Questions tagged [optimization]

For questions about implementing and improving optimization algorithms used in creating AI programs, or optimization in general.

Filter by
Sorted by
Tagged with
1
vote
1answer
25 views

How should I weight the factors that affect the choice of an action in a strategy board game with multiple actions?

I have written an AI that plays a strategy board game. There are lots of different types of moves (e.g. attack, defend, help ally colony, etc.). I calculate the best moves to do depending on a ...
2
votes
0answers
27 views

Is logistic regression used for unconstrained or constrained optimisation problems?

Is logistic regression used for unconstrained or constrained optimization problems, and why?
3
votes
1answer
151 views

What's the difference between RMSE and Euclidean distance, and when to use a custom loss? [closed]

I'm searching for a loss function that fits my project. Actually, I have two questions, but they are in the same direction. I take a look at the definition of the root mean squared error (RMSE) and ...
1
vote
0answers
99 views

How can I assign agents to tasks based on time and affinity?

I am working on an assignment problem. Consider $K$ agents $A_1, \dots A_K$ and $N$ tasks $T_1, \dots T_N$. Each task has a certain time $t(T_i)$ to be completed and each agent has a matching (or ...
1
vote
0answers
15 views

How does the automated temperature adjustment step work in Soft Actor-Critic?

In section 5 of the paper Soft Actor-Critic Algorithms and Applications, it is proposed an optimization problem to obtain an optimal temperature parameter $\alpha^*_t$. First, one uses the original ...
1
vote
0answers
48 views

Grey Wolf Optimization - Issue with Dimension [closed]

I'm trying to use the grey wolf optimization (GWO) for texts clustering. I used this code, https://github.com/7ossam81/EvoloPy-NN/blob/master/selector.py I tried using the dimension 30 for the GWO as ...
0
votes
0answers
14 views

Choosing best combinations from all possible combination expressions based few variables, unary operators, binary operators

I have a few financial variables of a stock universe like OHLC prices, volume, and other fundamentals with varying time-frequency. Using this set I'm creating an expression that gives the weights to ...
4
votes
1answer
49 views

What are examples of optimization problems that can be solved using a genetic algorithm?

I'm trying to learn how a genetic algorithm can solve optimization problems. I have already learned how a genetic algorithm can solve knapsack, TSP and set cover problems. I'm looking for some other ...
8
votes
1answer
374 views

What are the implications of the “No Free Lunch” theorem for machine learning?

The No Free Lunch (NFL) theorem states (see the paper Coevolutionary Free Lunches by David H. Wolpert and William G. Macready) any two algorithms are equivalent when their performance is averaged ...
5
votes
1answer
37 views

How can we conclude that an optimization algorithm is better than another one

When we test a new optimization algorithm, what the process that we need to do?For example, do we need to run the algorithm several times, and pick a best performance,i.e., in terms of accuracy, f1 ...
0
votes
2answers
79 views

LSTM network doesn't converge, what should be changed? [closed]

I'm testing out TensorFlow LSTM layer text generation task, not classification task; but something is wrong with my code, it doesn't converge. What changes should be done? Source code: ...
2
votes
1answer
32 views

Metrics of quality of parameter space exploration

Considering a black box optimization problem on non-linear, non-convex function where we want to minimize an objective function. One way to assess the quality of an optimizer is to look at the best ...
1
vote
0answers
30 views

why the sigmoid function will be 1 and 0 if we use a fully connected layer that produce a big enough positive(res negative )output

HI I am using a fully connected network that uses sigmoid if we feed a a big enough weights the sigmoid function will finally become 1 or 0 , is there any solution to avoid this ? and will this lead ...
3
votes
1answer
67 views

Advantages of Kullback-Leibler over L1/L2?

I've recently encounter different articles that are recommending to use KL instead of L1/L2 norm when trying to minimize a probability distribution. But none of the articles are giving a clear ...
1
vote
1answer
39 views

Create optimizer object using the tf.keras.optimizers.get function

I am trying to have the type of optimizer as a variable in the hyperparameter tuning phase. For that reason I am trying to use the tf.keras.optimizer.get function ...
-3
votes
3answers
229 views

Evolutionary neural architecture?

I'm working on an idea for an AI architecture, and would like to know if there are any apparent flaws, or if there is prior work in this vein. Set I/O so that the neural network can read and write ...
2
votes
0answers
48 views

Does Retina-net's focal loss accomplish its goal?

Taking out the weighting factor we can define focal loss as $$FL(p) = -(1-p)^\gamma log(p) $$ Where $p$ is the target probability. The idea being that single stage object detectors have a huge ...
1
vote
0answers
28 views

Is convergence to a local minima more likely with transfer learning?

While doing transfer learning where my two problems are face-generation and car-generation is it likely that, if I use the weights of one problem as the initialization of the weights for the other ...
0
votes
0answers
13 views

Derivation for Value Iteration of CVaR

I am reading a paper named Risk-sensitive and Robust Decision-making: a CVaR Optimization Approach. In appendix A.3 they provide a proof for their Theorem $4$. The $n=1$ case for equation (11) is ...
1
vote
0answers
52 views

Could the Jensen-Shannon divergence and Kullback-Leibler divergence be used as loss functions of non-generation problems?

If I understand correctly, the KL divergence is a measure of information loss between a ground truth distribution $P$ and a predicted distribution $Q$, and the Jensen-Shannon divergence is the mean of ...
4
votes
2answers
92 views

Can we optimize an optimization algorithm?

In this answer to the question Is an optimization algorithm equivalent to a neural network?, the author stated that, in theory, there is some recurrent neural network that implements a given ...
0
votes
1answer
48 views

Query regarding the minmax loss function formulation of the training of a Generative Adversarial Network (GAN)

Just needed a clarification on the training procedure for a standard GAN. Of my understanding the loss function to optimize is a min max (max min causing mode collapse due to focus on one class ...
1
vote
1answer
55 views

Is an optimization algorithm equivalent to a neural network?

Is an optimization algorithm equivalent to a neural network?
3
votes
1answer
47 views

Is it possible to have a dynamic $Q$-function?

I am trying to use Q-learning for energy optimization. I only wish to have states that will be visited by the learning agent, and, for each state, I have a function that generates possible actions, so ...
2
votes
0answers
46 views

Is a neural network the correct approach to optimising a fitness function in a genetic algorithm?

I've written an application to help players pick the optimal heroes during the draft phase of the Heroes of the Storm MOBA. It can be daunting to pick from 80+ characters that have synergies/counters ...
2
votes
0answers
32 views

Can we use the Tierra approach to optimize machine code?

Thomas Ray's Tierra is a computer program which simulates life. In the linked paper, he argues how this simulation may have real-world applications, showing how his digital organisms (computer ...
0
votes
0answers
23 views

Custom optimizer and word-vector evaluator lstm

I’m using Keras LSTM layers and building a model that is trained off ethics text. I have a problem of often over fitting (the network basically remembers my input corpus as it is very small). I was ...
1
vote
1answer
72 views

How does NEAT find the most successful generation without gradients?

I'm new to NEAT, so, please, don't be too harsh. How does NEAT find the most successful generation without gradient descent or gradients?
2
votes
2answers
67 views

Why is a mix of greedy and random usually “best” for stochastic local search?

I read that a mix of "greedy" and "random" are ideal for stochastic local search (SLS), but I'm not sure why. It mentioned that the greedy finds the local minima and the randomness avoids getting ...
1
vote
0answers
14 views

Estimating Baselines using ALS

I am trying to figure out how ALS works when minimizing the following formula: $\\ \\$ $\text{min}_{\lbrace b_u,b_i \rbrace} \sum_{(u,i)\in \mathcal{K}} (r_{ui} - \bar{r} - b_u - b_i )^2 + \lambda_{...
1
vote
1answer
38 views

How to properly optimize shared network between actor and critic?

I'm building an actor-critic reinforcment learning algorithm to solve environments. I want to use a single encoder to find representation of my environment. When I share the encoder with the actor ...
0
votes
2answers
59 views

How can the A* algorithm be optimized?

How can the A* algorithm be optimized? Any references that shows the optimization of A* algorithm are also appreciated.
2
votes
1answer
54 views

How can we reach global optimum?

Gradient descent can get stuck into local optimum. Which techniques are there to reach global optimum?
0
votes
1answer
322 views

Why isn't the reverse KL divergence commonly used in supervised learning?

Forward KL Divergence (also known as cross entropy loss) is a standard loss function in supervised learning problems. I understand why it is so: matching a known a trained distribution to a known ...
0
votes
0answers
54 views

Double Convolution Layers in Yolov3

Lately, I have been working on yolov3 and have been trying to train it on x-ray images to detect a fracture. However, I have decided that I would want to increase the number of convolution layers for ...
0
votes
1answer
74 views

How could we solve the TSP using an hill-climbing approach?

How could we solve the TSP using an hill-climbing approach?
5
votes
1answer
231 views

When should we use algorithms like Adam as opposed to SGD?

As far as I know, Stochastic Gradient Descent is an optimization algorithm which belongs to the the category of algorithms where hyper-parameters have to be defined beforehand. They are useful in many ...
5
votes
1answer
644 views

How can we use linear programming to solve an MDP?

Apparently, we can solve an MDP (that is, we can find the optimal policy for a given MDP) using a linear programming formulation. What's the basic idea behind this approach? I think you should start ...
2
votes
1answer
42 views

Recent Python Packages for Random Search Optimization

Which python packages do you recommend on random search optimization to use? Is there any recent and good one (better than the one in Sci-kit)?
1
vote
0answers
17 views

What is the purpose of the new neurons in the constrained neural network?

I would like to train a constrained neural network. I found a paper on this: https://papers.nips.cc/paper/4-constrained-differential-optimization.pdf. However, I don't really understand how to change ...
0
votes
0answers
22 views

Feature selection optimization and hyperparameters optimization for one model

Question is purely theoretical. I am desiging a machine learning model for classification purposes. I am using GridSearch optimization method to select best hyperparameters and I have written ...
2
votes
1answer
115 views

Training an artificial neural network using PSO

Hi guys I've been studying such combination where the idea is to replace the classic descendant gradient for an algorithm that is less sensitive to local optimum, so the PSO tries to select the best ...
1
vote
1answer
39 views

Which local minima to choose according to the shape of the error surface?

The following plot shows error function output based on system weights. Two equal local minima are shown in green pointers. Note that the red dots are not related to the question. Considering the ...
0
votes
1answer
52 views

Python Packages for Recent Optimization Methods

I want to try and compare different optimization methods in some datasets. I know that in scikit-learn there are some corresponding functions for grid and random search optimizations. However, I also ...
3
votes
2answers
60 views

Could error surface shape be useful to detect which local minima is better for generalization?

The following plot shows error function output based on system weights. Two equal local minima are shown in green pointers. Note that the red dots are not related to the question. Does the right one ...
1
vote
0answers
20 views

Any guidance on learning rate / batch size for noisy data (high Bayes error rate)?

Is there any guidance available for training on very noisy data, when Bayes error rate (lowest possible error rate for any classifier) is high? For example, I wonder if deliberately (not due to memory ...
2
votes
0answers
77 views

Is a very powerful oracle sufficient to trigger the AI singularity?

Lets say we have a oracle $S$ that, given any function $F$ and desired output $y$, can find an input $x$ that causes $F$ to output $y$ if it exists, or otherwise returns nil. I.e.: $$S(F, y) = x \...
1
vote
0answers
19 views

Feature visualization on neural networks which are not for classification

Feature visualization allows to better understand neural networks by generating images that maximize the activation of a specific neuron, and therefore understand what are the abstract features that ...
2
votes
1answer
52 views

Method to check goodness of combinatorial optimization algorithm implementation

How do I check which algorithm solves my problem best? Given a optimaization problem, I apply different well known optimization algorithms (genetic algorithm, simulated annealing, ant colony etc.) to ...
-2
votes
1answer
723 views

RNN LSTM not converging with Adam

I am trying to train a RNN with text from wikipedia but I having having trouble getting the RNN to converge. I have tried increasing the batch size but it doesn't seem to be helping. All data is one ...