Questions tagged [evolutionary-algorithms]

For questions about evolutionary algorithms (EAs), which use mechanisms inspired by biological evolution, such as mutation, recombination, and/or selection. EAs comprise genetic algorithms (GAs), genetic programming (GP), evolution strategies (ES), neuroevolution (NE), and so on. EAs are a sub-field of evolutionary computation, which comprises also ant colony optimization or particle swarm optimization, among others.

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1answer
39 views

When would you use Evolutionary Strategies over Step-Based Reinforcement Learning

In Salimans et al, 2016, the authors argue that ES should be considered a competitive alternative to MDP-based RL algorithms like Q-Learning, TRPO. However, in practice, I notice that more often than ...
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115 views

How about creating artificial intelligence in this way?

DeepMind-generally-capable-agents-emerge-from-open-ended-play Deepmind is saying "We then use population based training (PBT) to adjust the parameters of the dynamic task generation based on a ...
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How to create an environment(minigames) to train an agent?

deepmind-generally-capable-agents-emerge-from-open-ended-play They say training many mini-games results in universal intelligence. According to deepmind's research, they algorithmically create ...
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999 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 ...
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0answers
28 views

Similarities Between Evolutionary Algorithms and Reinforcement Learning [duplicate]

Evolutionary algorithms use the fitness function to score agents and tend to choose the one with the high score. This tends to maximize the score of surviving agents. Doesn't reinforcement learning ...
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132 views

If it evolves to perform many missions, won't it become a universal intelligence? [closed]

The more problems an agent can solve, the higher the probability of natural selection. Isn't this the process by which humans have become general-purpose intelligence by solving many problems in ...
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1answer
48 views

Why do all nodes in a GP tree need to be the same type?

Context: I'm a complete beginner to evolutionary algorithms and genetic algorithms and programming. I'm currently taking a course about genetic algorithms and genetic programming. One of the concepts ...
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79 views

What is the difference between ERL and EA by considering it as RL?

I am currently studying as an MSCS student and my research is based on Evolutionary Algorithm as Reinforcement Learning, and I am confused about the following terms: What is the difference between ...
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1answer
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Does elitism cause premature convergence in genetic algorithms?

I have a genetic algorithm which is working fairly well. It's got all the standard operators, including initial random population, crossover ratio, mutation rate, degree of mutation, etc. This works ...
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1answer
17 views

What approach would work well for predicting earthquake intensity based on historical data?

My problem: I own warning system where I collect data from institutions and send them over through various ways to users. I would like to hear your advice on what approach I can use for solving my ...
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1answer
45 views

Is there a name for this approach to evolutionary algorithms?

I am considering an approach to evolutionary algorithms, in which instead of maintaining a population of individuals, we maintain a pool of $N$ mutations that can be applied to a base genome. For ...
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1answer
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How to handle equality constraints in the mutation operation of evolutionary algorithms?

I am new in evolutionary algorithms field. I have a chromosome of 6 variables (real variable), where the sum of these variables is equal to 1. I am looking for mutation formulas that can generate a ...
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1answer
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What is the most computationally efficient genetic algorithm?

In researching genetic algorithms, it seems that there are various methods of selection and other operator methods that can significantly change the performance. For example, this picture contains ...
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260 views

What is meant by gene, chromosome, population in genetic algorithm in terms of feature selection?

I am trying to understand the genetic algorithm in terms of feature selection and these features are extracted using a machine learning algorithm. Let's suppose I have data of heart rate for 3 minutes ...
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1answer
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How can genetic programming be used for path planning?

I have been reading quite a few papers, on genetic programming and its applications, in particular, chapter 10 of "Genetic Programming: An Introduction and Tutorial, with a Survey of Techniques ...
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1answer
59 views

Does a differential evolution algorithm mutate its population during a generation?

I'm implementing a differential evolution algorithm and when it comes to evolving a population, the page I am referencing is vague on how the new population is generated. https://en.wikipedia.org/wiki/...
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Clonal operator in Immune Clonal Strategy

I was reading about Immune Clonal Strategy, specifically about Monoclonal operator from Immunity clonal strategies, and it goes as follows: Here $a_i $ is a point and $a_i = \{ x_1, x_2, \cdots, x_m \...
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1answer
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Is a neural network an evolutionary algorithm? [closed]

Is a neural network not just an evolutionary algorithm with increased amount of parameters to represent, and optimize a problem in the world?
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How to select good inputs and fitness function to achive good results with NEAT for Icy Tower bot

I'm trying to make a bot to the famous "Icy Tower" game. I rebuilt the game using pygame and I'm trying to build the bot using Python-NEAT. Every generation a population of 70 characters ...
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Is Universal Sentence Encoder helping producing supervised or not summaries?

I am currently working on generating automatic summaries of scientific texts and am wondering whether using Google's Universal Sentence Encoder makes my approach data-driven or supervised. I am doing ...
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1answer
208 views

What is the difference between a fitness function and a reward function?

In reinforcement learning (RL), the reward function (RF), which can be denoted as $r(s)$, $r(s, a)$, $r(s, a, s')$, $r(s, s')$ depending on its specific definition, provides the learning signal, which ...
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1answer
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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 ...
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1answer
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How do I write a good evaluation function for a board game?

I'm currently writing the Alpha-Beta pruning algorithm for a board game. Now I need to come up with a good evaluation function. The game is a bit like snakes and ladders (you have to finish the race ...
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6answers
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Open-source tools or libraries to experiment with neural networks and evolutionary algorithms [closed]

I'd like to do some experimenting with neural net evolution (NEAT). I wrote some GA and neural net code in C++ back in the 90s just to play around with, but the DIY approach proved to be labor-...
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1answer
840 views

Does fitness proportionate selection select multiple individuals?

Does fitness proportionate selection select multiple individuals? So, I read on Wikipedia and on multiple Stack Exchange threads about fitness proportionate selection or rather roulette selection, but ...
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How to create a good fitness function?

In genetic algorithms, a function called "fitness" (or "evaluation") function is used to determine the "fitness" of the chromosomes. Creating a good fitness function is one of the challenging tasks in ...
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1answer
99 views

How do I design a fitness function that weighs the importance of eating food?

Summary: I am teaching bots to pick food on a playing field. Some food is poisonous and some is good. Food Details: Poisonous food subtracts score points and good food adds. Food points vary based on ...
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1answer
58 views

Are Genetic Algorithms suitable for problems like the Knuth problem?

We all know that Genetic Algorithms can give an optimal or near-optimal solution. So, in some problems like NP-hard ones, with a trade-off between time and optimal solution the near-optimal solution ...
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1answer
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Why evolutionary training of neural networks is not popular?

Evolutionary algorithms are mentioned in some sources as a method to train a neural network (finding weights, not hyperparameters). However, I have not heard about one practical application of such an ...
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3answers
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Is it possible to separately evolve a part of the population?

In a classic example of a genetic algorithm, you would have a population and a certain amount of simulation time to evaluate it and breeding. Then proceed to the next generation. Is it possible, ...
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1answer
241 views

Have evolutionary algorithms been used for engineering design?

Recently, I've been looking recently into what uses AI - specifically evolutionary algorithms - may have in automating engineering design. For a long time, there have been algorithms that solve ...
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1answer
275 views

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

I'm trying to learn how genetic algorithms can solve optimization problems. I have already learned how genetic algorithms can solve the knapsack, TSP and set cover problems. I'm looking for some other ...
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1answer
186 views

What evolutionary algorithms are there that model epigenetics?

What evolutionary algorithms are there that model or incorporate some notion of epigenetics? What are the pros/cons of those approaches? Are there vast insufficiencies or wide-open questions about ...
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5answers
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What exactly are genetic algorithms and what sort of problems are they good for?

I've noticed that a few questions on this site mention genetic algorithms and it made me realize that I don't really know much about those. I have heard the term before, but it's not something I've ...
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Neuroevolution + RL: How to make sure my policies are more diverse?

I currently implemented Deep Neuroevolution and used it on a couple of Atari games. For my implementation I used a similar Genetic Algorithm, network and setup as the Uber AI Deep Neuroevolution paper ...
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1answer
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What is a "codon" in grammatical evolution?

The term codon is used in the context of grammatical evolution (GE), sometimes, without being explicitly defined. For example, it is used in this paper, which introduces and describes PonyGE 2, a ...
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2answers
336 views

Do genetic algorithms also evolve?

After witnessing the rise of deep learning as automatic feature/pattern recognition over classic machine learning techniques, I had an insight that the more you automate at each level, the better the ...
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1answer
719 views

How to represent the weights of a neural network as binary strings for a genetic algorithm?

I want to train my neural network by evolution, that is I want to recombine the weights of the best performing neural networks in each evolution cycle or generation. My initial instinct was to ...
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6answers
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How to evaluate a NEAT neural network?

I'm trying to write my own implementation of NEAT and I'm stuck on the network evaluate function, which calculates the output of the network. NEAT as you may know contains a group of neural networks ...
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2answers
980 views

What is a trap function in the context of a genetic algorithm?

What is a trap function in the context of a genetic algorithm? How is it related to the concepts of local and global optima?
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1answer
71 views

What are trap functions in genetic algorithms? [duplicate]

What are trap functions in genetic algorithms? Suppose you ran a GA with a trap function and examined the population midway through the run. Can someone explain what you would expect the population to ...
3
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1answer
138 views

How can a genetic algorithm adapt and get better in a changing environment?

I've just started studying genetic algorithms and I'm not able to understand why a genetic algorithm can improve if, at each learning, the 'world' that the population encounters change. For example, ...
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Why is cross-over a part of genetic algorithms?

Genetic Algorithms has come to my attention recently when trying to correct/improve computer opponents for turn-based strategy computer games. I implemented a simple Genetic Algorithm that didn't use ...
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2answers
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Are there clever (fitness-based) crossover operators for binary chromosomes?

While studying genetic algorithms, I've come across different crossover operations used for binary chromosomes, such as the 1-point crossover, the uniform crossover, etc. These methods usually don't ...
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1answer
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Is elitism preferred over non-elitism in the cross-over operator?

There are two potential approaches when performing cross-over operation in genetic algorithms. Use only the elites in the pool, probably the ones that are also going to be directly transferred to the ...
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1answer
256 views

What are the available selection methods in genetic algorithms?

In a genetic algorithm, there are different steps. One of those steps is the selection of chromosomes for reproduction. What are the available selection strategies in genetic algorithms?
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What is the difference between "mutation" and "crossover"?

In the context of evolutionary computation, in particular genetic algorithms, there are two stochastic operations "mutation" and "crossover". What are the differences between them?
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1answer
785 views

Are there any machine learning techniques to detect coding standard violations?

Are there any machine learning techniques (such as deep learning or evolutionary algorithms) to detect coding standard violations? Which one would be more suitable? I don't have any specific ...
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1answer
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What is the difference between sensitivity analysis and parameter tuning?

I tried different values of genetic algorithm operators: many crossover rates from 20% to 80% many crossover rates from 1% to 20% varying the population size The study of different parameter values ...
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2answers
643 views

How do I optimize a specific 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. $$ \begin{array}{r} \...