Tabu search is a metaheuristic technique that guides a local search procedure to explore the solution space beyond local optimality. It uses flexible memory-based processes to escape the trap of cycling. Particle swarm optimization is a swarm intelligence technique inspired by bird flocking where potential solutions fly through hyperspace to find optimal regions. Ant colony optimization is another swarm intelligence technique inspired by how ants find food, where artificial ants cooperate to find good solutions.
Presentation is about genetic algorithms. Also it includes introduction to soft computing and hard computing. Hope it serves the purpose and be useful for reference.
Guest Lecture about genetic algorithms in the course ECE657: Computational Intelligence/Intelligent Systems Design, Spring 2016, Electrical and Computer Engineering (ECE) Department, University of Waterloo, Canada.
Presentation is about genetic algorithms. Also it includes introduction to soft computing and hard computing. Hope it serves the purpose and be useful for reference.
Guest Lecture about genetic algorithms in the course ECE657: Computational Intelligence/Intelligent Systems Design, Spring 2016, Electrical and Computer Engineering (ECE) Department, University of Waterloo, Canada.
A presentation on PSO with videos and animations to illustrate the concept. The ppt throws light on the concept, the algo, the application and comparison of PSO with GA and DE.
This presentation provides an introduction to the Particle Swarm Optimization topic, it shows the PSO basic idea, PSO parameters, advantages, limitations and the related applications.
The GENETIC ALGORITHM is a model of machine learning which derives its behavior from a metaphor of the processes of EVOLUTION in nature. Genetic Algorithm (GA) is a search heuristic that mimics the process of natural selection. This heuristic (also sometimes called a metaheuristic) is routinely used to generate useful solutions to optimization and search problems.
Genetic Algorithm (GA) is a search-based optimization technique based on the principles of Genetics and Natural Selection. It is frequently used to find optimal or near-optimal solutions to difficult problems which otherwise would take a lifetime to solve. It is frequently used to solve optimization problems, in research, and in machine learning.
This Presentation were Made By BugsBusters team from faculty of Computers and information, Helwan University - Egypt
IMPORTANT NOTE !!!
Do not view this online or it will not be compatible Download it to view videos and see original slides :))
SA is a global optimization technique.
It distinguishes between different local optima.
It is a memory less algorithm & the algorithm does not use any information gathered during the search.
SA is motivated by an analogy to annealing in solids.
& it is an iterative improvement algorithm.
A presentation on PSO with videos and animations to illustrate the concept. The ppt throws light on the concept, the algo, the application and comparison of PSO with GA and DE.
This presentation provides an introduction to the Particle Swarm Optimization topic, it shows the PSO basic idea, PSO parameters, advantages, limitations and the related applications.
The GENETIC ALGORITHM is a model of machine learning which derives its behavior from a metaphor of the processes of EVOLUTION in nature. Genetic Algorithm (GA) is a search heuristic that mimics the process of natural selection. This heuristic (also sometimes called a metaheuristic) is routinely used to generate useful solutions to optimization and search problems.
Genetic Algorithm (GA) is a search-based optimization technique based on the principles of Genetics and Natural Selection. It is frequently used to find optimal or near-optimal solutions to difficult problems which otherwise would take a lifetime to solve. It is frequently used to solve optimization problems, in research, and in machine learning.
This Presentation were Made By BugsBusters team from faculty of Computers and information, Helwan University - Egypt
IMPORTANT NOTE !!!
Do not view this online or it will not be compatible Download it to view videos and see original slides :))
SA is a global optimization technique.
It distinguishes between different local optima.
It is a memory less algorithm & the algorithm does not use any information gathered during the search.
SA is motivated by an analogy to annealing in solids.
& it is an iterative improvement algorithm.
A large number of queries are been posed daily on databases spread across the globe. In order for processing these queries efficiently, the best strategies to generate plans are being devised. In distributed relational database systems, due to replica of relations at different sites, the relations required to answer a query might necessitate accessing of data from many different sites. This leads in exponential increase in the number of possible alternative query plans to process a query. Though it is not computationally feasible for exploring all possible query plans in such a vast search space, the query plan that provides the most cost-effective option for query processing is considered to be necessary and should be generated for a given query. Here in this project of ours, an effort has been made to give best possible query plans using Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and other soft computing techniques. Experimental comparisons of this algorithm with the GA based distributed query plan generation algorithm and it proves that for more number of relations, PSO based algorithm is able to generate better query plans.
In this Machine Learning tutorial, we will cover the top Neural Network Algorithms. These algorithms are used to train the Artificial Neural Network. This blog provides you a deep learning of the Gradient Descent, Evolutionary Algorithms, and Genetic Algorithm in Neural Network.
Mobile Recommendation Engine
collaborative filtering and content based approach in hybrid manner then Genetic Algorithm for Enhancement of the Recommendation Engine. by this marketers also will get the unique characteristics of the product that must be created and also recommend to the user.
Artificial Intelligence in Robot Path Planningiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Deep vs diverse architectures for classification problemsColleen Farrelly
Deep learning study, comparing deep learning methods with wide learning methods; applications include simulation data and real industry problems. Pre-print of paper found here: https://arxiv.org/ftp/arxiv/papers/1708/1708.06347.pdf
How to Win Machine Learning Competitions ? HackerEarth
This presentation was given by Marios Michailidis (a.k.a Kazanova), Current Kaggle Rank #3 to help community learn machine learning better. It comprises of useful ML tips and techniques to perform better in machine learning competitions. Read the full blog: http://blog.hackerearth.com/winning-tips-machine-learning-competitions-kazanova-current-kaggle-3
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
2. What is ?
In simple words:
• It is a repetitive process used to solve a
problem that even the toughest algorithm,
with greatest optimization powers, fail to be
effective or efficient.
3.
4. Artificial Neural Networks
• Most Basic Technique of AI-“ANN (Artificial neural
Networks)” , based on Human nervous system
• Processes information with the help of
– nodes
– synapses(inputs)
– weights(signals)
– mathematical formulas (calculating neuron output signal,
scalar product i.e. net ).
•Neuron Communicating through
input, hidden and output layers
and weighted connections
(feedforward, feedback, lateral and
time-delayed).
5. ANN continued....
• An ANN problems is solved through
• Training
• Generalization (Testing)
• Implementation.
• ANN is applied in Robotics, Data Processing, Functional
approximation and pattern sequence recognition.
• Disadvantage is that because the network finds out how to solve
the problem by itself and its operation can be unpredictable
6. Tabu Search (TS)
• Taking a solution as starting point (or local minima), uses a
Tabu list for recent moves and a Tabu memory to prevent it
from repeating, making a “Solution space - Diversification”.
When we need better version of better
Initialize
solution
Populate
candidate
list of
solutions
Evaluate
solutions
Choose
best
admissible
solution
Stopping
conditions
satisfied?
Update
aspiration
conditions
Update
tabu Final
soution
yes
no
7. Tabu search
• Many factors play a role like size, adaptability of TS memory and
list, local search procedure, form of moves and stopping rule.
8. Evolutionary Algorithm
• When we have a bundle of solutions and we know not to give up, we
use Evolutionary Algorithm
• Methods like Genetic programming, Evolutionary strategies, Genetic
Algorithms are used.
• Unlike TS it has many “local optimas”.
• EAs are used in like wire routing, scheduling, image processing, game
playing, Knapsack problem, etc.
mixing and matching the best part of each solution
9. Genetic Algorithm
• GA is an adaptive search and optimization using
random searches to find “local optimal
solutions”, so as to safeguard some critical info.
•Mutate, then select
the fittest solution and
repeat until the best
one is found.
• Popular for
Bankruptcy prediction,
residual estimation,
vehicle routing, etc.
10. Differential Evolution
• It makes a trial vectors using existing solutions and
mixes it with successful ones, further improved by
mutation, crossover and selection operators.
• DE Algorithm:
Advanced Version of GA that focuses on Mutation
• Best for numerical problems, used to find
approximate solutions where problems are non
linear, non-differentiable with many local minima and
constraints
11. Simulated Annealing
• A worse variation is accepted as the new solution with a
probability that decreases as the computation proceeds.
• The slower the cooling schedule, or rate of decrease, the
more likely the algorithm is to find an optimal or near-
optimal solution
• It is Useful in zoning, routing, facility layout problems.
When in need to find random variations of a present
solution, accepting the worst one
12. Swarm Intelligence
• Swarm Intelligence (SI), follows 5 principles:
proximity, quality, diverse response, stability
and adaptability.
Inspired from insects and their coordinated interactive
teamwork
13. Particle Swarm Optimization (PSO)
• Particle Swarm Optimization (PSO) is based
on population on concept of bird flocking.
• It is easy parallelization for concurrent
processing, derivative free and solve
convergence is very effective.
• Implemented in Parkinson’s disease
identification, electric power distribution,
biometrics, processing biochemistry, etc
14. Ant colony Optimization (ACO)
• Ant colony Optimization (ACO) is inspired by foraging and
colonization of ants.
• It includes trailing like ants, making progressive solutions,
using attractiveness and trail levels.
• ACO algorithms like Ant system, Ant colony system, ma-
min ant system, rank based ant system and best-worst
ant system are summarised.
• In Ant System, the contributions by ants depends
on quality of solution and better the trail contribution,
better the solution.
15. Ant colony Optimization (ACO)…
• To improve the algorithmic quality, performance and
behaviour ACS, enhanced AS through pseudo-random
proportion rule, updating pheromone trail offline through
daemon, hence not every ant follows the same ant.
• MMASas best enhancement of AS, ranking ant in decreasing
order of respective solutions.
• Pheromone which restricts them was influenced by rand and
quality of solution, so was the connection.
• BWAS using the transition rule and Pheromone
evaporation techniques, was also a good extension to
AS.
• ACO was largely useful in assignment, scheduling,
vehicle routing, travelling salesman and energy
forecasting
16. FUTURE RESEARCH DIRECTIONS
• However useful these techniques were, they did not guarantee an
optimal solution, overhead being , complex function , parameters and
constraints, also lack of standards of testing and comparison of
methods makes it rather in need for improvement.
• As in latest 5 years, Metaheuristic have gained importance through
textbooks, conferences, success in application many real-world
problems.
• ANNs and GAs softwares being available are more popular.
• As the better optimal solution are much needed for the scarcity of
time, money and resources, so will just keep on increasing in near
future .Thus the number of methods for Metaheuristic.
• All we need to do is, develop softwares for all such useful methods to
be better applied in the real-world.
Editor's Notes
heuristic is a technique designed for finding an approximate solution when classic methods fail to find any exact solution.
Metaheuristics; learning strategies are used to structure information in order to find efficiently near-optimal solutions.
These basically consist of inputs (like synapses), which are multiplied by weights (strength of the respective signals), and then computed by a mathematical function which determines the activation of the neuron .
There are three types of neuron layers: input, hidden and output layers. Two layers of neuron communicate via a weight conection network. There are four types of weighted connections: feedforward, feedback, lateral, and time-delayed connections.
lateral - winners-takes-all circuit, which serves the important role of selecting the winner .
time-delayed)- more suitable for temporal pattern recognitions.
disadvantage ;is that because the network finds out how to solve the problem by itself and its operation can be unpredictable
"that restaurant's menu lacks diversification; every day it is the same
they involve a search from a “population” of solutions, not from a single point.
Mutate; change.
Bankruptcy; Someone who has insufficient assets to cover their debts. failure
Residual; indicating a remainder
Annealing; Hardening something by heat treatment.
Simulated;Reproduce someone's behavior or looks.
algorithm is a technique to find a good solution of an optimization problem using a random variation of the current solution. A worse variation is accepted as the new solution with a probability that decreases as the computation proceeds. The slower the cooling schedule, or rate of decrease, the more likely the algorithm is to find an optimal or near-optimal solution