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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46 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 ...
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48 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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103 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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Apart from grammatical evolution, what are other examples of grammar-guided genetic programming approaches?

Grammatical Evolution (GE) is a well-known grammar-guided genetic programming (GGGP) approach, where we have genotypes (aka genomes or chromosomes) that are arrays (or lists) of integers (or groups of ...
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17 views

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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49 views

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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27 views

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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61 views

Isn't evolutionary theory the essence of intelligence after all?

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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27 views

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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62 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 ...
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35 views

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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How are the step size and covariance matrix updated in CMA-ES?

I've been following the tutorial The CMA Evolution Strategy: A Tutorial to try and understand the CMA-ES, but I'm having trouble understanding how the step size and the covariance matrix are been ...
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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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If artificial neural networks are a special case of computation graphs, so maybe let's optimize computational graphs rather than neural networks?

Existing ANNs are so good for solving complicated tasks on a different domain of data. But creating a neural network is always a hassle and its success mostly relies on an engineer's intuition and ...
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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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73 views

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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67 views

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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1answer
48 views

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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1answer
81 views

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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1answer
390 views

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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196 views

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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32 views

What qualifies as 'fitness' for a genetic algorithm that minimizes an error function?

Suppose I have a set of data that I want to apply a segmented regression to, fitting linearly across the breakpoint. I aim to find the offsets and slopes of either line and the position of the ...
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316 views

Can we evolve 0 and 1?

Is it possible to combine or create conditional statements of 0 and 1, and optimize with an evolutionary algorithm (given that all computers use a binary system)? There may be an algorithm that maps ...
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2answers
120 views

How to reduce amount of species in NEAT?

I am using the following library: https://github.com/vishnugh/evo-NEAT which seems to be a pretty simple NEAT-implementation. Therefore I am using the following Config: ...
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How do I choose an appropriate fitness function and hyper-parameters to train a 7-DOF arm?

I am trying to train an ANN to control a 7 Degrees-Of-Freedom arm. It should reach a target avoiding a single obstacle. Given my modeling of the situation, my input layer is composed of 12 nodes: 5 ...
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Shortest route GA: One loop through one dataset vs multiple loops through subsets of the same data?

I've a rather simple question for a school project. We're developing a GA solution for the following problem: Chromosome: A location with lat-lon coords. There are two types of locations - up to 15 ...
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76 views

Is there a reason evolutionary algorithms are language-bound in research material?

I've been working on genetic algorithms & evolutionary strategies for a while now in a research context. Across the vast majority of the articles and content I've read, every single one of them ...
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315 views

Can neural networks evolve other neural networks?

Can neural networks change or evolve other neural networks? Also, could evolutionary algorithms be applied to evolve neural networks? For example, suppose that we have neural networks A and B. The ...
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58 views

How accurate are neuroevolution algorithms in modelling organism evolution?

How accurate are neuro-evolution algorithms (such as NEAT) in modelling real organism evolution?
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Can neuroevolution be used for solving tasks other than games?

I'm seeing a lot of examples of neuroevolution techniques involving games or robot problems. Can neuroevolution be used for solving tasks other than games? For example, how could you transform a CSV ...
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39 views

How to shape the weights or nodes during gradient training of neural network? Training with constraints?

Gradient training changes indiscriminately all the weights and nodes of the neural network. But one can imagine the situations when the training should be shaped, e.g.: One can put constraints on ...
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521 views

How to design an AI that discovers more complex concepts on its own?

How would I go about designing a (relatively) simple AI that discovers and invents random more complex concepts on its own? For example, say I had a robot car. It doesn't know it's a car. It has ...
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184 views

Can NEAT produce neural networks where inputs are directly connected to outputs?

Can NEAT produce neural networks where inputs are directly (without intermediate hidden neurons) connected to outputs?
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100 views

Why do we apply the mutation operation after generating the offspring?

Why do we apply the mutation operation after generating the offspring, in genetic algorithms?