# Questions tagged [optimization]

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

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### Use Reinforcement Learning instead of genetic algorithm for optimization

I want to use RL instead of genetic or any other evolutionary algorithm in order to find the best parameter for a function. Here is the problem: Given a function $$f(x,y,z,data)$$ x,y and z are some ...
0answers
31 views

### Optimizer that prevents parameters from oscillating

When we perform gradient descent, especially in an online setting where the training data is presented in a non-random order, a particular 1-dimensional parameter (such as an edge weight) may first ...
0answers
26 views

### What are some use cases of discrete optimization in Deep Learning?

When we talk of optimization, it usually boils down to gradient descent and its variants in the context of deep learning. However, I wonder if there are some works that use discrete optimization in ...
0answers
33 views

### What are the best optimizations I can add to my neural network?

I am making an artificial neural network from scratch (without nn libraries) in python. So, as you can guess, its extremely unoptimized and slow. For this neural ...
1answer
51 views

### How to avoid being stuck local optima in q-learning and q-network

When using Bellman equation to update q-table or train q-network to fit to greedy max values, the q-values very often get to the local optima and get stuck although randomisation rate ($\epsilon$) ...
1answer
50 views

### How to deal with evolutionary/genetic fitness function that can have both negative and positive values?

I am optimising function that can have both positive and negative values in pretty much unknown ranges, might be -100, 30, 0.001, or 4000, or -0.4 and I wonder how I can transform these results so I ...
0answers
52 views

### Why are most commonly used activation functions continuous?

I have come to notice that the most commonly used activation functions are continuous. Is there any specific reason behind this? Results such as this paper have worked on training networks with ...
0answers
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2answers
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### How can I model this problem of delivering assets by choosing a route with reinforcement learning?

I would like to build a model based on reinforcement learning (RL) for the following scenario Recommend the best route (of cities listed for a given country) that satisfies the required criteria (...
1answer
115 views

### Can a machine learning approach solve this constrained optimisation problem?

I had done with different classification, regression and clustering approaches for predictions of values, etc. I was wondering if there is a machine learning approach for distribution of a whole based ...
1answer
76 views

### What is the difference between simulated annealing and deterministic annealing?

Not sure if this is the right place, but I was wondering if someone could briefly explain to me the differences & similarities between simulated annealing and deterministic annealing? I know that ...
0answers
37 views

### How many training runs are needed to obtain a credible value for performance?

I'm trying to optimize a neural network. For that, I'm changing parameters like the batch size, learning rate, weight initialization, etc. A neural network is not a deterministic algorithm, so, in ...
1answer
383 views

### What is the difference between reinforcement learning and evolutionary algorithms?

What is the difference between reinforcement learning (RL) and evolutionary algorithms (EA)? I am trying to understand the basics of RL, but I do not yet have practical experience with RL. I know ...
0answers
93 views

### How does SGD escape local minima?

SGD is able to jump out of local minima that would otherwise trap BGD I don't really understand the above statement. Could someone please provide a mathematical explanation for why SGD (Stochastic ...
1answer
51 views

### Which one is more important in case of different loss optimization algorithms, Speed or the Route?

We have different kinds of algorithms to optimize the loss like AdaGrad, SGD + Momentum, etc. Some are more commonly used than the others. In some algorithms, they usually range out before they ...
0answers
44 views

### Simplifying Log Loss

I am reading through a paper (https://www.mitpressjournals.org/doi/pdf/10.1162/0891201053630273) where they describe logloss as a ranking function and can be simplified to the margin of the training ...
0answers
23 views

### Which of these two strategies is the best to select solutions in simulated annealing?

I am using simulated annealing (SA) for an NP-hard combinatorial optimisation problem. 1) I am testing over a range of problem instances in which the objective values can be in the 100's or in the ...
0answers
25 views

### What are swarm optimization techniques used for: training the ANN by weight optimization or for feature selection?

Swarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. The concept is employed in work on artificial intelligence. SI-based algorithms, ...
0answers
101 views

### Can neural networks handle redundant inputs?

I have a fully connected neural network with the following number of neurons in each layer [4, 20, 20, 20, ..., 1]. I am using TensorFlow and the 4 real-valued ...