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# Genetic Algorithm - Science topic

Genetic Algorithm are solving problems in maths by optimization technique using GA
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Optimization by genetic algorithm (GAO) in electricity and smart grid?
why we use it ?
what is the process of this algorithms ?
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I am using a genetic algorithm to solve a multivariable optimization problem. The difficulty in exploring all the solutions is that the permissible set of each variable of the solution is of the form {0} U [a,b], where 0 < a < b (the magnitudes are around a=4 and b=15). "Solutions" that do not satisfy the constraints get a low fitness. So when the genetic explores the search space, it is difficult that it tries solutions with one variable at 0 (zero). I can try to enlarge the interval around 0 and to modify the fitness of variables close to zero. Does anybody know how to treat this kind of constraints? By the way I am using the DEAP genetic algorithms, more precisely this one: http://deap.gel.ulaval.ca/doc/default/examples/ga_onemax.html.
To optimize a multivariable problem with constraints of the form `{0} ∪ [a, b]` using a genetic algorithm, consider implementing custom mutation and crossover functions that increase the likelihood of variables taking on a value of zero, and adjust the fitness function to impose penalties for values within the non-desirable range `(0, a)`. You can also employ a two-part variable representation to handle zeros and non-zeros distinctly, initialize the population with a higher frequency of zeros, or use adaptive fitness scaling to dynamically value solutions with zeros more favorably. Lastly, specialized selection mechanisms can be designed to favor individuals that meet the constraints, ensuring the genetic algorithm efficiently explores and exploits the desired search space within the DEAP framework.
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I constantly use genetic algorithm and neural network , if you know and examine a better method to find when the data is high dimensional .
Dealing with high-dimensional data, especially when using genetic algorithms and neural networks, can be challenging due to the "curse of dimensionality." High dimensionality can lead to increased computational complexity, overfitting, and reduced model generalization.
I can suggest some methods and strategies you may utilize when working with high-dimensional data:
For Genetic Algorithms:
1. Feature Selection: High-dimensional data often contains many irrelevant or redundant features. Implement feature selection techniques, such as genetic algorithms themselves or recursive feature elimination, to reduce the dimensionality by selecting the most informative features.
2. Dimensionality Reduction: Consider dimensionality reduction techniques like Principal Component Analysis (PCA) or t-distributed Stochastic Neighbor Embedding (t-SNE) to project high-dimensional data into a lower-dimensional space while preserving important information.
3. Crossover and Mutation Operators: Design or adapt crossover and mutation operators that are well-suited for high-dimensional data. These operators should encourage exploration of the solution space while avoiding excessive computational cost.
4. Population Size: In high-dimensional spaces, increasing the population size can help improve the exploration of the search space. However, be cautious of computational resources.
5. Constraint Handling: Implement constraint handling mechanisms to ensure that generated solutions remain feasible in high-dimensional spaces. This prevents invalid solutions that could arise due to the sheer number of dimensions.
For Neural Networks:
1. Regularization: Use regularization techniques like L1 or L2 regularization to prevent overfitting. These techniques encourage neural networks to focus on a subset of important features.
2. Dropout: Implement dropout layers within the neural network to randomly deactivate a portion of neurons during training. This helps prevent overfitting and can be especially useful in high-dimensional scenarios.
3. Batch Normalization: Batch normalization can stabilize training in deep networks and make them more resistant to vanishing/exploding gradients, which can occur in high-dimensional networks.
4. Architectural Choices: Consider network architectures that are designed for high-dimensional data, such as deep convolutional networks for image data or recurrent networks for sequential data.
5. Early Stopping: Employ early stopping techniques to monitor the network's performance on validation data and halt training when it starts to overfit.
6. Ensemble Learning: Use ensemble learning methods like bagging or boosting with multiple neural networks to improve performance and reduce the impact of overfitting.
7. Reduced Learning Rates: Experiment with reduced learning rates or learning rate schedules to facilitate convergence in high-dimensional spaces.
8. Data Preprocessing: Apply data preprocessing techniques like feature scaling and normalization to make high-dimensional data more amenable to neural network training.
9. Autoencoders: Consider using autoencoders to learn lower-dimensional representations of high-dimensional data before feeding it into a neural network. Autoencoders can capture the most essential features.
10. Transfer Learning: Transfer learning, using pre-trained models, can be effective for high-dimensional data. Fine-tune models that have been trained on large datasets related to your problem.
When working with high-dimensional data, it's crucial to experiment with various combinations of the above techniques to find the best approach for your specific problem. Additionally, you may need to consider parallel computing or distributed computing to handle the increased computational requirements that can arise in high-dimensional spaces.
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I have a thesis right now, I don't know if its possible to do, I'm trying to create a website builder, but instead of using Draggable Pre-Templates or Libraries, I would make a UI Component Generator with Different Properties and Designs.
But as I did some research, I realized its going to be messed up upon generation, I wanted it Linear in sequence and not just random Components with Random Designs, I wanted an organized linear pattern of generated UI Components. and I was thinking of using Seeds to find previously generated UI Components and saving it in a History Panel of panel. and being able to search it.
Needs some opinions and ideas because we're blasting our way to graduation..
Thank you! any help is appreciated!
There is actually quite a bit of valuable information/ example/ research paper here on this site
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If ChatGPT is merged into search engines developed by internet technology companies, will search results be shaped by algorithms to a greater extent than before, and what risks might be involved?
Leading Internet technology companies that also have and are developing search engines in their range of Internet information services are working on developing technological solutions to implement ChatGPT-type artificial intelligence into these search engines. Currently, there are discussions and considerations about the social and ethical implications of such a potential combination of these technologies and offering this solution in open access on the Internet. The considerations relate to the possible level of risk of manipulation of the information message in the new media, the potential disinformation resulting from a specific algorithm model, the disinformation affecting the overall social consciousness of globalised societies of citizens, the possibility of a planned shaping of public opinion, etc. This raises another issue for consideration concerning the legitimacy of creating a control institution that will carry out ongoing monitoring of the level of objectivity, independence, ethics, etc. of the algorithms used as part of the technological solutions involving the implementation of artificial intelligence of the ChatGPT type in Internet search engines, including those search engines that top the rankings of Internet users' use of online tools that facilitate increasingly precise and efficient searches for specific information on the Internet. Therefore, if, however, such a system of institutional control on the part of the state is not established, if this kind of control system involving companies developing such technological solutions on the Internet does not function effectively and/or does not keep up with the technological progress that is taking place, there may be serious negative consequences in the form of an increase in the scale of disinformation realised in the new Internet media. How important this may be in the future is evident from what is currently happening in terms of the social media portal TikTok. On the one hand, it has been the fastest growing new social medium in recent months, with more than 1 billion users worldwide. On the other hand, an increasing number of countries are imposing restrictions or bans on the use of TikTok on computers, laptops, smartphones etc. used for professional purposes by employees of public institutions and/or commercial entities. It cannot be ruled out that new types of social media will emerge in the future, in which the above-mentioned technological solutions involving the implementation of ChatGPT-type artificial intelligence into online search engines will find application. Search engines that may be designed to be operated by Internet users on the basis of intuitive feedback and correlation on the basis of automated profiling of the search engine to a specific user or on the basis of multi-option, multi-criteria search controlled by the Internet user for specific, precisely searched information and/or data. New opportunities may arise when the artificial intelligence implemented in a search engine is applied to multi-criteria search for specific content, publications, persons, companies, institutions, etc. on social media sites and/or on web-based multi-publication indexing sites, web-based knowledge bases.
In view of the above, I address the following question to the esteemed community of scientists and researchers:
If ChatGPT is merged into search engines developed by online technology companies, will search results be shaped by algorithms to a greater extent than before, and what risks might be associated with this?
What is your opinion on the subject?
I invite you all to discuss,
Thank you very much,
Best wishes,
Dariusz Prokopowicz
If tools such as ChatGPT, after the necessary update and adaptation to current Internet technologies, are combined with search engines developed by Internet technology companies, search results can be shaped by certain complex algorithms, by generative artificial intelligence learned to use and improve complex models for advanced intelligent search of precisely defined topics, intelligent search systems based on artificial neural networks and deep learning. If such solutions are created, it may involve the risk of deliberate shaping of algorithms of advanced Internet search systems, which may generate the risk of interference and influence of Internet search engine technology companies on search results and thus shaping the general social awareness of citizens on specific topics.
What is your opinion on this issue?
I invite everyone to join the discussion,
Thank you very much,
Best regards,
Dariusz Prokopowicz
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Hello experts
I am dealing with an optimization problem in which the algorithm will choose the section of the column (RC structure) between a lower and an upper bound (e.g., LB and UB).
How can I ask the algorithm to change the cross section if the condition of the period isn't satisfied?
N.B: the period value is retrieved from the sap2000 software using the API, and it has no relation with the design variables to be set as a constriant.
Any one knows how to do it please?
You can give a default fitness function value that is very large (in case you are in a minimization problem), the algorithm will automatically avoid such parameters in next iterations
Like this:
If period verified
If not
Fitness= large
End
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Hi everyone,
I am a Ph.D. student and I am working on the production scheduling of an open-pit mine by genetic algorithm, and I have a problem with converting infeasible sequencing to a feasible one, so I need someone to help me. how can I create a normalized plan based on a completely random solution? on the other hand, how can I change the time periods that are assigned to genes of the chromosomes or all blocks of the ultimate pit?
I don't know any normalization method for obtaining a feasible sequencing plan, and I studied some papers related to it, but the normalization methods the authors used, have not been stated in detail! because when it is assigned a time period to blocks, there is any rule to regard the condition of 45 degrees on slopes!!!
I want to explain more about the MATLAB code I wrote. Precedence constraints (These constraints ensure that a block can only be extracted during some time period t if all of its predecessor blocks have been mined completely before or during time period t) are not true, I think because when I import the results into Surpac software, the extraction of blocks and sequencing of them seems like strip mining regardless of 45 degrees on slopes and instead of formation of inverse cones! I don't know how can I implement the normalization process you mentioned above?!
I attach an output file of MATLAB so you can see.
I greatly appreciate you for offering your valuable suggestions.
wishing you all the best!
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Please I need help me with some open research problems in genetic algorithm relative to computer vision.
Ummar Shehu Here are a few areas where GAs can be explored in the context of computer vision:
1. Feature selection and extraction: Genetic algorithms can be used to optimize the selection and extraction of relevant features from visual data. This involves finding the most discriminative and informative features that contribute to accurate computer vision tasks such as object recognition, image segmentation, or facial recognition.
2. Object tracking and detection: Genetic algorithms can aid in developing efficient and robust algorithms for object tracking and detection in video sequences. The challenge lies in designing GA-based methods that can handle occlusions, variations in scale, complex background clutter, and multiple object tracking scenarios.
3. Image registration: Genetic algorithms can assist in solving image registration problems, where the goal is to align multiple images taken from different viewpoints, time frames, or modalities. Efficient registration methods are crucial for tasks such as medical image analysis, 3D reconstruction, and motion estimation.
4. Optimal camera placement: Genetic algorithms can be utilized to optimize camera placement in computer vision systems. By considering various factors such as field of view, occlusions, and resolution requirements, GAs can help determine the optimal camera positions and orientations for achieving specific goals, such as surveillance, monitoring, or navigation.
5. Image synthesis and enhancement: Genetic algorithms can contribute to image synthesis and enhancement tasks, such as super-resolution, de-noising, or image inpainting. GAs can explore the space of possible solutions to generate high-quality images or to recover missing or corrupted parts of an image.
6. Deep learning model optimization: Genetic algorithms can be combined with deep learning techniques to optimize the architecture, hyperparameters, or training process of deep neural networks for computer vision tasks. This involves exploring the vast design space of deep learning models to improve their performance, efficiency, or interpretability.
These are just a few examples of research problems where genetic algorithms can be applied in the context of computer vision. It is important to note that each of these areas presents unique challenges and opportunities for further investigation and innovation.
When exploring these research problems, it is advisable to review the existing literature, identify current limitations, and propose novel approaches or adaptations of genetic algorithms that can address these challenges. Collaborating with other researchers in the field, attending conferences, and staying up to date with the latest advancements in both genetic algorithms and computer vision will also greatly contribute to your research endeavors.
I hope these suggestions provide you with a starting point for your exploration of genetic algorithms in relation to computer vision. If you have any specific questions or need further clarification, please feel free to ask. Good luck with your research!
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Hello,
I am trying to build an (r,Q) inventory control model , to be implemented in a local manufacturing firm. In my model, r is identified by studying the demand trends and fitting normal distributions, while Q is identified by genetic algorithm minimising an inventory cost function.
Firstly the list of parts for re-order are first ideintified, after which a genetic algorithm co-optimises the batch sizes across all parts on re-order, considering factory capacity and demand constraints.
In my simulations, I find that the Q values suggested by my algorithm are realistic and in-line with what is required, however, it turns out that the model identifies all the parts which are less likely to be ordered by the production team.
Would anyone be able to shed any light on how to control this ?
Dear Akhilnandh,
an (r,Q) model is a reorder point model, where r is the reorder point. When the inventory position of an item reaches r, the Q units are ordered. Thus, I don't understand what you mean when you say "Firstly the list of parts for re-order are first ideintified". In a (r,Q) policy the part to be re-ordered is the one that reaches the reorder point.
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I am studying an article about implementation of genetic algorithm. I have uploaded the parameters for this implementation.
As I know, in roullete selection method, probability of selecting individuals is based on their cost function values. the more optimum individual has the higher probability of being selected. So, what is the meaning of selection probability here?
Here is the link to the article:
here is the articles referring to GA using natural selection theory:
(1) (PDF) Genetic Algorithm Based on Natural Selection Theory for Optimization Problems (researchgate.net)
(2) (PDF) Genetic Algorithm Optimization by Natural Selection (researchgate.net)
although I have studied already this article completely, I could not still understand where selection probability term is used in this approach.
Thank you
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Dear All,
Could you please inform regarding software for the metaheuristic algorithms that are compatible with Ms. Excel (add-ins) and free (or not).
a) Genetic algorithm (GA) = ???
b) Particle swarm-optimization (PSO) = ???
c) Bee colony optimization (BCO) = ???
d) etc..
a) Genetic Algorithm (GA):
• Solver in Excel: Microsoft Excel has a built-in solver add-in that can be used to solve optimization problems, including genetic algorithms. It allows you to define optimization variables, constraints, and objectives to find optimal or near-optimal solutions.
b) Particle Swarm Optimization (PSO):
• PSO Excel VBA: PSO Excel VBA is a free add-in for Microsoft Excel that implements the Particle Swarm Optimization algorithm. It provides an intuitive interface to define optimization variables, constraints, and objectives, and offers various options to customize the PSO algorithm.
c) Bee Colony Optimization (BCO):
• Bees Algorithm in Excel: Bees Algorithm in Excel is an add-in that implements the Bee Colony Optimization algorithm. It allows you to define optimization variables, constraints, and objectives, and provides several customization options. The add-in is free for academic and personal use.
d) Other Metaheuristic Algorithms:
• Excel Solver with VBA: Microsoft Excel's built-in solver add-in, combined with Visual Basic for Applications (VBA), can be used to implement various metaheuristic algorithms like simulated annealing, tabu search, or ant colony optimization. With VBA, you can create custom macros and scripts to extend the capabilities of Excel's solver.
It's important to note that while these add-ins provide convenient interfaces for implementing metaheuristic algorithms within Excel, their functionalities and performance may vary. For more advanced or specialized applications, dedicated optimization software or programming languages like MATLAB, Python (with libraries such as DEAP or PyGMO), or R (with packages like GA or PSO) might be more suitable.
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I have been searching for genetic algorithm a lot, But I could not understand necessity of using crossover and mutation simultaneously yet. In an online course, following paragraph was written:
crossover is an operation which drive the population towards a local maximum(or minimum). If we use only crossover, it will yield approximately the same result as hill-climbing algorithm!!!mutation is a so-called divergence operation force one or more individuals of the population to discover other regions of the search space. So, this is essential in order to find the global optimum.
I can not understand it easily, especially because in metaheuristic algorithms, we must cope with somehow statistical-based optimization. Moreover, I had implemented GA in python and still can not realize the performance difference between these two parameters.
There are two concepts related to optimization.
1) Exploration
2) Exploitation
Through crossover you are exploiting (mostly) or searching solution within the found solutions i.e. new chromosomes are formed using same values (genes) of the chromosomes. Whereas mutation can help to bring some more diversity in the population and can mutate a gene to any random point. Hence, mutation can introduce more diversity through exploration of other solutions rather than existing solutions.
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Hi everyone,
I am a Ph.D. student and I am working on the production scheduling of an open-pit mine by genetic algorithm, and I have a problem with converting infeasible sequencing to a feasible one, so I need someone to help me. how can I create a normalized plan based on a completely random solution? on the other hand, how can I change the time periods that I assigned to genes of the chromosomes or all blocks of the ultimate pit? I don't know any normalization methods for obtaining a feasible sequencing, and I studied some papers related to it, but the normalization methods the authors used, have not been stated in detail!
Technically, if your model is an Integer Programming model then you need an algorithm for finding a feasible solution.
If this is the case then this paper might help
A Simplicial Approach to the Determination of an Integer Point of a Simplex
Chuangyin Dang and Hans Van Maaren, Mathematics of Operations Research, Vol. 23, No. 2 (May, 1998), pp. 403-415
Good luck
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Dear friend:
To reduce feature dimension and improve the model performance in machine learning, I am using the genetic algorithm to chose the best 20 features form 100 feature subset. However, some feaures are presented more than one in a chromosome, what is the best method to deal with this issue?
Best wishes!
WangChao
Genetic algorithms (GA) are optimization algorithms inspired by the natural process of evolution. They work by evolving a population of potential solutions to a problem through successive generations. Each individual in the population represents a potential solution, and their fitness is evaluated based on how well they solve the problem.
In a GA, a chromosome is a string of genes that represents a potential solution to the problem being solved. Each gene typically represents a feature or parameter that can be varied to improve the fitness of the solution. The chromosomes are then evaluated based on their fitness, and the fittest individuals are selected to produce the next generation of solutions.
When using a GA, it is possible for a gene to repeat within a chromosome. This can happen if the problem being solved has multiple features or parameters that are related and need to be optimized together. In this case, it may be beneficial to repeat certain genes to ensure that the related features are optimized together.
However, it is also possible for gene duplication to result in redundant or unnecessary features, which can increase the search space and make the optimization process more difficult. To address this issue, techniques such as crossover and mutation can be used to introduce diversity and prevent the population from getting stuck in local optima.
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Hello experts
This question is shared with one of my research team.
We are dealing with an optimization problem in which the algorithm will choose the cross-section of the column (RC structure) between a lower and an upper bound (e.g., LB and UB).
We know that the obtained optimal values for the section can be for example 330mm.
Recently, we came across this phrase in a paper dealing with the same problem where the author stated that 'The column sections are square with dimensions of 250 mm × 250 mm to 1200 mm × 1200 mm, by a step of 50 mm'
Our concern is how to oblige the algorithm to choose 50mm for the next candidate within the design interval.
Anyone knows how to do it, please?
To ensure that the optimization algorithm chooses a step size of 50mm within the design interval, you can incorporate a discretization constraint into the optimization problem. This constraint will restrict the possible values for the cross-section to a finite set of discrete values, with a fixed step size of 50mm.
One way to implement this constraint is to introduce a binary variable for each possible cross-section dimension, where the variable takes the value of 1 if that dimension is selected and 0 otherwise. Then, you can use a set of linear constraints to ensure that only one dimension is selected, and the selected dimension is a multiple of 50mm within the given design interval.
For example, if the design interval is [250mm, 1200mm], you can define the binary variable x_i for each possible dimension d_i within this interval, where d_i = 250mm + 50mm * (i-1) for i = 1, 2, ..., 19. Then, you can add the following set of constraints to the optimization problem:
• Only one dimension can be selected: Sum(x_i) = 1
• The selected dimension is within the design interval: Sum(d_i * x_i) >= LB and Sum(d_i * x_i) <= UB
• The selected dimension is a multiple of 50mm: d_i * x_i = k * 50mm for some integer k
These constraints will ensure that the algorithm chooses a cross-section dimension within the given design interval, with a step size of 50mm, and only one dimension is selected. By adding this discretization constraint to the optimization problem, you can ensure that the algorithm will always choose the next candidate within the design interval with a step size of 50mm.
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How should the learning algorithms contained in ChatGPT technology be improved so that the answers to questions generated by this form of artificial intelligence are free of factual errors and fictitious 'facts'?
How can ChatGPT technology be improved so that the answers provided by the artificial intelligence system are free of factual errors and inconsistent information?
In view of the above, I address the following question to the esteemed community of scientists and researchers:
How should the learning algorithms contained in the ChatGPT technology be improved so that the answers generated by this form of artificial intelligence to the questions asked are free of factual errors and fictitious "facts"?
How can ChatGPT technology be improved so that the answers provided by the artificial intelligence system are free of factual errors and inconsistent information?
What is your opinion on this subject?
I invite you all to discuss,
Thank you very much,
Best regards,
Dariusz Prokopowicz
Dear people,
“We live in a world of radical ignorance, and the marvel is that any kind of truth cuts through the noise,” says Proctor. Even though knowledge is ‘accessible’, it does not mean it is accessed, he warns…the knowledge people have often comes from faith or tradition, or propaganda, more than anywhere else.”
Is it a reality - how do you think?
he Turk, also known as the Mechanical Turk or Automaton Chess Player (German: Schachtürke, lit. 'chess Turk'; Hungarian: A Török), was a fraudulent chess-playing machine constructed in the late 18th century. From 1770 until its destruction by fire in 1854 it was exhibited by various owners as an automaton, though it was eventually revealed to be an elaborate hoax.[2] Constructed and unveiled in 1770 by Wolfgang von Kempelen (1734–1804) to impress Empress Maria Theresa of Austria, the mechanism appeared to be able to play a strong game of chess against a human opponent, as well as perform the knight's tour, a puzzle that requires the player to move a knight to occupy every square of a chessboard exactly once.
The Turk was in fact a mechanical illusion that allowed a human chess master hiding inside to operate the machine. With a skilled operator, the Turk won most of the games played during its demonstrations around Europe and the Americas for nearly 84 years, playing and defeating many challengers including statesmen such as Napoleon Bonaparte and Benjamin Franklin. The device was later purchased in 1804 and exhibited by Johann Nepomuk Mälzel. The chess masters who secretly operated it included Johann Allgaier, Boncourt, Aaron Alexandre, William Lewis, Jacques Mouret, and William Schlumberger, but the operators within the mechanism during Kempelen's original tour remain a mystery.
Who answers to users in large language model built on great human thoughts database?
Thinking?
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How to use a genetic algorithm for optimizing the Artificial Neural Network model in R.
The goal is to solve a diabetes classification problem using an Artificial Neural Network (ANN) optimized by a Genetic Algorithm, discovering the performance difference of different parameters of the ANN model and comparing this training method with additional optimizers like stochastic gradient descent, RMSprop, and Adam optimizer.
Regards,
Shafagat
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In genetic algorithms, if two parents are two graphs or two trees, instead of two sequences, then how to design the crossover operator?
See what Google says "randomly swap it, or compose it out of two parts of two parental (sub)trees", where it = tree/ subtree.
Perhaps ChatGPT would provide you with even smarter guesses.
Technically, Fig. 3 and 4, next, show crossover and mutation.
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How can I calculate ANOVA table for the quadratic model by Python?
I want to calculathe a table like the one I uploaded in the image.
To calculate an ANOVA (Analysis of Variance) table for a quadratic model in Python, you can use the statsmodels library. Here is an example of how you can do this:
#################################
import statsmodels.api as sm
# Fit the quadratic model using OLS (Ordinary Least Squares)
model = sm.OLS.from_formula('y ~ x + np.power(x, 2)', data=df)
results = model.fit()
# Print the ANOVA table
print(sm.stats.anova_lm(results, typ=2))
#################################
In this example, df is a Pandas DataFrame that contains the variables y and x. The formula 'y ~ x + np.power(x, 2)' specifies that y is the dependent variable and x and x^2 are the independent variables. The from_formula() method is used to fit the model using OLS. The fit() method is then used to estimate the model parameters.
The anova_lm() function is used to calculate the ANOVA table for the model. The typ parameter specifies the type of ANOVA table to compute, with typ=2 corresponding to a Type II ANOVA table.
This code will print the ANOVA table to the console, with columns for the source of variance, degrees of freedom, sum of squares, mean squares, and F-statistic. You can also access the individual elements of the ANOVA table using the results object, for example:
#################################
# Print the F-statistic and p-value
print(results.fvalue)
print(results.f_pvalue)
#################################
I hope that helps
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Few researches have stated that a multi population genetic algorithm avoids premature convergence as opposed to the conventional GA. Does multipopulation GA offer any other advantages over the single population GA? Also, is using a large population size for GA better than multipopulations?
There is no theory about population size. In most cases, population size is determined experimentally. A bigger single population is not better than a group of populations.
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I am trying to use the Genetic Algorithm to optimise the strategies for containing the spread of COVID-19. The strategies include (i) travel restrictions, (ii) lock-down, and (iii) wearing of nose masks. The model is formulated as an SEIR compartmentalized model.
I have spent a lot of time trying to learn how to do that using MATLAB but I have not succeeded.
I will be grateful if anyone can share their MATLAB codes with me.
MATLAB documentation contains many examples on implementation. Please see this.
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How to use genetic algorithm in predicting some values based on csv file
Here is simple implementation of Genetic Algorithm. It is based on current best public solution https://www.kaggle.com/jazivxt/using-a-baseline.
Regards,
Shafagat
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Over the last few decades, there have been numerous metaheuristic optimization algorithms developed with varying inspiration sources. However, most of these metaheuristics have one or more weaknesses that affect their performances, for example:
1. Trapped in a local optimum and are not able to escape.
2. No trade-off between the exploration and exploitation potentials
3. Poor exploitation.
4. Poor exploration.
5. Premature convergence.
6. Slow convergence rate
7. Computationally demanding
8. Highly sensitive to the choice of control parameters
Metaheuristics are frequently improved by adding efficient mechanisms aimed at increasing their performance like opposition-based learning, chaotic function, etc. What are the best efficient mechanisms you suggest?
Providing a proper balance between exploration and exploitation is a good choice.
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Dear community,
I am planning on conducting a GWAS analysis with two groups of patients differing in binary characteristics. As this cohort naturally is very rare, our sample size is limited to a total of approximately 1500 participants (low number for GWAS). Therefore, we were thinking on studying associations between pre-selected genes that might be phenotypically relevant to our outcome. As there exist no pre-data/arrays that studied similiar outcomes in a different patient cohort, we need to identify regions of interest bioinformatically.
1) Do you know any tools that might help me harvest genetic information for known pathways involved in relevant cell-functions and allow me to downscale my number of SNPs whilst still preserving the exploratory character of the study design? e.g. overall thrombocyte function, endothelial cell function, immune function etc.
2) Alternatively: are there bioinformatic ways (AI etc.) that circumvent the problem of multiple testing in GWAS studies and would allow me to robustly explore my dataset for associations even at lower sample sizes (n < 1500 participants)?
Thank you very much in advance!
Kind regards,
Michael Eigenschink
for the second part of your problem, you can try vcf2gwas pipeline, that is very easy to run as a docker image
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i am using genetic algorithm. when i am testing its performance, i found my code doesn't work as it should be. when I am changing the weights from 1 to -1 (Maximize to minimize), my code shows the exact same results.
my code is as follows:
def train_evaluate(individual_solution):
x_bits = BitArray(individual_solution[0:4])
y_bits = BitArray(individual_solution[4:])
x = x_bits.uint
y = y_bits.uint
return x+y ,
population_size = 6
num_generations = 10
gene_length = 8
creator.create('FitnessMin', base.Fitness, weights = (-1.0,)) ## I only change this parameter
creator.create('Individual', list , fitness = creator.FitnessMin)
toolbox = base.Toolbox()
toolbox.register('binary', bernoulli.rvs, 0.5)
toolbox.register('individual', tools.initRepeat, creator.Individual, toolbox.binary, n = gene_length)
toolbox.register('population', tools.initRepeat, list , toolbox.individual)
toolbox.register('select', tools.selRoulette)
toolbox.register('mate', tools.cxOrdered)
toolbox.register('mutate', tools.mutShuffleIndexes, indpb = 0.2)
toolbox.register('evaluate', train_evaluate)
population = toolbox.population(n = population_size)
r, log = algorithms.eaSimple(population, toolbox, cxpb = 0.4, mutpb = 0.1, ngen = num_generations, verbose = False)
tools.selBest(population,k = 1)
when I test weights = (1, )
the result is: [[1, 1, 1, 1, 1, 1, 1, 1]]
when I test weights = (-1, )
the result is: [[1, 1, 1, 1, 1, 1, 1, 1]]
great thanks if you could kindly help me.
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Hello,
I found this approach -the per-node approach- in a paper recently published, in which the author tried to describe a new way to tick a behaviour tree instead of using the conventional approach, in order to evolve a behaviour for an agent (game agent) using genetic programming.
I would like to discuss this approach and how it works deeply.
Thank you.
Hello, yes for sure,
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In his name is the judge
Hi
There is a fuzzy logic control system in python. The system contain 2 inouts and 18 outputs.
inference of system is mamdani and shape function used to be guassian.
Then in term of refine performance of the controller I need to optimize specifications belong to shape functions of both input and output. In order to that I need to use multi objective optimization.
We have 2 input and 1 output in case of this problem. I have developed 3 shape functions for each entrance and 3 for output and the shape function is gaussian so we have 18 parameters totally.
I defined my problem as a function in python. But notice this I there is not any clear relationship between input and output of function. It’s just a function which is so complicated with 2 inputs and 18 outputs.
I made my decision to use NSGAII algorithm and I really don't want to change the algorithm.
So I try every way to optimize my function but I didn’t find any success. By searching about python library which can do multiobjective optimization I find Pymoo as the best solution but I really failed to optimize my function which is complicated custom function, with it.
So It’s really my pleasure if you can introduce me a library in python or suggest me a way that I can use Pymoo in order to this aim.
wish you best
Take refuge in the right.
You can stick with Mamdani Fuzzy System if it is comfortable for you to design a "workable" FIS. You probably don't need optimization if your FIS works satisfactorily. From the "workable" FIS to the "satisfactory" FIS, you probably just need make minor adjustment to the design parameters. That's the Spirit of Engineering Design.
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Hello,
I'm working with a large number of L*a*b* colour points from ceramic samples, and I'm interested in reconstructing possible spectrums that could have made those colours. There's more than one possibility, of course, but my plan was to use a genetic algorithm approach to find several answers at a distance from each other to set out the solution space. The better candidates are all positive, and reasonably smooth as I'm dealing with natural clays, not sharply pigments goods.
The problem is that I'm currently unable to get closer than about 3units to the target colour, and often no closer than 10-15 units. This could be for the following reasons, is there anything else I've missed?
1) Solution space pixelation - I'm working with 5nm wide bands, so there are only 81 bands in the spectrum, only a fraction of which matter for the specific colour, and it's possible that there's no close solution at this resolution
2) many local maximas - it's possible the solution space is very unsmooth, and the algorithm is latching onto the 'wrong' one too early. The evidence for this is that runs frequently 'lock' quite early on, but repeated runs give different results.
3) something else...
What you need is 'CIE Standard Observer Colorimetry'. There are many good publications and books available under the above tag.
Regards David
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Hi,
What is the different between 'fitness function score' with 'objective function value'? If it is different, how we define the score? I use single objective GA run using Matlab optimization tool. I only have bounds for my variables. Thank you
Why is " survival of the fittest"qouted if low-fitted features are selected to make fitness and objective functions the same?
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Although the genetic algorithm is very helpful to find a good solution, it is very time-consuming. I've recently read an article in which the authors used the genetic algorithm for pruning a neural network on a very small dataset. They pruned a third of their network and reduces the size of the model from 90MG to 60MG. Their pruning also decreased the inference time of their model by around 0.5 seconds. Although, unlike other methods, pruning using the genetic algorithm does not deteriorate their model's performance, finding redundant parts of the network using the genetic algorithm is an overhead. I wonder why they think their method is useful and why people still use the genetic algorithm.
Genetic algorithms have the several characteristics over others such as , (1) Natural selection and natural genetics are at the heart of the Genetic Algorithm. -(2)These are simple to comprehend and use. -(3) These are frequently encountered in optimization issues. (4)These are based on a population of points rather than a single point. -(5) Instead of deterministic rules, these utilize probabilistic transition rules. -(6)Genetic algorithms can handle a large number of variables efficiently. - (7)No derivative information is required for these methods. -(8)These are more successful when dealing with complicated difficulties than when dealing with basic problems.
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I want to use pyomo to model MILP problems in python such as Green Vehicle Routing Problem and others but use metaheuristics algorithms (GA, Harmony search, etc.) to solve those problems instead of using pyomo solvers, its part of my phd project and can not use derivative-based solvers at all; If the answer that I can not, what is the alternative to model the problems in python then solve them with metaheuristics algorithms because i found pyomo easy and time saving to be used in formulating the problems.
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Hi,
How can we prove the results of the GA that are optimised, in other words, are there criteria or measures to determine the best results (optimize results).
regards
Hi, When the search space is wide, the genetic algorithm may converge to the optimal value very late or suffer from the problem of local optimality. One of the ways to solve this problem is the use of local optimal search methods, which seems that, for example, the use of SA may be able to improve the optimal value.
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MATLAB optimization tool box. nothing mentioned in the matlab documentation.
Crossover is a genetic operator used to vary the programming of a chromosome or chromosomes from one generation to the next. Crossover is sexual reproduction. Two strings are picked from the mating pool at random to crossover in order to produce superior offspring. The method chosen depends on the Encoding Method.
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Need to know the procedure of implementation as various options are available in CST for optimization.
Hello,
Few papers are attached for your help.
Thanks,
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I have studied in research paper adaptive genetic algorithm, adaptive firefly algorithm but the clear description is not available in those research papers. Plz help me so that I can clarify this and why it is used ?
Normally, an adaptive controller is designed based on one of the available techniques. Each technique is originally designed for a specific class of dynamic system. The controller is then adjusted as data are collected during run time to extend its effectiveness to control a large class of dynamic systems.
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Is there an index that includes the mixing index and pressure drop for micromixers?
For example, for heat exchangers and heat sinks, there is an index that includes heat transfer performance and hydraulic performance, which is presented below:
η=(Nu/Nub)/(f/fb)1/3
The purpose of these indexes is to create a general criterion to check the devices' overall performance and investigate the parameters' impact.
Is there an index that includes mixing performance and hydraulic performance for micromixers?
Dear
Rani P Ramachandran
Thank you for your answer. I think mixing energy cost (MEC) is the index that I look for it.
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Hello,
When I should use memetic algorithm rather than genetic algorithm?
Best Regards,
There is no straightforward answer to this question. A memetic algorithm is just an extension of a genetic algorithm. It uses local search to reduce the likelihood of premature convergence. But according to the No Free Lunch theorem, there is no single best optimization algorithm, so this doesn't necessarily mean it is better. The best way is to check papers with problems similar to the ones you want to solve and then pick the one that performs better. But I wouldn't limit myself to only these two algorithms. Take the one that is best suited for the given problem. Here are some papers comparing a Memetic and a Genetic algorithm:
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How does one optimize a set of data which is comprised of 3 input variables and 1 output variable (numerical variables) using a Genetic Algorithm? and also how can I create a fitness function? How is a population selected in this form of data? What will the GA result look like, will it be in a form of 3 inputs and 1 output?
I do understand how the GA works, however, I am confused about how to execute it with the form of data that I have.
My data is structured as follows, just for better understanding:
3 columns of Input data, and the fourth column of Output Data. (4 Columns and 31 rows) data. The 3 input variables are used to predict the fourth variable. I want to use GA to improve the prediction results.
Lastly, Can I use decimal numbers (E.g. 0.23 or 24.45); or I should always use whole numbers for chromosomes.
Decide first whether you want to improve the features or parameters of an algorithm or both? When designing your chromosome in terms of features You must first create a population of candidates that you can do randomly .... On some chromosomes you may want to turn off some features or on some features you can use 1 or 0 for this. Zero to indicate an added feature and to exclude a feature from a given set. In the case of parameters, the value of the chromosome can be a decimal point in the given range.
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I have running GA optimization using "gamultiobj" in Matlab. The upper bound and lower bound of my design variables are like [10 30 175 1] and [30 60 225 3]. But after convergence, I see the design variables (like all of them) near 20,47,175 and 1. I am not getting a Pareto Front. What could be the possible reasons for that?
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how genetic algorithm fittness function calculated for botnet attack detection?
A Decision Tree-Aware Genetic Algorithm for Botnet Detection
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I already saw some examples of GA (genetic algorithm) applications to tune PID parameters, but I (until now) don't know a way to define the bounds. The bounds are always presented in manuscripts, but they appear without great explanations. I suspect that they are obtained in empirical methods.
Could Anyone recommend me research?
Dear Italo,
In general, the bounds selection is made empirically because the "suitable range" of a PID controller is problem-dependent. The way I use to select the bounds is: 1. I tune a PID controller that produces a stable response to the closed-loop system. Then, 2. I choose a range around this "nominal" value large enough such that the GA has still some degree of freedom to search in the optimization space. Finally, 3. if the GA converges, I start decreasing/increasing this range till I got a more or less good behavior of the GA, i.e., the GA doesn't stick in a sub-optimal minimum or so.
If you want to use a more rigorous approach, I would suggest computing the set of all stabilizing PID controllers for the particular system. Then, I would establish the bounds for the GA search space to be the this computed set. In that way, you would search for the optimal controller only within those producing a stable closed-loop response.
Best,
Jorge
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Assuming, I have a number of vectors where each item in the vector is a real number. I was to generate some vectors from those initials ones and add them to the collection of initial vectors. This was I can have a initial population of increased size. What are some ways to do this?
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Dear all,
according to [1], QGA are more efficient than GA (genetic algorithms), and according to [2] PSO is better than GA. Do you know if there are papers that do the comparison between PSO and QGA ?
Thank you
[1] - Z.Laboudi (2012) - Comparison of Genetic Algorithm and Quantum Genetic Algorithm
[2] - F D Wihartiko (2017) - Performance comparison of genetic algorithms and particle swarm optimization for model integer programming bus
time tabling problem
Best regards
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How can I optimize ANFIS using Genetic Algorithm; and also with Aquila Optimizer in MATLAB? Any available code I can use?
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Dear Members
I had recently gone through a sample program code (in MATLAB) corresponding to Pareto GA in Appendix II (Program 4, Page: 219) of the book PRACTICAL GENETIC ALGORITHMS by Haupt. However, I failed to understand the Niching strategy employed in the program code. Also, kindly explain the Pareto dominance tournaments that have been employed or used for mate selection in the example provided in the book. The flow diagram for Pareto GA delineated in Figure 5.2 of the book, mentions an increase in the cost of closely located chromosomes. But the code apparently contains no such steps. Kindly explain the anomaly.
If there is no layout available, nexttile creates one. Create a Pareto chart by passing the axes to the pareto function as the first argument. Call the nexttile function to add a second axes object to the layout. Then create a second Pareto chart.
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hello!
how i can use the Neural network fitness function i.e., after simulation the value we get basically in terms of MSE(mean squared error). so can you tell me how we this MSE as fitness function in Genetic Algorithm in order to minimize the MSE for better neural netwrok model.
Regards,
Shafagat
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hello!
can anyone tell me how i can integrate the neural network and genetic algorithm. i have a simple real coded genetic algorith where i need to use the neural network. please help me on thia. my code is in matlab. if some one provide me the code
Regards,
Shafagat
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genetic algorithm MATLAB code
Regards,
Shafagat
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How can I get a MATLAB code for solving multi objective transportation problem and traveling sales man problem?
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Here, I have implemented my genetic algorithm with combining two crossover operators i.e..(PMX,OX). After running the algorithm I am getting same fitness values as output from staring generation to ending generation. There is no change Is it correct.? please can anyone answer
Best regards
Anna
Nabil Boumedine I used initially ordered crossover of genetic algorithm for solving tsp problem
after generating new offspring (a,b) with ordered crossover.  After that I used a,b as new parents for partially mapped crossover to produce another new offspring i.e(c,d).
So here c, d acts as another parent to apply uniform crossover to produce another new offspring. This will be done on one algorithm
def crossovers(self, p1, p2, r_cross):
off1 = self.ordered_crossover(p1, p2, r_cross)
off2 = self.ordered_crossover(p2, p1, r_cross)
#print(off1, off2)
Off1 = self.partially_mapped(off1[0], off2[0],r_cross)
Off2 = self.partially_mapped(off2[0], off1[0],r_cross)
#print(Off1,Off2)
off3 = self.uniform(Off1[0], Off1[1],1)
off4 = self.unifrom(Off2[1], Off2[0],1)
return [off3, off4]
couuld you tell for above theory , is this code is correct
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Hii,
I have implemented genetic algorithm with partially mapped crossover for solving tsp. Here I'm using different random.seed vlaues(24,42,72,112). When I run the algorithm , can I get the same graph with different seed values without changing the parameters but city co-ordinates are randomly generated.
best regards
Annapareddy
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Hello all, I am new to image processing.... I want to detect edges from an image. When I searched on detecting edges in images I came across about edge detection using genetic algorithms. Where I find the chromosomes and find fitness function for crossover and mutation process. Also, they say that chromosomes are binary array. I am finding it difficult to understand how can we find binary arrays from an image and picking that as a chromosomes or do we have any methods to find chromosomes from an image. Please help me...any help is appreciated.
1.Taking gray levels of histogram of an image in an array u can create a chromosome.
2. Taking coordinate (i, j) and amplitude of pixels in an array u can do it.
Generate an expression including statistical parameters and features of image as the fitness function.
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Hii,
I have implemented genetic algorithm with partially mapped and ordered crossover for solving tsp.
In partially mapped crossover, I have taken 100 generations, when I run the algorithm the best solution is getting of 20th generation. from 20th generations onwards the solution is constant until last generation( 100). may I know the error . Please acn anyone suggest the error.?
Thank you
I need help for this code. How can add getters and setters for this class city method : please
import math
class City:
def __init__(self, x, y):#initializing the cities
self.x = x
self.y = y
def dist(self, city):#calculate distance betwwen 2 cities
dx = abs(self.x - city.x)#mising getter and setters methods cant access
dy = abs(self.y - city.y)
return math.sqrt(dx ** 2 + dy ** 2)
def __repr__(self):#
return 'City : x:' + str(self.x) + ', y:' + str(self.y)
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genetic algorithm
thank you
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Define attributes suitable for comparison of both algorithms?
Genetic algorithm with tabu search OR Genetic algorithm with simulated annealing ?
Mahnoor Iftekhar In my opinion, you must examine both algorithms in your problems, then select which one is the best.
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I am using an evolutionary-based GA optimisation tool that divides the space of possibilities into subsets to perform hypercube sampling within each subset, and thereafter generates multiple generation of results, the best of which will form the Pareto front and through each iteration move closer to the global optimum. However is it possible that through the process of hypercube sampling (and hence disregarding some options) , this tool might miss a true global optimum?
this is a natural thing in mathematics there is no perfect solution especially the no linear problem, and most metaH utilized in the no linear problem, so you need always to expect and accept that fact, and keep improving the study of the metaH
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While working in both the software, after loading the training and validation data for the prediction of a single output using several input variables (say 10), it skips some of the inputs and delivered an explicit mathematical equation for future prediction of the specific parameter but it skips some of the input variables (say 2 or 3 or maybe greater). What criteria are these software uses in the back for picking the most influential parameters while providing a mathematical predictive model?
First of all, was the fitness (error) zero (0) at the end of the evolution?
If yes, it means that the skipped variables are not important for the data being analyzed.
If not, it can either mean that some variables are not important or that the evolution is stuck in a local optimum.
Note, that for real-world data, it is unlikely to obtain fitness 0 because of noise or other imperfections (in data collection or measurement).
regards,
Mihai
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Hello everyone,
Could you recommend papers, books or websites about mathematical foundations of artificial intelligence?
Thank you for your attention and valuable support.
Regards,
Cecilia-Irene Loeza-Mejía
Mathematics helps AI scientists to solve challenging deep abstract problems using traditional methods and techniques known for hundreds of years. Math is needed for AI because computers see the world differently from humans. Where humans see an image, a computer will see a 2D- or 3D-matrix. With the help of mathematics, we can input these dimensions into a computer, and linear algebra is about processing new data sets.
Here you can find good sources for this:
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Both genetic algorithms and metaheuristic algorithms are optimization algorithms. Is one category of these two is included under the other?
Evolutionary algorithms, physics-based algorithms, swarm-based algorithms, and human-based algorithms are the four primary types of meta-heuristic algorithms. These algorithms are based on human or animal behavior, as well as some physical behaviors of molecules, and so forth.
Genetic algorithms, partial swarm optimization, grey wolff optimization, ant colony optimization, and Social-Based Algorithm are some example.
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I am working on RCSR. I need genetic algorithm for 1-bit coding Metasurface so that i can arrange my unit cells.
Dear Hamza,
May you please describe a little about the technical details of this GA? I am able to modify the GA code based on your goal if I know the details.
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in his name is the judge
hi
I wrot a sub code on opensees for active tlcd or tuned liqiud gass damper (tlcgd) and assign it to some structures, it seems worked correctly.
In next step i want to optimize tlcgds location on each story with some objects like max dislacement or torsion ratio and ... so i have to use multi objective optimization (which may NSGAII algorithm is the best choice) code or toolbox on matlab and simulink. For this purpose i want to run NSGAII algorithm in matlab, then algorithm Calling my code in opensees (tcl) and run it, after that NSGAII algorithm modify damper location (in opensees code) after each timehistory analysis In order to improve objectives and then analysis my code again and again until find best location for dampers.
Note that I actually want to changing dampers location be part of the NSGAII algorithm and the algorithm itself automatically relocation the dampers to get the best answer.
one best solution may use openseespy but i think it's not free access and i can't get it from iran, So i think realyhead Over heels in this case.
Any help is greatly appreciated.
Take refuge in the right.
I think that's the best answer
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I am studying on global optimization algorithm now, it seems that there are many different versions of each kind of algorithm. Only for particle swarm optimization (PSO) algorithm, there are many versions like APSO, CPSO, PSOPC, FAPSO, ARPSO, DGHPSOGS (see Han and Liu, 2014, Neurocomputing). In addition, the class of genetic algorithm (GA), differential evolution (ED), ant colony optimization (ACO), simulated annealing (SA) and so on, can also be used to solve the problem. When we develop a new global algorithm, it is worthwhile comparing the performance of these different methods on some benchmark functions (like Resenbrock function and Rastrigin function) in a fair way. For example, I would say the average number of evaluations of cost functions, the success rate, and the average time-consuming is good as measurements for comparison.
So my question is "is there any source code (Matlab) has been developed for comparing different kinds of global optimization (GO) methods?". The code should be easy to use, convenient to fairly compare enough advanced GO methods, and should also have provided enough benchmark functions (given gradient of each function would be better, so that we can compare some gradient-based global optimization algorithm).
Best wishes,
Bin She.
Hi Serhat, you can find most of the recent Nature Inspired Global Optimization algorithms source codes for all benchmark test functions Matlab File Exchange. Seyed Ali Mirjalili is my favourite author not only publishing a lot of articles, but also source codes on File Exchange. And this web site is also my favourite for global optimization test problems. http://www-optima.amp.i.kyoto-u.ac.jp/member/student/hedar/Hedar_files/TestGO.htm
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I am working on a hybrid wind turbine system and pumped storage system to meet the load of Morocco. the objective is to optimize the gain to resume as much as possible from #pload. optimization #PSO #genetic #algorithm #using #python.
the objective function to be maximized :
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I wanna optimize 2 complex functions (that have an imaginary part and a real part) using MOGA in Matlab, Are there any other methods for this type or there is no change even if the objective functions are complex?
NB: variables are all scalars
Optimize complex objectives in what sense? For example, a complex objective value z1= a1 + ib1 can be better than z2= a2 + ib2 if say the magnitude is larger, i.e. (a12 + b12 ) > (a22 + b22 ). In this sense, you basically have a real-valued objective that is a function of the real and imaginary parts of the complex outcome. Alternatively, you may be interested in real and imaginary parts individually. Say, for instance, you want to maximize the real part and minimize the imaginary part, well then you basically have two individual real-valued objectives for the real and imaginary parts separately. In this sense, if you have 2 complex functions you may treat this as an optimization problem with 4 real-valued objectives.
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I am presently working on the application of the genetic algorithm for solving multi-objective optimisation of machining parameters.
I need assistance in this area. especially as it has to do with genetic Algorithms.
Multi-Objective optimization problem involves more than one objective function to find one or more solutions.
The best reference that can help you understand multi-objective optimization (MOO) is the book by K. DEB "multi-objective optimization using evolutionary algorithms (GA)". the book includes a section about using GA for MOO:
1-Binary GA
2-Real parameters GA
3- Constraint-handling in GA
moreover, it discusses different evolution strategies.
It also discusses Linear and Nonlinear MOO, convex and nonconvex MOO, Pareto optimal solution.
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Hi,
Can anyone be kind to help me with optimizing PID controller gains using genetic algorithm for a MIMO system (Twin Rotor MIMO System (TRMS)). I have been stuck with this problem for quite a very long time now. I have uploaded the code and simulink model for the TRMS with the decoupler that i used.
Whenever i call and run the optimtool for the ga program in MATLAB, the tool does not optimize the system. Instead the system fails to decay and settle on the step input, but both the pitch and yaw signals become unstable and uncontrollable (go into infinity along the y-axis).
I believe the code i am using is wrong and maybe the configured simulink model. Somethings are wrong.
Please I need help Sirs. Thank you
You can try to get initial PID gain values from Ziegler Nicola's method for closed loop decoupled transfer functions of pich and yaw rotors. Then you can use GA MATLAB tool box setting the range of gains nearby the values you got from ZN method.
This paper might be useful to you.
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I have a multi-objective optimization with the following properties:
Objective function: thee minimization objective functions) two non-linear functions and one linear function
Decision variable: two real variables (Bounded)
Constraint: three linear constraint (two bounding constraint and one relationship constraint)
Problem type: non-convex
Solution required: Global optimum
I have used two heuristic algorithms to solve the problem NSGA-II and NSGA-III.
I have performed NSGA-II and NSGA-III for the following instances (population size, number of generations, maximum number of functional evaluations(i.e. pop size x no. of gen)): (100,10,1000), (100,50,5000),(100,100,10000), (500, 10, 1000), (500, 50, 25000), and (500,100,50000).
My observations:
Hypervolume increases with increase in number of functional evaluations. However, for a given population size, as the number of generation increases the hypervolume reduces. Which I think should rather increase. Why am I getting such an answer?
Greetings to you all.
Please how can I find MATLAB code for Accelerated Particle Swarm Optimization algorithm for tuning PID controller.
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