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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Does the policy search work if there is no state to state dependency through actions?
There is a game in which the state comes one after the other without depending on the agent's action. The agent gets a reward for its actions at the end of the game. The goal of the agent is to reach ...
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How does OpenAI-ES use Adam?
I just read that OpenAI's ES uses Adam: "OpenAI’s ES is denoted as “OptimES” (since it uses Adam optimizer)"?? I verified they are correct using the link they posted, (see es_distributed/...
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Should hyper-parameters used in the mutation operator be fixed or variable?
Let's say I have a function $f(p_{i,t})$ and want to optimize $p_{i,t}$ so that I minimize a certain loss. Here, $p_{i,t}$ is the individual $i$ at iteration $t$. Now, $p_{i,t}$ can live anywhere ...
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How to represent multiple-output logic circuits in tree-based genetic programming
Consider the following digital logic circuit, which has multiple inputs and one output:
The logic circuit above can be represented in tree form:
This tree representation could then be used in a tree-...
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neat - what is the purpose of looped networks?
So im writing my own implementation of NEAT and i'm wondering how looped
networks (like one shown in the image) can be useful. I'll probably implement
them anyway because i want to fiddle around with ...
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Would it be a good idea to mutate half of the offspring of each GA generation 100% of the time and the other half 0% of the time?
I was reading about genetic algorithms, and to my understanding a genetic algorithm (GA) is an algorithm that starts with an initial population of chromosomes, where each chromosome has associated ...
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Is there a benchmark for multi-objective evolutionary algorithms?
I'm working on a project for an evolutionary algorithms course, and the problem we're trying to solve is multi-objective. We'll use NSGA-II but we also wanted to compare with some other MOEAs, however,...
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What does "unknown search spaces" mean in the context of Evolutionary Algorithms?
In the article Multi-Verse Optimizer: a nature-inspired algorithm for global
optimization (DOI 10.1007/s00521-015-1870-7), it's written
The results of the real case studies also demonstrate the ...
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Evaluating species stagnation in NEAT
I'm exploring the NEAT algorithm, and among all of my questions on how to optimize my models, I was wondering how should I evaluate the stagnation of species in order to eventually extinct it.
...
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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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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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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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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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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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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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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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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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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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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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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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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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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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Isn't evolutionary theory the essence of intelligence after all? [closed]
The theory of evolution seems to be intelligent as it creates life
The mechanism of evolutionary theory consists of mutation, recombination, and natural selection like a genetic algorithm.
Isn't this ...
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Which 6-bit string would represent an optimal solution for trap-3 in the Linkage Learning Genetic Algorithm?
I am struggling to learn certain Evolutionary algorithm concepts and also relations between each of them. I am going through the Linkage Learning Genetic Algorithm (LLGA) right now and came across ...
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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 ...
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Choosing an AI method to recreate a given binary 2D image
If the title wan not very clear, I want a method to take an input image like this,
[[0, 0, 0, 0],
[1, 1, 1, 0],
[1, 1, 1, 0],
[0, 1, 1, 0]]
and output the 2D ...
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NEAT can't solve XOR completely
I'm currently implementing the NEAT algorithm. But problems occur when testing it with problems which don't have a linear solution(for example xor). My xor only produces 3 correct outputs once at a ...
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Artificial life simulator that is fully embodied and passes open endedness tests
Geb is an alife simulation that as far as I know passes all of the tests we have tried to come up with in defining open endedness. However, when you actually run the code, the behavioral complexity ...
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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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Crossover method for gene value containing a set of values
I have a chromosome where each gene contain s set of values. Like the following:
chromosome = [[A,B,C],[C,B,A],[C,D,],[],[E,F]]
The order in each gene values matters. (A,B,C is different to A,C,B)
...
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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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How to effectively crossover mathematical curves?
I'm trying to optimize some reflective properties of curves of the form:
$a_1x^n+a_2x^{n-1}+a_3x^{n-2} + ... + a_n + b_1y^n+b_2y^{n-1}+b_3y^{n-2} + ... + b_n = 0$
which is basically the curve that ...
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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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What is the difference between evolutionary computation and evolutionary algorithms?
A book on evolutionary computation by De Jong mentions both the term evolutionary algorithms (EA) as well as evolutionary computation (EC). However, it remains unclear to me what the difference ...
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What are evolutionary algorithms for topology and weights evolving of ANN (TWEANN) other than NEAT?
I wonder, if there are other than NEAT approaches to evolving architectures and weights of artificial neural networks?
To be more specific: I am looking for projects/frameworks/libraries that use ...
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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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Are there any strategies that would help me visualize the 'behavior space' and make a novelty function?
In “Abandoning Objectives: Evolution through the Search for Novelty Alone”, it is explained how the novelty search is a function that is domain specific, depending on the differing behaviors that can ...
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What is the difference between genetic algorithms and evolutionary game theory algorithms?
What is the difference between genetic algorithms and evolutionary game theory algorithms?
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What is the difference between evolutionary game theory and meta-heuristics?
Here is a list of meta-heuristic algorithms
Ant colony optimization,
Ant lion optimizer,
Artificial bee colony algorithm,
Bat algorithm,
Cat swarm optimization,
Crow search algorithm,
Cuckoo ...
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What is the name of an AI whose primary goal is to create a better AI?
A general AI x creates another AI y which is better than x.
y creates an AI better than itself.
And so on, with each generation's primary goal to create a better AI.
Is there a name for this.
By ...
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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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How to calculate multiobjective optimization cost for ordinary problems?
What I did:
Created a population of 2D legged robots in a simulated environment. Found the best motor rotation values to make the robots move rightward, using an objective function with Differential ...
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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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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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Why isn't the evolutionary Turing machine mainstream?
Given that recurrent neural networks are equivalent to a Turing machine, then why isn't the evolutionary Turing machine, e.g. described in the paper Evolution of evolution: Self-constructing ...
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Is there a rule-of-thumb to determine which behaviours must be learned in a lifetime and which innate?
I was training an AI to learn things during its lifetime such as find food and navigate a maze. Behaviors that might change during its lifetime.
But I hit upon a snag. Some behaviors, like avoiding ...
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How can I solve the linkage problem in genetic algorithms?
In a genetic algorithm, the order of the genes on a chromosome can have a significant effect on the performance (capacity to generate adaptation) of the genetic algorithm, where two or more genes ...
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Can we automate the choice of the hyper-parameters of the evolutionary algorithms?
Certain hyper-parameters (e.g. the size of the offspring generation or the definition of the fitness function) and the design (e.g. how the mutation is performed) of evolutionary algorithms usually ...
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Is there any research work that attempts to combine neuroevolution with deep reinforcement learning?
Neuroevolution can be used to evolve a network's architecture (and weights, of course). Deep reinforcement learning, on the other hand, has been proven to be extremely powerful at optimising the ...
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Exploding population size in neat-python
I am trying to make my AI win the board game "Catan" against my friends.
Therefore i am using the python implementation of NEAT.
As I changed the values of ...
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