutionary Computation Toolkit : ECJ is a freeware evolutionary computation research system written in Java. It is a framework that supports a variety of evolutionary computation techniques, such as genetic algorithms, genetic programming, evolution strategies, coevolution, particle swarm optimization, and diffe... |
Java Evolutionary Computation Toolkit : Paradiseo, a metaheuristics framework MOEA Framework, an open source Java framework for multiobjective evolutionary algorithms |
Java Evolutionary Computation Toolkit : ECJ project page Wilson, G. C. McIntyre, A. Heywood, M. I. (2004), "Resource Review: Three Open Source Systems for Evolving Programs-Lilgp, ECJ and Grammatical Evolution", Genetic Programming And Evolvable Machines, 5 (19): 103-105, Kluwer Academic Publishers. ISSN 1389-2576 |
Learnable evolution model : The learnable evolution model (LEM) is a non-Darwinian methodology for evolutionary computation that employs machine learning to guide the generation of new individuals (candidate problem solutions). Unlike standard, Darwinian-type evolutionary computation methods that use random or semi-ran... |
Learnable evolution model : Cervone, P.; Franzese (January 2010), "Machine Learning for the Source Detection of Atmospheric Emissions", Proceedings of the 8th Conference on Artificial Intelligence Applications to Environmental Science, Code J1.7 Wojtusiak, J.; Michalski, R. S. (2006), "The LEM3 implementation of learna... |
MCACEA : MCACEA (Multiple Coordinated Agents Coevolution Evolutionary Algorithm) is a general framework that uses a single evolutionary algorithm (EA) per agent sharing their optimal solutions to coordinate the evolutions of the EAs populations using cooperation objectives. This framework can be used to optimize some c... |
MCACEA : MCACEA, uses multiple EAs (one per each agent) that evolve their own populations to find the best solution for its associated problem according to their individual and cooperation constraints and objective indexes. Each EA is an optimization problem that runs in parallel and that exchanges some information wit... |
MCACEA : The complete evaluation phase of the individual cooperating EAs is divided in six steps. When searching for the solution of a single EA, only the first two steps of this new evaluation process are used. MCACEA extends this process from these two only steps to the next six: 1. Evaluating the individual objectiv... |
MCACEA : Although MCACEA may look similar to the habitual parallelization of EAs, in this case, instead of distributing the solutions of the whole problem between different EAs that share their solutions periodically, the algorithm is dividing the problem into smaller problems that are solved simultaneously by each EA ... |
MCACEA : MCACEA has been used for finding and optimizing unmanned aerial vehicles (UAVs) trajectories when flying simultaneously in the same scenario. |
MCACEA : Artificial development Developmental biology Evolutionary computation Evolutionary robotics Fitness function Fitness landscape Fitness approximation Genetic operators Interactive evolutionary computation MOEA Framework, an open source Java framework for multiobjective evolutionary algorithms ECJ, a toolkit to ... |
MCACEA : L. de la Torre, J. M. de la Cruz, and B. Andrés-Toro. Evolutionary trajectory planner for multiple UAVs in realistic scenarios. IEEE Transactions on Robotics, vol. 26, no. 4, pp. 619–634, August 2010. |
Meta-optimization : Meta-optimization from numerical optimization is the use of one optimization method to tune another optimization method. Meta-optimization is reported to have been used as early as in the late 1970s by Mercer and Sampson for finding optimal parameter settings of a genetic algorithm. Meta-optimizatio... |
Meta-optimization : Optimization methods such as genetic algorithm and differential evolution have several parameters that govern their behaviour and efficiency in optimizing a given problem and these parameters must be chosen by the practitioner to achieve satisfactory results. Selecting the behavioural parameters by ... |
Meta-optimization : A simple way of finding good behavioural parameters for an optimizer is to employ another overlaying optimizer, called the meta-optimizer. There are different ways of doing this depending on whether the behavioural parameters to be tuned are real-valued or discrete-valued, and depending on what perf... |
Meta-optimization : Automated machine learning (AutoML) Hyper-heuristics == References == |
SolveIT Software : SolveIT Software Pty. Ltd. is a provider of advanced planning and scheduling enterprise software for supply and demand optimisation and predictive modelling. Based in Adelaide, South Australia, 70% of its turnover is generated from software deployed in the mining and bulk material handling sectors. |
SolveIT Software : The company was set up in 2005 by four academics who were also experienced business people, all recent immigrants to Australia. The team was headed by ex-Ernst & Young consultant Matthew Michalewicz, who had moved to Adelaide in 2004 after selling his last company, NuTech Solutions. The other three p... |
SolveIT Software : Headquartered in Adelaide, the company has over 150 staff based across operational offices in: Melbourne; Brisbane; Perth; and Chişinău, Moldova. The company develops advanced planning and scheduling business optimisation software, which helps manage complex operations using artificial intelligence. ... |
SolveIT Software : Advanced Planning & Scheduling (APS): Enterprise software for optimising complex planning and scheduling activities, especially those that are heavily constrained or require multi-stage processing. To allow for optimisation of a wide variety of planning and scheduling activities, APS can include vari... |
SolveIT Software : Zbigniew Michalewicz, Martin Schmidt, Matthew Michalewicz, Constantin Chiriac (10 November 2006). Adaptive Business Intelligence. Springer Publishing. ISBN 3540329285.: CS1 maint: multiple names: authors list (link) |
SolveIT Software : Company website Adaptive Business Intelligence® |
Glorb : Glorb (also known as GlorbWorldwide) is a pseudonymous YouTuber and TikToker who specializes in rap music using AI-generated voices of characters from the animated series SpongeBob SquarePants. Despite being very similar to him name and genre wise, a channel by the name of Blorg is not owned nor operated by Glo... |
Glorb : Glorb is pseudonymous. In an interview with Moist Cr1TiKaL, Glorb claimed to have been leaving small hints in their music about their identity. Glorb began uploading to YouTube and TikTok in June 2023, and their first song, "The Formula" (stylized in all caps), was released on June 4. The animated music video f... |
Glorb : Artificial intelligence and copyright Music and artificial intelligence == References == |
Magpasikat : Magpasikat (lit. 'Show-Off') is the annual talent competition segment of the Philippine television variety show It's Showtime, where the hosts and other cast members compete against each other. A week-long anniversary special, it is usually held either a week before the show's anniversary date, October 24,... |
Magpasikat : Magpasikat was first held on October 23, 2010, as an anniversary special. Since then, it has become a week-long annual tradition. However, the 2020 and 2021 editions were held in one day due to the COVID-19 pandemic. Magpasikat was also the name of a placeholder show that replaced Showtime in January 2010,... |
Magpasikat : Except on certain occasions, the It's Showtime cast members and staff are usually divided into five teams by drawing lots. The same method is also used to decide the order of performances. The first team would perform on Monday, the second team on Tuesday, and so on until Friday, with the results being ann... |
Magpasikat : Tawag ng Tanghalan |
Magpasikat : Official website |
Anytime algorithm : In computer science, an anytime algorithm is an algorithm that can return a valid solution to a problem even if it is interrupted before it ends. The algorithm is expected to find better and better solutions the longer it keeps running. Most algorithms run to completion: they provide a single answer... |
Anytime algorithm : An anytime algorithm may be also called an "interruptible algorithm". They are different from contract algorithms, which must declare a time in advance; in an anytime algorithm, a process can just announce that it is terminating. |
Anytime algorithm : The goal of anytime algorithms are to give intelligent systems the ability to make results of better quality in return for turn-around time. They are also supposed to be flexible in time and resources. They are important because artificial intelligence or AI algorithms can take a long time to comple... |
Anytime algorithm : When the decider has to act, there must be some ambiguity. Also, there must be some idea about how to solve this ambiguity. This idea must be translatable to a state to action diagram. |
Anytime algorithm : The performance profile estimates the quality of the results based on the input and the amount of time that is allotted to the algorithm. The better the estimate, the sooner the result would be found. Some systems have a larger database that gives the probability that the output is the expected outp... |
Anytime algorithm : Initial behavior: While some algorithms start with immediate guesses, others take a more calculated approach and have a start up period before making any guesses. Growth direction: How the quality of the program's "output" or result, varies as a function of the amount of time ("run time") Growth rat... |
Anytime algorithm : == Further reading == |
Autonomic computing : Autonomic computing (AC) is distributed computing resources with self-managing characteristics, adapting to unpredictable changes while hiding intrinsic complexity to operators and users. Initiated by IBM in 2001, this initiative ultimately aimed to develop computer systems capable of self-managem... |
Autonomic computing : The AC system concept is designed to make adaptive decisions, using high-level policies. It will constantly check and optimize its status and automatically adapt itself to changing conditions. An autonomic computing framework is composed of autonomic components (AC) interacting with each other. An... |
Autonomic computing : Forecasts suggested that the computing devices in use would grow at 38% per year and the average complexity of each device was increasing. This volume and complexity was managed by highly skilled humans; but the demand for skilled IT personnel was already outstripping supply, with labour costs exc... |
Autonomic computing : A possible solution could be to enable modern, networked computing systems to manage themselves without direct human intervention. The Autonomic Computing Initiative (ACI) aims at providing the foundation for autonomic systems. It is inspired by the autonomic nervous system of the human body. This... |
Autonomic computing : IBM defined five evolutionary levels, or the autonomic deployment model, for the deployment of autonomic systems: Level 1 is the basic level that presents the current situation where systems are essentially managed manually. Levels 2–4 introduce increasingly automated management functions, while l... |
Autonomic computing : The design complexity of Autonomic Systems can be simplified by utilizing design patterns such as the model–view–controller (MVC) pattern to improve concern separation by encapsulating functional concerns. |
Autonomic computing : A basic concept that will be applied in Autonomic Systems are closed control loops. This well-known concept stems from Process Control Theory. Essentially, a closed control loop in a self-managing system monitors some resource (software or hardware component) and autonomously tries to keep its par... |
Autonomic computing : A fundamental building block of an autonomic system is the sensing capability (Sensors Si), which enables the system to observe its external operational context. Inherent to an autonomic system is the knowledge of the Purpose (intention) and the Know-how to operate itself (e.g., bootstrapping, con... |
Autonomic computing : Autonomic networking Autonomic nervous system Organic computing Resilience (network) |
Autonomic computing : International Conference on Autonomic Computing (ICAC 2013) Autonomic Computing by Richard Murch published by IBM Press Autonomic Computing articles and tutorials Practical Autonomic Computing – Roadmap to Self Managing Technology Autonomic computing blog Whitestein Technologies – provider of deve... |
Batch normalization : Batch normalization (also known as batch norm) is a method used to make training of artificial neural networks faster and more stable through normalization of the layers' inputs by re-centering and re-scaling. It was proposed by Sergey Ioffe and Christian Szegedy in 2015. The reasons behind the ef... |
Batch normalization : Each layer of a neural network has inputs with a corresponding distribution, which is affected during the training process by the randomness in the parameter initialization and the randomness in the input data. The effect of these sources of randomness on the distribution of the inputs to internal... |
Batch normalization : Although batch normalization has become popular due to its strong empirical performance, the working mechanism of the method is not yet well-understood. The explanation made in the original paper was that batch norm works by reducing internal covariate shift, but this has been challenged by more r... |
Batch normalization : Ioffe, Sergey; Szegedy, Christian (2015). "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift", ICML'15: Proceedings of the 32nd International Conference on International Conference on Machine Learning - Volume 37, July 2015 Pages 448–456 Simonyan, Karen; ... |
Belief–desire–intention software model : The belief–desire–intention software model (BDI) is a software model developed for programming intelligent agents. Superficially characterized by the implementation of an agent's beliefs, desires and intentions, it actually uses these concepts to solve a particular problem in ag... |
Belief–desire–intention software model : In order to achieve this separation, the BDI software model implements the principal aspects of Michael Bratman's theory of human practical reasoning (also referred to as Belief-Desire-Intention, or BDI). That is to say, it implements the notions of belief, desire and (in partic... |
Belief–desire–intention software model : A BDI agent is a particular type of bounded rational software agent, imbued with particular mental attitudes, viz: Beliefs, Desires and Intentions (BDI). |
Belief–desire–intention software model : Action selection Artificial intelligence Belief–desire–intention model Belief revision Intelligent agent Reasoning Software agent |
Belief–desire–intention software model : A. S. Rao and M. P. Georgeff. Modeling Rational Agents within a BDI-Architecture. In Proceedings of the 2nd International Conference on Principles of Knowledge Representation and Reasoning, pages 473–484, 1991. A. S. Rao and M. P. Georgeff. BDI-agents: From Theory to Practice Ar... |
Blackboard system : A blackboard system is an artificial intelligence approach based on the blackboard architectural model, where a common knowledge base, the "blackboard", is iteratively updated by a diverse group of specialist knowledge sources, starting with a problem specification and ending with a solution. Each k... |
Blackboard system : The following scenario provides a simple metaphor that gives some insight into how a blackboard functions: A group of specialists are seated in a room with a large blackboard. They work as a team to brainstorm a solution to a problem, using the blackboard as the workplace for cooperatively developin... |
Blackboard system : A blackboard-system application consists of three major components The software specialist modules, which are called knowledge sources (KSs). Like the human experts at a blackboard, each knowledge source provides specific expertise needed by the application. The blackboard, a shared repository of pr... |
Blackboard system : We start by discussing two well known early blackboard systems, BB1 and GBB, below and then discuss more recent implementations and applications. The BB1 blackboard architecture was originally inspired by studies of how humans plan to perform multiple tasks in a trip, used task-planning as a simplif... |
Blackboard system : Blackboard-like systems have been constructed within modern Bayesian machine learning settings, using agents to add and remove Bayesian network nodes. In these 'Bayesian Blackboard' systems, the heuristics can acquire more rigorous probabilistic meanings as proposal and acceptances in Metropolis Has... |
Blackboard system : Artificial intelligence systems integration Autonomous decentralized systems Opportunistic reasoning Pandemonium architecture Tuple spaces |
Blackboard system : Open Blackboard System An open source framework for developing blackboard systems. GBBopen An open source blackboard system framework for Common Lisp. Blackboard Event Processor An open source blackboard implementation that runs on the JVM but supports plan scripting in JavaScript and JRuby. KOGMO-R... |
Blackboard system : Craig, Iain (1995). Blackboard Systems. Norwood, NJ: Ablex. ISBN 978-0-89391-594-0. Corkill, Daniel D.; Gallagher, Kevin Q.; Johnson, Philip M. (July 1987). "Achieving flexibility, efficiency, and generality in blackboard architectures" (PDF). Proceedings of the National Conference on Artificial Int... |
Blended artificial intelligence : Blended artificial intelligence (blended AI) refers to the blending of different artificial intelligence techniques or approaches to achieve more robust and practical solutions. It involves integrating multiple AI models, algorithms, and technologies to leverage their respective streng... |
Blended artificial intelligence : In the context of machine learning, blended AI can involve using different types of models, such as generative AI, decision trees, neural networks, and support vector machines. By combining their results, predictions are more accurate and reliable. This blending of models can be done t... |
Blended artificial intelligence : F5: How AI can be blended into IT automation security - Intelligent CIO Africa A New Artificial Intelligence (AI) Study Proposes A 3D-Aware Blending Technique With Generative NeRFs Google blends AI with art to create these fantastic casual games The Perfect Blend: How to Successfully C... |
Data pack : A data pack (or fact pack) is a pre-made database that can be fed to a software, such as software agents, game, Internet bots or chatterbots, to teach information and facts, which it can later look up. In other words, a data pack can be used to feed minor updates into a system. |
Data pack : Common data packs may include abbreviations, acronyms, dictionaries, lexicons and technical data, such as country codes, RFCs, filename extensions, TCP and UDP port numbers, country calling codes, and so on. Data packs may come in formats of CSV and SQL that can easily be parsed or imported into a database ... |
Data pack : A data pack DataPack Definition is similar to a data packet it contains loads of information (data) and stores it within a pack where the data can be compressed to reduce its file size. Only certain programs can read a data pack therefore when the data is packed it is vital to know whether the receiving pro... |
Data pack : When you refer to the word data pack it can come in many forms such as a mobile data pack. A mobile data pack refers to an add-on which can enable you to boost the amount of data which you can use on your mobile phone. The rate at which you use your data can also be monitored, so you know how much data you ... |
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