Analyzing Meme Propagation in Multimemetic AlgorithmsRafael Nogueras
This document describes a study analyzing meme propagation in multimemetic algorithms (MMAs). It presents an idealized model of MMAs where each agent carries a solution ("gene") and a meme to improve the solution. Computational experiments were conducted to explore how population size, meme improvement potential, and spatial structure affect meme propagation dynamics. The results show that selection intensity, spatial structure, and initial meme margins influence which memes proliferate. Future work is proposed to study other network topologies and memetic models.
Ramos, almeida: artificial ant colonies in digital image habitats – a mass be...ArchiLab 7
This document discusses using artificial ant colony behavior models to perform image segmentation. It begins by summarizing previous work using ant colony models for optimization problems. It then describes the Chialvo and Millonas model of ant swarm behavior, which uses pheromone deposition and evaporation on a grid to simulate ant trails. The document proposes extending this model to digital image habitats, treating pixel intensities as the landscape. It argues that global image perception could emerge from the collective behavior of individual "ants" reacting locally to the pheromone field and pixel intensities. The goal is for the ant colony to implicitly learn and represent the image through their adaptive pheromone deposition.
An Analysis of a Selecto-Lamarckian Model of Multimemetic Algorithms with Dyn...Rafael Nogueras
This document analyzes a selecto-Lamarckian model of multimemetic algorithms with a dynamic self-organized topology. It explores how memes propagate in a population structure that allows individuals to move on a grid and form clusters. The model combines ideas from swarm intelligence and cellular automata. Simulation results show that a dynamic population structure provides better results than static structures and panmictic populations, and that the convergence of the algorithm can be adjusted by tuning self-organization versus evolution. Future work is proposed to analyze other topologies and movement policies.
On Meme Self-Adaptation in Spatially-Structured Multimemetic AlgorithmsRafael Nogueras
This document summarizes a paper that examines meme self-adaptation in spatially-structured multimemetic algorithms. It introduces key concepts like memes, memetic algorithms, and multimemetic algorithms. It then describes the model used, which represents memes as rewriting rules of variable length and uses a spatial structure with neighborhoods. The document outlines the experimental setup, benchmark problems, and presents results showing that the spatially-structured approach finds better solutions and the optimum more often than a panmictic approach.
This document presents a method for applying random matrix theory to analyze deep neural networks using nonlinear activations. The key results are:
1) The moments method is used to derive a quartic equation that the Stieltjes transform of the Gram matrix YTY satisfies, where Y=f(WX) and W,X are random matrices and f is a nonlinear activation.
2) This allows computing properties of the Gram matrix like its limiting spectral distribution and the training loss of a random feature network.
3) Certain activations preserve the eigenvalue distribution of the data covariance matrix XTX, analogous to batch normalization. These activations may improve training and are a new class worthy of further study.
Michael Farina presented on establishing new upper bounds for the k-distance domination numbers of grid graphs by generalizing an existing construction of dominating sets to k-distance dominating sets. Armando Grez examined a method for constructing fullerene patches with 4 pentagonal faces and produced an exact process for drawing them. Darleen Perez-Lavin partitioned the set of permutations with a peak set into subsets ending with an ascent or descent and provided formulas to enumerate these subsets for Coxeter groups of types B and D.
soft computing BTU MCA 3rd SEM unit 1 .pptxnaveen356604
This document discusses hard computing and soft computing. Hard computing uses deterministic algorithms and mathematical models to produce accurate and predictable results, while soft computing can handle imprecision, uncertainty, and ambiguity. Soft computing techniques include fuzzy logic, neural networks, genetic algorithms, probabilistic reasoning, and evolutionary computation. These techniques aim to mimic human-like reasoning by tolerating uncertainty, learning and adapting, and integrating multiple methods. Examples of evolutionary computation algorithms provided are genetic algorithms, genetic programming, evolutionary strategies, differential evolution, and particle swarm optimization. Neural networks, ant colony optimization, and fuzzy logic are also summarized.
Neural Model-Applying Network (Neuman): A New Basis for Computational Cognitionaciijournal
NeuMAN represents a new model for computational cognition synthesizing important results across AI, psychology, and neuroscience. NeuMAN is based on three important ideas: (1) neural mechanisms perform all requirements for intelligence without symbolic reasoning on finite sets, thus avoiding exponential matching algorithms; (2) the network reinforces hierarchical abstraction and composition for sensing and acting; and (3) the network uses learned sequences within contextual frames to make predictions, minimize reactions to expected events, and increase responsiveness to high-value information. These systems exhibit both automatic and deliberate processes. NeuMAN accords with a wide variety of findings in neural and cognitive science and will supersede symbolic reasoning as a foundation for AI and as a model of human intelligence. It will likely become the principal mechanism for engineering intelligent systems.
Analyzing Meme Propagation in Multimemetic AlgorithmsRafael Nogueras
This document describes a study analyzing meme propagation in multimemetic algorithms (MMAs). It presents an idealized model of MMAs where each agent carries a solution ("gene") and a meme to improve the solution. Computational experiments were conducted to explore how population size, meme improvement potential, and spatial structure affect meme propagation dynamics. The results show that selection intensity, spatial structure, and initial meme margins influence which memes proliferate. Future work is proposed to study other network topologies and memetic models.
Ramos, almeida: artificial ant colonies in digital image habitats – a mass be...ArchiLab 7
This document discusses using artificial ant colony behavior models to perform image segmentation. It begins by summarizing previous work using ant colony models for optimization problems. It then describes the Chialvo and Millonas model of ant swarm behavior, which uses pheromone deposition and evaporation on a grid to simulate ant trails. The document proposes extending this model to digital image habitats, treating pixel intensities as the landscape. It argues that global image perception could emerge from the collective behavior of individual "ants" reacting locally to the pheromone field and pixel intensities. The goal is for the ant colony to implicitly learn and represent the image through their adaptive pheromone deposition.
An Analysis of a Selecto-Lamarckian Model of Multimemetic Algorithms with Dyn...Rafael Nogueras
This document analyzes a selecto-Lamarckian model of multimemetic algorithms with a dynamic self-organized topology. It explores how memes propagate in a population structure that allows individuals to move on a grid and form clusters. The model combines ideas from swarm intelligence and cellular automata. Simulation results show that a dynamic population structure provides better results than static structures and panmictic populations, and that the convergence of the algorithm can be adjusted by tuning self-organization versus evolution. Future work is proposed to analyze other topologies and movement policies.
On Meme Self-Adaptation in Spatially-Structured Multimemetic AlgorithmsRafael Nogueras
This document summarizes a paper that examines meme self-adaptation in spatially-structured multimemetic algorithms. It introduces key concepts like memes, memetic algorithms, and multimemetic algorithms. It then describes the model used, which represents memes as rewriting rules of variable length and uses a spatial structure with neighborhoods. The document outlines the experimental setup, benchmark problems, and presents results showing that the spatially-structured approach finds better solutions and the optimum more often than a panmictic approach.
This document presents a method for applying random matrix theory to analyze deep neural networks using nonlinear activations. The key results are:
1) The moments method is used to derive a quartic equation that the Stieltjes transform of the Gram matrix YTY satisfies, where Y=f(WX) and W,X are random matrices and f is a nonlinear activation.
2) This allows computing properties of the Gram matrix like its limiting spectral distribution and the training loss of a random feature network.
3) Certain activations preserve the eigenvalue distribution of the data covariance matrix XTX, analogous to batch normalization. These activations may improve training and are a new class worthy of further study.
Michael Farina presented on establishing new upper bounds for the k-distance domination numbers of grid graphs by generalizing an existing construction of dominating sets to k-distance dominating sets. Armando Grez examined a method for constructing fullerene patches with 4 pentagonal faces and produced an exact process for drawing them. Darleen Perez-Lavin partitioned the set of permutations with a peak set into subsets ending with an ascent or descent and provided formulas to enumerate these subsets for Coxeter groups of types B and D.
soft computing BTU MCA 3rd SEM unit 1 .pptxnaveen356604
This document discusses hard computing and soft computing. Hard computing uses deterministic algorithms and mathematical models to produce accurate and predictable results, while soft computing can handle imprecision, uncertainty, and ambiguity. Soft computing techniques include fuzzy logic, neural networks, genetic algorithms, probabilistic reasoning, and evolutionary computation. These techniques aim to mimic human-like reasoning by tolerating uncertainty, learning and adapting, and integrating multiple methods. Examples of evolutionary computation algorithms provided are genetic algorithms, genetic programming, evolutionary strategies, differential evolution, and particle swarm optimization. Neural networks, ant colony optimization, and fuzzy logic are also summarized.
Neural Model-Applying Network (Neuman): A New Basis for Computational Cognitionaciijournal
NeuMAN represents a new model for computational cognition synthesizing important results across AI, psychology, and neuroscience. NeuMAN is based on three important ideas: (1) neural mechanisms perform all requirements for intelligence without symbolic reasoning on finite sets, thus avoiding exponential matching algorithms; (2) the network reinforces hierarchical abstraction and composition for sensing and acting; and (3) the network uses learned sequences within contextual frames to make predictions, minimize reactions to expected events, and increase responsiveness to high-value information. These systems exhibit both automatic and deliberate processes. NeuMAN accords with a wide variety of findings in neural and cognitive science and will supersede symbolic reasoning as a foundation for AI and as a model of human intelligence. It will likely become the principal mechanism for engineering intelligent systems.
NEURAL MODEL-APPLYING NETWORK (NEUMAN): A NEW BASIS FOR COMPUTATIONAL COGNITIONaciijournal
NeuMAN represents a new model for computational cognition synthesizing important results across AI,
psychology, and neuroscience. NeuMAN is based on three important ideas: (1) neural mechanisms perform
all requirements for intelligence without symbolic reasoning on finite sets, thus avoiding exponential
matching algorithms; (2) the network reinforces hierarchical abstraction and composition for sensing and
acting; and (3) the network uses learned sequences within contextual frames to make predictions, minimize
reactions to expected events, and increase responsiveness to high-value information. These systems exhibit
both automatic and deliberate processes. NeuMAN accords with a wide variety of findings in neural and
cognitive science and will supersede symbolic reasoning as a foundation for AI and as a model of human
intelligence. It will likely become the principal mechanism for engineering intelligent systems.
The document describes using a genetic algorithm to find the maximum values of single-variable functions. It presents the genetic algorithm process, including representation of solutions, initialization, evaluation, selection, and genetic operators. The algorithm is tested on various continuous and non-continuous functions, like polynomials, rationals, trigonometric, and those with asymptotes. The results show that the genetic algorithm can find the true maximum or one very close within a reasonable number of generations, even for complex multi-modal functions that are difficult to optimize with traditional methods.
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
The document describes using a genetic algorithm to find the maximum values of single-variable functions. It discusses:
1) How genetic algorithms work by simulating biological evolution to optimize solutions.
2) Testing the genetic algorithm on continuous and non-continuous functions that are difficult to optimize with traditional methods, such as multimodal, non-differentiable functions.
3) The genetic algorithm was able to find maximum values close to the real maximum for complex test functions, demonstrating its effectiveness at optimizing these difficult single-variable functions.
Speech Recognition using HMM & GMM Models: A Review on Techniques and Approachesijsrd.com
Many ways of communications are used between human and computer, while using gesture is considered to be one of the most natural ways in a virtual reality system. Speech recognition is one of the typical methods of non-verbal communication for human beings and we naturally use various gestures to express our own intentions in everyday life. Gesture recognizers are supposed to capture and analyze the information transmitted by the hands of a person who communicates in sign language. This is a prerequisite for automatic sign-to-spoken-language translation, which has the potential to support the integration of deaf people into society. This paper present part of literature review on ongoing research and findings on different technique and approaches in gesture recognition using Hidden Markov Models for vision-based approach.
Energy-Based Models with Applications to Speech and Language Processingnxmaosdh232
Energy-based models (EBMs) are an important class of probabilistic models that define a joint probability distribution based on an "energy function". EBMs include undirected graphical models and random fields. The tutorial will cover the basics of EBMs, including learning and inference methods, and applications of EBMs to language modeling, speech recognition, and natural language labeling. It will also discuss upgrading EBMs to handle sequential data and semi-supervised learning tasks.
During the past decade, the size of 3D seismic data volumes and the number of seismic attributes have increased
to the extent that it is difficult, if not impossible, for interpreters to examine every seismic line and time
slice. To address this problem, several seismic facies classification algorithms including k-means, self-organizing
maps, generative topographic mapping, support vector machines, Gaussian mixture models, and artificial neural
networks have been successfully used to extract features of geologic interest from multiple volumes. Although
well documented in the literature, the terminology and complexity of these algorithms may bewilder the average
seismic interpreter, and few papers have applied these competing methods to the same data volume. We have
reviewed six commonly used algorithms and applied them to a single 3D seismic data volume acquired over the
Canterbury Basin, offshore New Zealand, where one of the main objectives was to differentiate the architectural
elements of a turbidite system. Not surprisingly, the most important parameter in this analysis was the choice of
the correct input attributes, which in turn depended on careful pattern recognition by the interpreter. We found
that supervised learning methods provided accurate estimates of the desired seismic facies, whereas unsupervised
learning methods also highlighted features that might otherwise be overlooked.
The document discusses von Neumann entropy in quantum computation. It provides definitions of key terms like von Neumann entropy, density matrix, and computational complexity theory. Von Neumann entropy extends concepts of classical entropy to quantum mechanics and characterizes the classical and quantum information capacities of an ensemble. It quantifies the degree of mixing of a quantum state and how much a state departs from a pure state. The von Neumann entropy of a system is computed using the density matrix and eigendecomposition of the system's quantum state.
This document provides information about genetic algorithms including:
1. Definitions of genetic algorithms from Grefenstette and Goldberg that describe genetic algorithms as search algorithms based on biological evolution and natural selection.
2. An overview of genetic algorithms including the basic concepts of populations, chromosomes, genes, fitness functions, selection, crossover, and mutation.
3. Examples of genetic representations like binary encoding and permutation encoding.
4. Descriptions of genetic operators like selection, crossover, and mutation that maintain genetic diversity between generations.
SWARM INTELLIGENCE FROM NATURAL TO ARTIFICIAL SYSTEMS: ANT COLONY OPTIMIZATIONFransiskeran
This document summarizes research on ant colony optimization (ACO), a metaheuristic algorithm inspired by the foraging behavior of ants. It describes how real ant colonies use pheromone trails to efficiently find short paths between their nest and food sources through decentralized cooperation. The document then explains how ACO works by simulating artificial ants that probabilistically construct solutions and update pheromone values to guide future construction. Several standard ACO algorithms are outlined, including Ant System, Ant Colony System, Max-Min Ant System, and Rank-Based Ant System. Applications of ACO discussed include the traveling salesman problem.
A COMPREHENSIVE SURVEY OF GREY WOLF OPTIMIZER ALGORITHM AND ITS APPLICATIONJaresJournal
This study presents a comprehensive and through summary of the Grey Wolf Optimizer (GWO).The GWO algorithm is a newly-presented meta-heuristic, propelled from the social hunting behavior of grey wolves. The GWO has become a progressively critical device of Swarm Intelligence that has been used in nearly all zones of optimization, and engineering practice. Numerous issues from different regions have been effectively illuminated utilizing the GWO algorithm and its variants. In arrange to utilize the calculation to illuminate assorted issues, the original GWO algorithm required to be modified or hybridized. This study conducts an exhaustive review of this living and advancing area of Swarm Intelligence, so that to show that the GWO algorithm might be connected to each issue emerging in hone. However, it empowers novice researchers and algorithm developers to utilize this straightforward and however exceptionally effective algorithm for issue tackling. It frequently ensures that the gained results about will meet the expectation.
Join us for an enlightening session on AI/ML by Jeevanshi Sharma, an MS graduate from the University of Alberta with accolades from Outreachy'22 and MITACS GRI'21. Delve into cutting-edge advancements, applications, and ethical considerations. Learn basic steps to start your ML journey and explore industry applications, advancements, and associated careers.
Trabajo Ya Publicado presentado en CoSECiVi'14.
Resumen:
Flocking strategies are sets of behavior rules for the interaction of agents that allow to devise controllers with reduced complexity that generate emerging behavior. In this paper, we present an application of genetic algorithms and flocking strategies to control the Ghost Team in the game Ms. Pac-Man. In particular, we define flocking strategies for the Ghost Team and optimize them for robustness with respect to the stochastic elements of the game and effectiveness against different possible opponents by means of genetic algorithm. The performance of the methodology proposed is tested and compared with that of other standard controllers. The results show that flocking strategies are capable of modeling complex behaviors and produce effective and challenging agents.
Swarm intelligence is an artificial intelligence technique inspired by the collective behavior of decentralized and self-organized systems found in nature, such as ant colonies and bird flocks. Two common swarm intelligence algorithms are ant colony optimization and particle swarm optimization. Ant colony optimization is based on the behavior of real ant colonies and can be used to find approximate solutions to difficult optimization problems. Particle swarm optimization is a population-based stochastic optimization technique inspired by swarming behavior in nature, such as bird flocking. It searches for optimal solutions within a problem space by updating the movement of individual particles based on their own experiences and those of neighboring particles.
A Neural Probabilistic Language Model.pptx
Bengio, Yoshua, et al. "A neural probabilistic language model." Journal of machine learning research 3.Feb (2003): 1137-1155.
A goal of statistical language modeling is to learn the joint probability function of sequences of
words in a language. This is intrinsically difficult because of the curse of dimensionality: a word
sequence on which the model will be tested is likely to be different from all the word sequences seen
during training. Traditional but very successful approaches based on n-grams obtain generalization
by concatenating very short overlapping sequences seen in the training set. We propose to fight the
curse of dimensionality by learning a distributed representation for words which allows each
training sentence to inform the model about an exponential number of semantically neighboring
sentences. The model learns simultaneously (1) a distributed representation for each word along
with (2) the probability function for word sequences, expressed in terms of these representations.
Generalization is obtained because a sequence of words that has never been seen before gets high
probability if it is made of words that are similar (in the sense of having a nearby representation) to
words forming an already seen sentence. Training such large models (with millions of parameters)
within a reasonable time is itself a significant challenge. We report on experiments using neural
networks for the probability function, showing on two text corpora that the proposed approach
significantly improves on state-of-the-art n-gram models, and that the proposed approach allows to
take advantage of longer contexts.
Behavior study of entropy in a digital image through an iterative algorithmijscmcj
Image segmentation is a critical step in computer vision tasks constituting an essential issue for pattern recognition and visual interpretation. In this paper, we study the behavior of entropy in digital images through an iterative algorithm of mean shift filtering. The order of a digital image in gray levels is defined. The behavior of Shannon entropy is analyzed and then compared, taking into account the number of iterations of our algorithm, with the maximum entropy that could be achieved under the same order. The use of equivalence classes it induced, which allow us to interpret entropy as a hyper-surface in real m dimensional space. The difference of the maximum entropy of order n and the entropy of the image is used to group the the iterations, in order to caractrizes the performance of the algorithm.
The document discusses Approximate Bayesian Computation (ABC). ABC allows inference for statistical models where the likelihood function is not available in closed form. ABC works by simulating data under different parameter values and comparing simulated to observed data. ABC has been used for model choice by comparing evidence for different models. Consistency of ABC for model choice depends on the criterion used and asymptotic identifiability of the parameters.
Approximate Bayesian Computation (ABC) provides a framework for Bayesian inference when the likelihood function is intractable or impossible to evaluate directly. ABC produces approximations of posterior distributions by simulating data under different parameter values and accepting simulations that match the observed data closely according to some predefined tolerance level. ABC has been widely used for inference in population genetics models where the genealogical structure relating samples makes the likelihood intractable. While ABC does not provide true Bayesian inference, it can produce inferences that are consistent under certain conditions as the number of simulations increases.
The document discusses Approximate Bayesian Computation (ABC), a computational technique for Bayesian inference when the likelihood function is intractable. ABC allows sampling from the likelihood and making inferences based on simulated data without calculating the actual likelihood. The technique originated in population genetics models where likelihoods for genetic polymorphism data cannot be calculated in closed form. ABC is presented as both an inference machine with its own legitimacy compared to classical Bayesian approaches, as well as a way to address computational issues with intractable likelihoods.
This paper describes using a genetic algorithm to teach a simulated three-legged creature to walk. The creature, called a Tripod, lives in a 3D physics simulation. Its goal is to travel as far as possible within 30 seconds. A genetic algorithm varies the Tripod's joint movements and selects those that perform best, as measured by distance traveled, to be passed on to the next generation. While genetic algorithms are useful for problems like this where the underlying functions are unknown, they are computationally intensive and cannot guarantee optimal solutions. The paper discusses challenges in analyzing the genetic algorithm's convergence and tuning its parameters for this complex, non-deterministic problem domain.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
NEURAL MODEL-APPLYING NETWORK (NEUMAN): A NEW BASIS FOR COMPUTATIONAL COGNITIONaciijournal
NeuMAN represents a new model for computational cognition synthesizing important results across AI,
psychology, and neuroscience. NeuMAN is based on three important ideas: (1) neural mechanisms perform
all requirements for intelligence without symbolic reasoning on finite sets, thus avoiding exponential
matching algorithms; (2) the network reinforces hierarchical abstraction and composition for sensing and
acting; and (3) the network uses learned sequences within contextual frames to make predictions, minimize
reactions to expected events, and increase responsiveness to high-value information. These systems exhibit
both automatic and deliberate processes. NeuMAN accords with a wide variety of findings in neural and
cognitive science and will supersede symbolic reasoning as a foundation for AI and as a model of human
intelligence. It will likely become the principal mechanism for engineering intelligent systems.
The document describes using a genetic algorithm to find the maximum values of single-variable functions. It presents the genetic algorithm process, including representation of solutions, initialization, evaluation, selection, and genetic operators. The algorithm is tested on various continuous and non-continuous functions, like polynomials, rationals, trigonometric, and those with asymptotes. The results show that the genetic algorithm can find the true maximum or one very close within a reasonable number of generations, even for complex multi-modal functions that are difficult to optimize with traditional methods.
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
The document describes using a genetic algorithm to find the maximum values of single-variable functions. It discusses:
1) How genetic algorithms work by simulating biological evolution to optimize solutions.
2) Testing the genetic algorithm on continuous and non-continuous functions that are difficult to optimize with traditional methods, such as multimodal, non-differentiable functions.
3) The genetic algorithm was able to find maximum values close to the real maximum for complex test functions, demonstrating its effectiveness at optimizing these difficult single-variable functions.
Speech Recognition using HMM & GMM Models: A Review on Techniques and Approachesijsrd.com
Many ways of communications are used between human and computer, while using gesture is considered to be one of the most natural ways in a virtual reality system. Speech recognition is one of the typical methods of non-verbal communication for human beings and we naturally use various gestures to express our own intentions in everyday life. Gesture recognizers are supposed to capture and analyze the information transmitted by the hands of a person who communicates in sign language. This is a prerequisite for automatic sign-to-spoken-language translation, which has the potential to support the integration of deaf people into society. This paper present part of literature review on ongoing research and findings on different technique and approaches in gesture recognition using Hidden Markov Models for vision-based approach.
Energy-Based Models with Applications to Speech and Language Processingnxmaosdh232
Energy-based models (EBMs) are an important class of probabilistic models that define a joint probability distribution based on an "energy function". EBMs include undirected graphical models and random fields. The tutorial will cover the basics of EBMs, including learning and inference methods, and applications of EBMs to language modeling, speech recognition, and natural language labeling. It will also discuss upgrading EBMs to handle sequential data and semi-supervised learning tasks.
During the past decade, the size of 3D seismic data volumes and the number of seismic attributes have increased
to the extent that it is difficult, if not impossible, for interpreters to examine every seismic line and time
slice. To address this problem, several seismic facies classification algorithms including k-means, self-organizing
maps, generative topographic mapping, support vector machines, Gaussian mixture models, and artificial neural
networks have been successfully used to extract features of geologic interest from multiple volumes. Although
well documented in the literature, the terminology and complexity of these algorithms may bewilder the average
seismic interpreter, and few papers have applied these competing methods to the same data volume. We have
reviewed six commonly used algorithms and applied them to a single 3D seismic data volume acquired over the
Canterbury Basin, offshore New Zealand, where one of the main objectives was to differentiate the architectural
elements of a turbidite system. Not surprisingly, the most important parameter in this analysis was the choice of
the correct input attributes, which in turn depended on careful pattern recognition by the interpreter. We found
that supervised learning methods provided accurate estimates of the desired seismic facies, whereas unsupervised
learning methods also highlighted features that might otherwise be overlooked.
The document discusses von Neumann entropy in quantum computation. It provides definitions of key terms like von Neumann entropy, density matrix, and computational complexity theory. Von Neumann entropy extends concepts of classical entropy to quantum mechanics and characterizes the classical and quantum information capacities of an ensemble. It quantifies the degree of mixing of a quantum state and how much a state departs from a pure state. The von Neumann entropy of a system is computed using the density matrix and eigendecomposition of the system's quantum state.
This document provides information about genetic algorithms including:
1. Definitions of genetic algorithms from Grefenstette and Goldberg that describe genetic algorithms as search algorithms based on biological evolution and natural selection.
2. An overview of genetic algorithms including the basic concepts of populations, chromosomes, genes, fitness functions, selection, crossover, and mutation.
3. Examples of genetic representations like binary encoding and permutation encoding.
4. Descriptions of genetic operators like selection, crossover, and mutation that maintain genetic diversity between generations.
SWARM INTELLIGENCE FROM NATURAL TO ARTIFICIAL SYSTEMS: ANT COLONY OPTIMIZATIONFransiskeran
This document summarizes research on ant colony optimization (ACO), a metaheuristic algorithm inspired by the foraging behavior of ants. It describes how real ant colonies use pheromone trails to efficiently find short paths between their nest and food sources through decentralized cooperation. The document then explains how ACO works by simulating artificial ants that probabilistically construct solutions and update pheromone values to guide future construction. Several standard ACO algorithms are outlined, including Ant System, Ant Colony System, Max-Min Ant System, and Rank-Based Ant System. Applications of ACO discussed include the traveling salesman problem.
A COMPREHENSIVE SURVEY OF GREY WOLF OPTIMIZER ALGORITHM AND ITS APPLICATIONJaresJournal
This study presents a comprehensive and through summary of the Grey Wolf Optimizer (GWO).The GWO algorithm is a newly-presented meta-heuristic, propelled from the social hunting behavior of grey wolves. The GWO has become a progressively critical device of Swarm Intelligence that has been used in nearly all zones of optimization, and engineering practice. Numerous issues from different regions have been effectively illuminated utilizing the GWO algorithm and its variants. In arrange to utilize the calculation to illuminate assorted issues, the original GWO algorithm required to be modified or hybridized. This study conducts an exhaustive review of this living and advancing area of Swarm Intelligence, so that to show that the GWO algorithm might be connected to each issue emerging in hone. However, it empowers novice researchers and algorithm developers to utilize this straightforward and however exceptionally effective algorithm for issue tackling. It frequently ensures that the gained results about will meet the expectation.
Join us for an enlightening session on AI/ML by Jeevanshi Sharma, an MS graduate from the University of Alberta with accolades from Outreachy'22 and MITACS GRI'21. Delve into cutting-edge advancements, applications, and ethical considerations. Learn basic steps to start your ML journey and explore industry applications, advancements, and associated careers.
Trabajo Ya Publicado presentado en CoSECiVi'14.
Resumen:
Flocking strategies are sets of behavior rules for the interaction of agents that allow to devise controllers with reduced complexity that generate emerging behavior. In this paper, we present an application of genetic algorithms and flocking strategies to control the Ghost Team in the game Ms. Pac-Man. In particular, we define flocking strategies for the Ghost Team and optimize them for robustness with respect to the stochastic elements of the game and effectiveness against different possible opponents by means of genetic algorithm. The performance of the methodology proposed is tested and compared with that of other standard controllers. The results show that flocking strategies are capable of modeling complex behaviors and produce effective and challenging agents.
Swarm intelligence is an artificial intelligence technique inspired by the collective behavior of decentralized and self-organized systems found in nature, such as ant colonies and bird flocks. Two common swarm intelligence algorithms are ant colony optimization and particle swarm optimization. Ant colony optimization is based on the behavior of real ant colonies and can be used to find approximate solutions to difficult optimization problems. Particle swarm optimization is a population-based stochastic optimization technique inspired by swarming behavior in nature, such as bird flocking. It searches for optimal solutions within a problem space by updating the movement of individual particles based on their own experiences and those of neighboring particles.
A Neural Probabilistic Language Model.pptx
Bengio, Yoshua, et al. "A neural probabilistic language model." Journal of machine learning research 3.Feb (2003): 1137-1155.
A goal of statistical language modeling is to learn the joint probability function of sequences of
words in a language. This is intrinsically difficult because of the curse of dimensionality: a word
sequence on which the model will be tested is likely to be different from all the word sequences seen
during training. Traditional but very successful approaches based on n-grams obtain generalization
by concatenating very short overlapping sequences seen in the training set. We propose to fight the
curse of dimensionality by learning a distributed representation for words which allows each
training sentence to inform the model about an exponential number of semantically neighboring
sentences. The model learns simultaneously (1) a distributed representation for each word along
with (2) the probability function for word sequences, expressed in terms of these representations.
Generalization is obtained because a sequence of words that has never been seen before gets high
probability if it is made of words that are similar (in the sense of having a nearby representation) to
words forming an already seen sentence. Training such large models (with millions of parameters)
within a reasonable time is itself a significant challenge. We report on experiments using neural
networks for the probability function, showing on two text corpora that the proposed approach
significantly improves on state-of-the-art n-gram models, and that the proposed approach allows to
take advantage of longer contexts.
Behavior study of entropy in a digital image through an iterative algorithmijscmcj
Image segmentation is a critical step in computer vision tasks constituting an essential issue for pattern recognition and visual interpretation. In this paper, we study the behavior of entropy in digital images through an iterative algorithm of mean shift filtering. The order of a digital image in gray levels is defined. The behavior of Shannon entropy is analyzed and then compared, taking into account the number of iterations of our algorithm, with the maximum entropy that could be achieved under the same order. The use of equivalence classes it induced, which allow us to interpret entropy as a hyper-surface in real m dimensional space. The difference of the maximum entropy of order n and the entropy of the image is used to group the the iterations, in order to caractrizes the performance of the algorithm.
The document discusses Approximate Bayesian Computation (ABC). ABC allows inference for statistical models where the likelihood function is not available in closed form. ABC works by simulating data under different parameter values and comparing simulated to observed data. ABC has been used for model choice by comparing evidence for different models. Consistency of ABC for model choice depends on the criterion used and asymptotic identifiability of the parameters.
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An Analysis of a Selecto-Lamarckian Model of Multimemetic Algorithms with Dynamic Self-Organized Topology
1. An Analysis of a Selecto-Lamarckian Model of
Multimemetic Algorithms with Dynamic
Self-Organized Topology
Rafael Nogueras1
Juan L.J. Laredo3
Carlos Cotta1 Carlos M. Fernandes2
Juan J. Merelo4 Agostinho C. Rosa2
1 Universidad
3 University
de M´laga (Spain), 2 Technical University Lisbon (Portugal),
a
of Luxembourg (Luxembourg), 4 University of Granada (Spain)
TPNC 2013, C´ceres, 3-5 December 2013
a
R. Nogueras et al.
MMAs with Dynamic Self-Organized Topology
2. What are Memes?
Memes are information pieces that constitute units of imitation.
“Examples of memes are tunes, ideas,
catch-phrases, clothes fashions, ways of
making pots or of building arches. Just as
genes propagate themselves in the gene pool
by leaping from body to body via sperms or
eggs, so memes propagate themselves in the
meme pool by leaping from brain to brain via a
process which, in the broad sense, can be
called imitation.”
The Selfish Gene, Richard Dawkins, 1976
R. Nogueras et al.
MMAs with Dynamic Self-Organized Topology
3. What is a Memetic Algorithm?
Memetic Algorithms
A Memetic Algorithm is a population of agents
that alternate periods of self-improvement
with periods of cooperation, and competition.
Pablo Moscato, 1989
Memes can be implicitly defined be the choice of local-search (i.e.,
self-improvement) method, or can be explicitly described in the
agent.
R. Nogueras et al.
MMAs with Dynamic Self-Organized Topology
4. Multimemetic Algoritms (and Memetic Computing)
The term “multimemetic” was coined by N. Krasnogor and J.
Smith (2001). In a MMA, each agent carries a solution and the
meme(s) to improve it.
Evolution works at these two levels, cf. Moscato (1999).
Memetic Computing
A paradigm that uses the notion of meme(s) as units of
information encoded in computational representations for the
purpose of problem solving.
Ong, Lim, Chen, 2010
R. Nogueras et al.
MMAs with Dynamic Self-Organized Topology
5. Scope
Some interesting issues in MMAs:
Memes evolve in MMAs alongside with the solutions they
attach to. It is up to the algorithm to (self-adaptively)
discover good fits between genotypes and memes.
Memes are indirectly assessed via the effect they have on
genotypes.
We consider an analyze an idealized model of MMAs to analyze
meme propagation with dynamic self-organized spatial structures.
R. Nogueras et al.
MMAs with Dynamic Self-Organized Topology
6. Background
Dynamic of meme propagation is more complex than genetic
counterparts.
Genes represent solutions objectively measurable via the
fitness function.
Memes are indirectly evaluated by their effect on solutions.
A first analysis was done by Nogueras and Cotta (2013) with
panmictic and spatially-structured populations. Population
structure is very important to determine the behavior of the
algorithm.
R. Nogueras et al.
MMAs with Dynamic Self-Organized Topology
7. Dynamic Self-Organized Topology
A dynamic model defined by Fernandes et al. (2012) combining
ideas from swarm intelligence and cellular automata is considered
in this study.
Model uses simple rules for movement on a
large 2D-lattice, giving rise to self-organized
clusters of particles.
The clusters evolve and change their shape
with some kind of dynamic order.
We consider how memes propagate in this environment.
R. Nogueras et al.
MMAs with Dynamic Self-Organized Topology
8. Preliminaries
Each agent is a pair g , m ∈ R2 – i.e., gene, meme . The effect
of a meme is captured by a function f : R2 → R, i.e.,
meme application
g , m − − − − − → f (g , m), m
−−−−−
m actually represents the improvement potential of the meme.
lim f n (g , m) = m if g < m
n→∞
f (g , m) = g
if g
m
The population P = [ g1 , m1 , · · · , gµ , mµ ] of the MMA is a
collection of µ such agents.
Agent communication is constrained by a spatial structure,
characterized by a µ × µ Boolean matrix S.
R. Nogueras et al.
MMAs with Dynamic Self-Organized Topology
9. Model Pseudocode
Algorithm 1: Selecto-Lamarckian Model
for i ∈ [1 · · · µ] do
Initialize gi , mi ;
end
while ¬ Converged (P) do
i ← URand(1, µ) // Pick random location
g , m ←Selection(P, S, i);
g ← f (g , m) // Local improvement
P ← Replace(P, S, i, g , m );
end
R. Nogueras et al.
MMAs with Dynamic Self-Organized Topology
10. Concepts
Let G be a grid of size r × s > µ. Each cell Guv of the grid is a
tuple (ηuv , ζuv ), where:
ηuv ∈ {1, · · · , µ} ∪ {•} and ζuv ∈ (D × N) ∪ {•}.
ηuv indicates the index of the individual that occupies position
u, v in the grid.
ζuv is a mark placed by individuals which occupied that
position in the past, where:
f
ζuv is the fitness value of the individual.
t
ζuv is a time stamp.
R. Nogueras et al.
MMAs with Dynamic Self-Organized Topology
11. Individual Movement
The system combine ideas from swarm intelligence and cellular
automata.
R. Nogueras et al.
MMAs with Dynamic Self-Organized Topology
12. Individual Movement
Algorithm 2: Individual Movement (i, t, G )
u, v ← ρ(i); move←true;
f
if exists u (i) , v (i) ∈ N u, v such that ζuv > gi then
f
f
u , v ← arg min{ζuv | ζuv > gi };
else
f
if exists u (i) , v (i) ∈ N u, v such that ζuv < gi then
f
f
u , v ← arg max{ζuv | ζuv < gi };
else
if N u, v = ∅ then
Pick u , v at random from N u, v ;
else
move ← false;
end
end
end
if move then
f
t
ζuv ← gi ; ζuv ← t; // mark old cell
ηuv = •; ηu v = i; // move to new cell
end
R. Nogueras et al.
MMAs with Dynamic Self-Organized Topology
13. Setting
Goal
Explore the dynamics of meme propagation and how it is affected
by factors such as the selection probability, the improvement
potential of memes and the spatial structure of the population.
µ = 256.
pLS ∈ {1/256, 0.1, 0.5, 1.0}.
Spatial structure:
1
2
3
Panmictic: full connectivity with static structure.
Von Neumman neighborhood (r = 1) with static structure.
Moore neighborhood (r = 1) with dynamic structure.
Meme application:
f (g , m) =
g
(g + m)/2
R. Nogueras et al.
if g m
if g < m
MMAs with Dynamic Self-Organized Topology
14. Numerical Simulations
Individual Distribution – Evolution
Individuals start from a random distribution and quickly group in
clusters during the run.
R. Nogueras et al.
MMAs with Dynamic Self-Organized Topology
15. Numerical Simulations
Growth Curves
The number of copies of the dominant meme grows until taking
over the population:
the panmictic model is the first to converge.
the dynamic model is closer to von Neumann model
depending on the value of pS .
R. Nogueras et al.
MMAs with Dynamic Self-Organized Topology
18. Numerical Simulations
Spectral Analysis
The spectrum of the average number of neighbors indicates:
the intensity is proportional to f a for some a < 0.
the spectrum slope is closer to pink noise.
R. Nogueras et al.
MMAs with Dynamic Self-Organized Topology
19. Conclusions
A dynamic model provides promising results in comparison to
unstructured populations and to populations arranged in static
lattices.
By tuning the ratio between self-organization and evolution the
convergence of the algorithm can be adjusted.
Future work:
other topologies and movement policies,
decouple movement for neighborhood and evolutionary
interaction,
full-fledged MMA.
R. Nogueras et al.
MMAs with Dynamic Self-Organized Topology
20. Thank You!
Please find us in Facebook
http://facebook.com/AnySelfProject
and in Twitter
@anyselfproject
AnySelf Project
R. Nogueras et al.
MMAs with Dynamic Self-Organized Topology