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Questions tagged [optimization]

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

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1answer
22 views

Neural Network Optimizers in Reinforcement Learning non-well behaved environments

https://stackoverflow.com/questions/36162180/gradient-descent-vs-adagrad-vs-momentum-in-tensorflow Here, the nice gifs explain how different algorithms approach towards the root. Unfortunately, the ...
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1answer
33 views

Genetic Algorithm vs Particle Swarm Optimization

Which one gives better optimization results? Genetic Algorithm or Particle Swarm Optimization? Can I use them for online tuning problems? Thanks in advance!
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0answers
9 views

What does it essentially mean if the neural network has convex error surface?

Suppose if I am building a Linear Regression model with one fully connected layer and a sigmoid with minimizing mean squared error as objective. Why would the error surface be convex? Does finding ...
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1answer
30 views

Reinforcement Learning to Grouped Scheduling Optimisation Problem

I am not sure the name of this kind of problem, but anyway, the situation is as below. Assign teachers into Groups and consider on each of their workload, availability etc. There are some other soft/...
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4answers
178 views

What is the actual learning algorithm: back-propagation or gradient descent?

What is the actual learning algorithm: back-propagation or gradient descent (or, in general, the optimization algorithm)? I am reading through chapter 8 of Parallel Distributed Processing hand book ...
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1answer
19 views

Way to control movement and coverage in an embedded AI cleaning system?

There's the need to design a horizontal plane cleaning system that is controlled by positioning servos. Two in two of three floor rollers and three in the x, y, and z positioning of a wiping device. ...
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2answers
35 views

Optimization of many vs few features

Let's say there's an optimization problem of 10 elements. If I try to optimize it, I'll get to some result. (***) My question is, if somehow I start optimizing while having 3 elements already solved ...
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1answer
11 views

AI that maximizes the storage of rectangular parallelepipeds in a bigger parallelepiped

As you can see in the title, I'm trying to program an AI in Java that would help someone optimize his storage. The user has to enter the size of his storage space (a box, a room, a warehouse etc...) ...
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1answer
22 views

What are the advantages and disadvantages of using LISP for constraint satisfaction in 3D space

We are currently working on developing a 3D modeling software that allows designers to set spatial constraints to models. The computer then should generate a 3D mesh conforming to these constraints. ...
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0answers
26 views

Is anyone extending torch.optim.Optimizer for Nesterov-accelerated and non-constent β ADAM?

Varieties of stochastic gradient descent are currently dominant in deep learning engineering. Training efficiency can be optimized by beginning with a high learning rate and tapering it to a lower ...
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1answer
66 views

How to optimize a function using a genetic algorithm?

I recently learned about Genetic algorithms and I solved the 8 queens problem using a genetic algorithm but I don't know how to optimize any functions using a genetic algorithm. I want a guide on how ...
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1answer
29 views

What is the difference between the study of Evolutionary algorithm vs. Optimization?

I have a course named "Evolutionary Algorithm". But, our teacher is always mentioning the word "Optimization" in his lectures. I am confused. Is he actually teaching Optimization? If yes, why is the ...
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1answer
57 views

Is a calculus or ML approach to varying learning rate as a function of loss and epoch been investigated?

Many have examined the idea of modifying learning rate at discrete times during the training of an artificial network using conventional back propagation. The goals of such work have been a balance ...
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1answer
510 views

Loss jumps abruptly when I decay the learning rate with Adam optimizer in PyTorch

I'm training an auto-encoder network with Adam optimizer (with amsgrad=True) and ...
3
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1answer
40 views

Genetic Algorithms: Trade-off between time and variance with regards to fitness function

I'm developing an AI to play a card game with a genetic algorithm. Initially, I will evaluate it against a player that plays randomly, so there will naturally be a lot of variance in the results. I ...
2
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1answer
29 views

How do I compute log-likelihood for training set in supervised learning?

I am building a supervised learning model and I wish to compute the log-likelihood for the training set at the point of the minimum validation error. Initially, I was computing the sum of all the ...
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0answers
26 views

Optimization step in Apprenticeship Learning via Inverse Reinforcement Learning

Why the optimization step of the algorithm a quadratic program? [See: Apprenticeship Learning via Inverse Reinforcement Learning; page 3] Isn't the objective function linear? Why don't we treat ...
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1answer
63 views

Quiescence search

Games like checkers have compulsory moves. In checkers for instance, if there's a jump available a player must take it over any non-jumping move. My question is, if jumps are compulsory will there ...
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2answers
50 views

Is an evaluation function as good as an optimization function

I have been so for self-learning basic A.I concepts and would like to know if having a really good evaluation function as good as any of alpha-beta pruning optimization functions such as killer moves, ...
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0answers
29 views

Gradient of boltzmann policy over reward function

I'm struggling with an inverse reinforcement learning problem which seems to appear quite often around the literature, yet I can't find any resources explaining it. The problem is that of calculating ...
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0answers
55 views

reinforcement learning rmsprop does not improve, average reward through time oscillates

I have a reinforcement learning project using policy gradient method with rmsprop optimization. (used vanilla REINFORCE algorithm)(the game is a simple pong game in open-ai gym atari environment) the ...
2
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1answer
101 views

If Deep Learning is non convex, then why use convex loss?

I was just reading through some convex optimization textbooks to hopefully improve my deep learning understanding and come up with new ideas. Halfway through, I decided to Google a bit! It's obvious ...
0
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1answer
32 views

Can TensorFlow minimize “symbolically”

From https://stackoverflow.com/questions/36370129/does-tensorflow-use-automatic-or-symbolic-gradients, I understood TensorFlow requires all the operations in the Graph to be explicit formulas (instead ...
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2answers
94 views

Input optimization on a supervised learning system

Problem Given a collection of pairs (X, y) where X belongs to R^n and y belongs to R, find the X such that the associated y would be maximum. Example Given: (X=(1, 2), y=-9) (X=(-2, 4), y=-36) (X=...
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1answer
131 views

Why does a one-layer hidden network get more robust to poor initialization with growing number of hidden neurons?

In a nutshell: I want to understand why a one hidden layer neural network converges to a good minimum more reliably when a larger number of hidden neurons is used. Below a more detailed explanation of ...
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1answer
28 views

How to use MOPSO to align characters vertically?

I need to efficiently align characters vertically using Multi Objective PSO. Alignment is achieved by adding spaces in between a given set of characters. ...
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2answers
3k views

Time complexity for training a Neural Network

So, I was wondering: what is the time complexity of NN? Say we take the simple case of the back-propagation algorithm with n hidden layers, ...
4
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1answer
300 views

Why number of hidden units in a layer are suggested to be in powers of 2?

It is suggested that the number of hidden units in a layer should be in powers of 2 because it helps converge faster. Is it a fact and if it is, how this helps the NN learn faster. Does it have to do ...
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0answers
34 views

How to calculate Adaptive gradient?

In the FaceNet paper there mentions an gradient algorithm called 'AdaGrad'(Adaptive Gradient) referenced to this paper which has apparently been used to calculate the gradient of the Triplet Loss ...
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0answers
167 views

Application of Ai to task scheduling problems on heterogenous platforms

Let's say we have a cluster of 20-2000 heterogenous compute nodes. Consider for example the parallel solution of the helmholtz equation: Now we want to distribute the solution process and, to make ...
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1answer
33 views

Should the mutation be applied with the hill climbing algorithm

As far as I understand, the hill climbing algorithm is a local search algorithm that selects any random solution as an initial solution to start the search. Then, should we apply an operation (i.e., ...
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0answers
75 views

Which features and algorithm could optimize this air-conditioner problem?

Imagine we have 2 air conditioner systems (AA) and 2 "free cooling" systems which mix external and internal air (FC) in a closed box which always tends to warm up. For each system, we have to find ...
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0answers
38 views

Which algorithm would you use to solve a multiple producer-consumer problem with constraints?

I'm solving this problem similar to consumer-producer of materials (i.e. sand). This is the graph of the problem: Where Req (...
7
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1answer
173 views

Why does 'loss' change depending on the number of epochs chosen?

I am using Keras to train different NN. I would like to know why if I increment the epochs in 1, the result until the new epoch is not the same. I am using shuffle=False, and np.random.seed(2017), and ...
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1answer
604 views

Why is the merged neural network of Alpha Go Zero more efficient than two separate neural networks?

Alpha Go Zero contains several improvements compared to its predecessors. Architectural details of Alpha Go Zero can be seen in this cheat sheet. One of those improvements is using a single neural ...
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0answers
72 views

Problems getting ADADELTA to converge

I have followed the pseudocode in the ADADELTA paper (top right on page 3), and wrote the following Python code for solving the optimization problem L(x) = x^2: ...
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1answer
1k views

What is the difference between Memetic Algorithms and Genetic Algorithms?

Can someone please explain the difference between Memetic Algorithms and Genetic Algorithms? Is an indivudal's lifetime learning part of memetic algorithms?
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0answers
63 views

Reproduce Firefly Algorithm experiments of original paper?

I have been trying to reproduce the experiments done in the original: "Firefly Algorithm for multimodal optimization" (linked in the question) so far: unsuccesfully. For the moment being I'm okay if ...
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0answers
31 views

Knapsack of mixture with constraints

I'm trying to find the optimized mixture for a specific set of substances. Each of those substances have characteristics that I want to optimize in the mixture (some characteristics I want to minimize ...
2
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1answer
70 views

How to find proper parameter settings for a given optimization algorithm?

Is there any methodology to find proper parameter settings for a given meta-heuristic algorithm, eg. Firefly Algorithm or Cuckoo Search? Is this an open issue in optimization? Is extensive ...
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3answers
80 views

Would a sentient AI try to create a more optimised AI which would eventually overtake AI 1.0?

Would AI be a self-propogating iteration in which the previous AI is destroyed by a more optimised AI child? Would the AI have branches of it's own AI warning not to create the new AI?
2
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1answer
686 views

Are FFNN (MLP) Lipschitz functions?

My question is regarding standard dense-connected feed forward neural networks with sigmoidal activation. I am studying Bayesian Optimization for hyper-parameter selection for neural networks. There ...
8
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1answer
391 views

What are Hyper-heuristics?

I wanted to know what the differences between hyper-heuristics and meta-heuristics are, and what their main applications are. Which problems are suited to be solved by Hyper-heuristics?
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2answers
52 views

Is there a way to define the boundaries of the optimal size of a training set?

At a related question in Computer Science SE, a user told: Neural networks typically require a large training set. Is there a way to define the boundaries of the "optimal" size of a training set ...
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2answers
185 views

Can artificial intelligence be thought of as optimization?

In this video an expert says, "One way of thinking about what intelligence is [specifically with regard to artificial intelligence], is as an optimization process." Can intelligence always be thought ...
3
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1answer
62 views

What are the methods of optimizing overfitted models?

I'm worrying that my network has become too complex. I don't want to end up with half of the network doing nothing but just take up space and resources. So, what are the techniques for detecting and ...