#GeneticAlgorithm

[damilola-doodles]

📌Project Title: Massive-Scale Public Transport Route Optimization using Network Analysis and Genetic Algorithms.🔴

ai-ml-ds-operations-research-transport-optimization-014 Filename: public_transport_route_optimization_ga.py Timestamp: Mon Jun 02 2025 19:31:21 GMT+0000 (Coordinated Universal Time) Problem Domain:Urban Planning, Transportation Engineering, Operations Research, Network Science, Optimization, Evolutionary Computation. Project Description:This project tackles the complex problem of optimizing…

[dammyanimation]

📌Project Title: Massive-Scale Public Transport Route Optimization using Network Analysis and Genetic Algorithms.🔴

ai-ml-ds-operations-research-transport-optimization-014 Filename: public_transport_route_optimization_ga.py Timestamp: Mon Jun 02 2025 19:31:21 GMT+0000 (Coordinated Universal Time) Problem Domain:Urban Planning, Transportation Engineering, Operations Research, Network Science, Optimization, Evolutionary Computation. Project Description:This project tackles the complex problem of optimizing…

[damilola-ai-automation]

📌Project Title: Massive-Scale Public Transport Route Optimization using Network Analysis and Genetic Algorithms.🔴

ai-ml-ds-operations-research-transport-optimization-014 Filename: public_transport_route_optimization_ga.py Timestamp: Mon Jun 02 2025 19:31:21 GMT+0000 (Coordinated Universal Time) Problem Domain:Urban Planning, Transportation Engineering, Operations Research, Network Science, Optimization, Evolutionary Computation. Project Description:This project tackles the complex problem of optimizing…

[damilola-warrior-mindset]

📌Project Title: Massive-Scale Public Transport Route Optimization using Network Analysis and Genetic Algorithms.🔴

ai-ml-ds-operations-research-transport-optimization-014 Filename: public_transport_route_optimization_ga.py Timestamp: Mon Jun 02 2025 19:31:21 GMT+0000 (Coordinated Universal Time) Problem Domain:Urban Planning, Transportation Engineering, Operations Research, Network Science, Optimization, Evolutionary Computation. Project Description:This project tackles the complex problem of optimizing…

[damilola-moyo]

📌Project Title: Massive-Scale Public Transport Route Optimization using Network Analysis and Genetic Algorithms.🔴

ai-ml-ds-operations-research-transport-optimization-014 Filename: public_transport_route_optimization_ga.py Timestamp: Mon Jun 02 2025 19:31:21 GMT+0000 (Coordinated Universal Time) Problem Domain:Urban Planning, Transportation Engineering, Operations Research, Network Science, Optimization, Evolutionary Computation. Project Description:This project tackles the complex problem of optimizing…

[govindhtech]

Intel Distribution For Python To Create A Genetic Algorithm

Python Genetic Algorithm

Genetic algorithms (GA) simulate natural selection to solve finite and unconstrained optimization problems. Traditional methods take time and resources to address NP-hard optimization problems, but these algorithms can do it. GAs are based on a comparison between human chromosomal behavior and biological evolution.

This article provides a code example of how to use numba-dpex for Intel Distribution for Python to create a generic GA and offload a calculation to a GPU.

Genetic Algorithms (GA)

Activities inside GAs

Selection, crossover, and mutation are three crucial biology-inspired procedures that may be used to provide a high-quality output for GAs. It’s critical to specify the chromosomal representation and the GA procedures before applying GAs to a particular issue.

Selection

This is the procedure for choosing a partner and recombining them to produce children. Because excellent parents encourage their children to find better and more appropriate answers, parent selection is critical to the convergence rate of GA.

An illustration of the selection procedure whereby the following generation’s chromosomes are reduced by half.

The extra algorithms that decide which chromosomes will become parents are often required for the selection procedure.

Crossover

Biological crossover is the same procedure as this one. In this case, more than one parent is chosen, and the genetic material of the parents is used to make one or more children.

A crossover operation in action.

The crossover procedure produces kid genomes from specific parent chromosomes. There is only one kid genome produced and it may be a one-point crossing. The first and second parents each give the kid half of their DNA.

Mutation

A novel answer may be obtained by a little, haphazard modification to the chromosome. It is often administered with little probability and is used to preserve and add variation to the genetic population.

A mutation procedure involving a single chromosomal value change.

The mutation procedure may alter a chromosome.

Enhance Genetic Algorithms for Python Using Intel Distribution

With libraries like Intel oneAPI Data Analytics Library (oneDAL) and Intel oneAPI Math Kernel Library (oneMKL), developers may use Intel Distribution for Python to obtain near-native code performance. With improved NumPy, SciPy, and Numba, researchers and developers can expand compute-intensive Python applications from laptops to powerful servers.

Use the Data Parallel Extension for Numba (numba-dpex) range kernel to optimize the genetic algorithm using the Intel Distribution for Python. Each work item in this kernel represents a logical thread of execution, and it represents the most basic kind of data-parallel and parallelism across a group of work items.

The vector-add operation was carried out on a GPU in the prior code, and vector c held the result. In a similar vein, the implementation is the same for every other function or method.

Code Execution

Refer to the code sample for instructions on how to develop the generic GA and optimize the method to operate on GPUs using numba-dpex for Intel Distribution for Python. It also describes how to use the various GA operations selection, crossover, and mutation and how to modify these techniques for use in solving other optimization issues.

Set the following values to initialize the population:

5,000 people live there.

Size of a chromosome: 10

Generations: 5.

There are ten random floats between 0 and 1 on each chromosome.

Put the GA into practice by developing an assessment strategy: This function serves as numba-dpex’s benchmark and point of comparison. The calculation of an individual’s fitness involves using any combination of algebraic operations on the chromosome.

Carry out the crossover operation: The inputs are first and second parents to two distinct chromosomes. One more chromosome is returned as the function’s output.

Carry out the mutation operation: There is a one percent probability that every float in the chromosome will be replaced by a random value in this code example.

Put into practice the selection process, which is the foundation for producing a new generation. After crossover and mutation procedures, a new population is generated inside this function.

Launch the prepared functions on a CPU, beginning with a baseline. Every generation includes the following processes to establish the first population:

Utilizing the eval_genomes_plain function, the current population is evaluated

Utilizing a next_generation function, create the next generation.

Wipe fitness standards, since a new generation has already been produced.

Measured and printed is the calculation time for those operations. To demonstrate that the calculations were the same on the CPU and GPU, the first chromosome is also displayed.

Run on a GPU: Create an evaluation function for the GPU after beginning with a fresh population initialization (similar to step 2). With GPU implementation, chromosomes are represented by a flattened data structure, which is the sole difference between it and CPU implementation. Also, utilize a global index and kernels from numba-dpex to avoid looping over every chromosome.

The time for assessment, generation production, and fitness wipe is monitored when a GPU is operating, just like it is for the CPU. Deliver the fitness container and all of the chromosomes to the selected device. After that, a kernel with a specified range may be used.

Conclusion

Use the same procedures for further optimization issues. Describe the procedures of chromosomal selection, crossing, mutation, and assessment. The algorithm is executed the same way in its entirety.

Execute the above code sample and evaluate how well this method performs while executing sequentially on a CPU and parallelly on a GPU. The code result shows that using a GPU-based numba-dpex parallel implementation improves performance speed.

Read more on Govindhtech.com

[luxlaff]

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It covers cutting-edge machine learning algorithms like neural networks, convolutional networks, genetic algorithms, and reinforcement learning. This is a real trading revolution!

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Of course, there are risks like model overfitting and security concerns. But when done right, the potential of AI in crypto trading is limitless.

So hit that link, dive into this topic, and join the vanguard of future traders! Artificial intelligence is game-changing, and we need to be ready. 💥🔜

[guillaumelauzier]

Generative Art: The Algorithmic Touch

Generative art represents an exciting merger of creativity and computation, where algorithms replace the brush, shaping intricate patterns and complex systems that transcend the bounds of traditional art forms. This unique form of art has a variety of methodologies at its disposal, from fractals to neural networks. This article explores these key algorithms and techniques that contribute to the world of generative art. Fractals A fractal is a self-similar shape that repeats infinitely at every level of magnification. The enchanting visuals they generate have long been a fascination for mathematicians and artists alike. Classic fractal types, like the Mandelbrot and Julia sets, exhibit stunningly intricate patterns and infinite complexity. More advanced constructs like 3D fractals or Mandelbulbs extend the concept to create highly complex three-dimensional works of art. Fractal-based algorithms leverage the recursive nature of fractals, making them an ideal tool in the generative artist's toolbox. Cellular Automata Cellular Automata (CA) is a discrete model studied in computational theory. A well-known example, Conway's Game of Life, consists of a grid of cells that evolve through discrete time steps according to a set of simple rules based on the states of neighboring cells. Through these rules, even from simple initial conditions, CAs can produce complex, dynamic patterns, providing a rich foundation for generative art. Noise Functions Noise functions such as Perlin noise or Simplex noise create organic, smoothly varying randomness. These algorithms generate visually appealing patterns and textures, often forming the basis for more complex generative pieces. From naturalistic textures to lifelike terrains, the randomness introduced by noise functions can mimic the randomness in nature, providing a sense of familiarity within the generated art. Genetic Algorithms Genetic algorithms (GAs) are based on the process of natural selection, with each image in a population gradually evolved over time. A fitness function, which quantifies aesthetic value, guides the evolutionary process. Over several generations, this method results in images that optimize for the defined aesthetic criteria, creating a kind of survival of the fittest, but for art. Lindenmayer Systems Lindenmayer Systems (L-systems) are a type of formal grammar primarily used to model the growth processes of plant development but can also generate complex, branching patterns that imitate those found in nature. This makes L-systems a powerful tool for generative art, capable of producing intricate, natural-looking designs. Neural Networks The advent of deep learning has opened up exciting new possibilities for generative art. Neural networks, such as Generative Adversarial Networks (GANs) and Convolutional Neural Networks (CNNs), are trained on a dataset of images to generate new images that bear stylistic similarities. Variations of these networks like StyleGAN, DCGAN, and CycleGAN have been instrumental in creating a broad range of generative artworks, from mimicking famous painters to creating entirely new, AI-driven art styles. Physics-Based Algorithms Some generative artists turn to physics to inspire their work. These algorithms use models of natural processes, like fluid dynamics, particle systems, or reaction-diffusion systems, to create pieces that feel dynamic and organic. Physics-based algorithms can produce stunningly realistic or fantastically abstract images, often emulating the beautiful complexity of nature. Swarm Intelligence Swarm Intelligence algorithms, like particle swarm optimization or flocking algorithms, simulate the behavior of groups of organisms to generate art based on their movement patterns. The collective behavior of a swarm, resulting from the local interactions between the agents, often leads to intricate, evolving patterns, creating compelling pieces of art. Shape Grammars Shape grammars constitute a method that starts with a base shape and then iteratively applies a set of transformation rules to create more complex forms. This systematic approach of shape manipulation allows artists to produce intricate designs that can evolve in visually surprising ways, offering a versatile tool for generative art creation. Agent-Based Models Agent-Based Models (ABMs) simulate the actions and interactions of autonomous entities, allowing artists to investigate their effects on the system as a whole. Each agent follows a set of simple rules, and through their interactions, complex patterns and structures emerge. These emergent phenomena give rise to a range of visually captivating outcomes in the realm of generative art. Wave Function Collapse Algorithm The Wave Function Collapse Algorithm is a procedural generation algorithm that generates images by arranging a collection of tiles according to a set of constraints. This algorithm has seen wide use in generative art and game development for creating coherent and interesting patterns or complete scenes based on a given input. Chaos Theory Chaos theory studies the behavior of dynamical systems that are highly sensitive to initial conditions. Art based on strange attractors, bifurcation diagrams, and other concepts from chaos theory can generate intricate and unpredictable patterns. These patterns, although deterministic, appear random and complex, making chaos theory a fascinating contributor to generative art. Ray Marching Ray Marching, a technique used in 3D computer graphics, is a method of rendering a 3D scene via virtual light beams, or rays. It allows for the creation of sophisticated lighting effects and complex geometric shapes that might not be possible with traditional rendering techniques. Its flexibility and power make it an attractive option for generative artists working in three dimensions.

Conclusion

The intersection of art and algorithms in the form of generative art enables artists to explore new modes of creation and expression. From fractals to neural networks, a variety of techniques provide generative artists with powerful tools to create visually captivating and complex art forms. The dynamic and emergent nature of these algorithms results in artworks that are not just static images but evolving entities with life and movement of their own. Read the full article

[newscientistsawards]

Multi-objective optimization of inlet and outlet in a circular culture fish tank using the GBDT-NSGAII algorithm and computational fluid dynamic.

New Scientists Awards

Nomination Link: https://newscientists.net/award-nomination/?ecategory=Awards&rcategory=Awardee

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For Enquiry: support@newscientists.net

#sciencefather #scientificexcellence #scientist #PublicHealthResearcher #Anthropologist #Ecologist #Geologist #MaterialsScientist #VisitingResearcher #ScienceCommunicator #RegulatoryAffairsSpecialist #ResearchFellow #MultiObjectiveOptimization #CFDAnalysis #GBDT #NSGAII #ComputationalFluidDynamics #AquacultureEngineering #GeneticAlgorithm #Hydrodynamics #AIOptimization #SustainableAquaculture

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[lovelypol]

AI + Semiconductor Manufacturing = A $9.8B Market by 2034! 🚀

AI for Semiconductor Manufacturing Market is revolutionizing chip production by integrating artificial intelligence to enhance precision, efficiency, and yield. AI-driven solutions such as predictive maintenance, quality control, process optimization, and supply chain management are pivotal in addressing the growing demand for advanced semiconductor devices.

To Request Sample Report : https://www.globalinsightservices.com/request-sample/?id=GIS10040 &utm_source=SnehaPatil&utm_medium=Article

The market is experiencing strong growth, driven by advancements in automation and precision manufacturing. Machine learning dominates with a 45% market share, optimizing production efficiency and yield. Predictive maintenance ranks second, reducing downtime and operational costs through real-time analytics. AI-powered quality control ensures defect-free production, elevating semiconductor standards.

North America leads due to robust technological infrastructure and R&D investments, followed closely by Asia-Pacific, with China and South Korea driving growth through AI-driven manufacturing initiatives. Europe also sees expansion, fueled by semiconductor demand in automotive and industrial sectors.

The industry is forecasted to process 320 million chips in 2024, with an expected surge to 550 million by 2028. Major players like IBM, NVIDIA, and Intel lead AI-driven semiconductor innovations.

#ai #semiconductors #machinelearning #deeptech #computervision #predictivemaintenance #qualitycontrol #waferfabrication #neuralnetworks #automation #datadriven #chipmanufacturing #foundries #nanotechnology #electronics #smartmanufacturing #deeplearning #aioptimization #geneticalgorithms #fuzzylogic #lithography #etching #deposition #semiconductorindustry #chipdesign #artificialintelligence #cloudcomputing #digitaltransformation #iot #bigdata #edgeai #manufacturingai #supplychainoptimization #processautomation #industrialai #automotivechips #advancedanalytics #smartfactories #integratedcircuits #hardwareacceleration #aihardware #aichips

[mansijadav]

Dynamic Mechanical Metamaterials #sciencefather #materialsscience #sci...

Mechanical vibration isolation for variable payloads has long been a complex challenge in engineering. This groundbreaking research employs a genetic algorithm and machine learning techniques to design quasi-zero stiffness (QZS) metamaterials tailored for predetermined payloads. Using multi-material 3D printing, these metamaterials are fabricated as a single integrated unit, showcasing adaptability and precision.

website: https://popularscientist.com/

#MechanicalEngineering #Metamaterials #VibrationIsolation #GeneticAlgorithms #MachineLearning #AdaptiveDesign #3DPrinting #QuasiZeroStiffness #PayloadOptimization #DynamicTesting

[francescolelli]

On Genetic Algorithms as an Optimization Technique for Neural Networks

This is a short preview of the article: Genetic algorithms are an optimization technique inspired by the process of natural selection and genetics. In computer science, they are used to solve complex problems and find optimal solutions by mimicking the principles of evolution. A population of potential solutions is created and evolves

If you like it consider checking out the full version of the post at: On Genetic Algorithms as an Optimization Technique for Neural Networks

If you are looking for ideas for tweet or re-blog this post you may want to consider the following hashtags:

Hashtags: #ArtificialIntelligence, #Automation, #ComputerScience, #GeneticAlgorithms, #MachineLearning, #ML, #NeuralNetworks, #NN, #Optimization

The Hashtags of the Categories are: #MachineLearning, #Programming

On Genetic Algorithms as an Optimization Technique for Neural Networks is available at the following link: https://francescolelli.info/machine-learning/on-genetic-algorithms-as-an-optimization-technique-for-neural-networks/ You will find more information, stories, examples, data, opinions and scientific papers as part of a collection of articles about Information Management, Computer Science, Economics, Finance and More.

The title of the full article is: On Genetic Algorithms as an Optimization Technique for Neural Networks

It belong to the following categories: Machine Learning, Programming

The most relevant keywords are: Artificial Intelligence, automation, computer science, genetic algorithms, machine learning, ML, Neural Networks, NN, optimization

It has been published by Francesco Lelli at Francesco Lelli a blog about Information Management, Computer Science, Finance, Economics and nearby ideas and opinions

Genetic algorithms are an optimization technique inspired by the process of natural selection and genetics. In computer science, they are used to solve complex problems and find optimal solutions by mimicking the principles of evolution. A population of potential solutions is created and evolves

Hope you will find it interesting and that it will help you in your journey

Genetic algorithms are an optimization technique inspired by the process of natural selection and genetics. In computer science, they are used to solve complex problems and find optimal solutions by mimicking the principles of evolution. A population of potential solutions is created and evolves over generations through the application of genetic operators such as selection,…

[beardysoul]

Neuroevolution

The concept of neuroevolution is to use a genetic algorithm in order to produce a suitably evolved neural network to solve a problem.

Normally, neural networks use a system of training in which the network is trained through an algorithm which compares its “error” with the the desired output, and adjusts its internal weights slightly to reduce the error. Neuroevolution uses a system in which we create many neural networks (a “population” of neural networks), then apply Darwinian theory to “mutate” and “evolve” the internal weights of each member of the population based on the “fitness” of the network for the next generation, until a suitable neural network is evolved.

The example MARI/O demonstrates a complex version of a neuroevolution algorithm in which a neural network is created to play through a level of Super Mario World. This is using the NEAT (Neuroevolution of augmenting topologies) algorithm which not only adjusts the internal weights of the neural network, but adds neurons when necessary.

Daniel Shiffman’s series on neuroevolution provides an accessible (to those with a basic understandig of programming) introduction to neuroevolution and the types of situations it can be applied to.

[incegna]

Latent semantic indexing is a mathematical technique to extract information from unstructured data. It is based on the principle that words used in the same context carry the same meaning. Check our Info : www.incegna.com Reg Link for Programs : http://www.incegna.com/contact-us Follow us on Facebook : www.facebook.com/INCEGNA/? Follow us on Instagram : https://www.instagram.com/_incegna/ For Queries : enquiry@incegna.com #nlp,#semanticindexing,#semanticanalysis,#unstructureddata,#Decryptingciphers,#spamdetection,#geneticalgorithm,#naturallanguageprocessing,#nltk,#matplotlib,#pandas,#numpy,#SciKit,#scipy https://www.instagram.com/p/B9YZeubgCPj/?igshid=ktsmuvxjmz6e

[ijtsrd]

Identification of Disease in Leaves using Genetic Algorithm

by K. Beulah Suganthy "Identification of Disease in Leaves using Genetic Algorithm" 

Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, 

URL: https://www.ijtsrd.com/papers/ijtsrd22901.pdf

Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/22901/identification-of-disease-in-leaves-using-genetic-algorithm/k-beulah-suganthy

paper publication for engineering, engineering journal, ugc approved journals for engineering

Plant disease is an impairment of normal state of a plant that interrupts or modifies its vital functions. Many leaf diseases are caused by pathogens. Agriculture is the mains try of the Indian economy. Perception of human eye is not so much stronger so as to observe minute variation in the infected part of leaf. In this paper, we are providing software solution to automatically detect and classify plant leaf diseases. In this we are using image processing techniques to classify diseases and quickly diagnosis can be carried out as per disease. This approach will enhance productivity of crops. It includes image processing techniques starting from image acquisition, preprocessing, testing, and training. 

[procedural-generation]

Evolving Ecosystem with Genetic Algorithm (Ver. 2)

Inspired by Daniel Shiffman, Tom Uchida made this ecosystem simulator.

I am, of course, a total sucker for artificial life sims: Boids, Sugarscape, Game of Life, and so on.

What’s particularly interesting about this one is that it builds on several different techniques--flocking, genetic algorithms--to create a complete system. None of the underlying algorithms are new: they don’t need to be.

I’d love to see a ton more projects like this, that take procgen techniques and use them as building blocks. There are, of course, quite a few out there already, but there’s no shortage of opportunity.

Take an algorithm, make it yours, and then tell me about what you made!

More here: https://tomo-masa.tumblr.com/post/156971382343/evolving-ecosystem-with-genetic-algorithm