Questions tagged [genetic-algorithms]
For questions related to genetic algorithms (GAs), which are a form of evolutionary algorithms. A genetic algorithm is a method (more precisely, a metaheuristic) for solving optimization and search problems based on natural selection processes (that is, they use bio-inspired operators such as mutation, crossover, and selection).
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Unable to meet desired mean squared error
I wish to get MSE < 0.5 on test data (https://easyupload.io/zr7xf3) which is 20% of given data chosen randomly. But I am reaching 0.73 using both plain Ridge Regression as well as a neural network ...
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Is it possible to perform neuroevolution without a fitness function?
My question is about neuroevolution (genetic algorithm + neural network): I want to create artificial life by evolving agents. But instead of relying on a fitness function, I would like to have the ...
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Measuring novel configuration of points
I am trying to implement Novelty search; I understand why it can work better than the standard Genetic Algorithm based solution which just rewards according to the objective.
I am working on a problem ...
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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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What are most commons methods to measure improvement rate in a meta-heuristic?
When I run a meta-heuristics, like a Genetic Algorithm or a Simulated Annealing, I want to have a termination criterion that stops the algorithms when there is not any significant fitness improvement.
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Can we use genetic algorithms to evolve datasets?
Genetic algorithms are used to solve many optimization tasks.
If I have a dataset, can I evolve it with a genetic algorithm to create an evolved version of the same dataset?
We could consider each ...
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experiences on using genetic algorithms as a way to improve neural networks?
I wonder if there is research, patents, or libraries using Genetic algorithms (GA) to improve Neural Networks. I don't find anything in the subject. For example:
use GA to find better parameters in a ...
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How to avoid running out of solutions in genetic algorithm due to selection?
The genetic algorithm consists of 5 phases of which 4 are repeated:
Initial population (initially)
Fitness function
Selection
Crossover
Mutation
In the selection phase, the number of solutions ...
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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 do I determine the genomes to use for crossover in NEAT?
If I have the fitness of each genome, how do I determine which genome will crossover with which, and so on, so that I get a new population?
Unfortunately, I can't find anything about it in the ...
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How does the crossover operator work when my output contains only 2 states?
I'm currently working on a project where I am using a basic cellular automata and a genetic algorithm to create dungeon-like maps. Currently, I'm having an incredibly hard time understanding how ...
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Genetic Algorithm Python Snake not improving
So, i have created Snake game using Pygame and Python. Then i wanted to create an AI with Genetic algorithm and a simple NN to play it. Seems pretty fun, but things aren't working out.
This is my ...
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What does it mean if classification error is equal between two networks but the MSE is different?
I'm experimenting with training a feedforward neural network using a genetic algorithm and I've done a few tests using both the mean squared error and classification error functions as fitness ...
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How to perform classification with NEAT-Python?
I am trying to do classification using NEAT-python for the first time, and I am having difficulty getting the accuracy rate. I tried the same problem with an ANN and was able to get a good accuracy ...
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Using ML for Enemy Generation in Video Games
I am attempting to make a 2-D platformer game where the player traverses through an evil factory that is producing killer robots. The robots spawn at multiple specific locations in each level and ...
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Are connections genes in a genome ever deleted or just disabled?
When a new node is added, the previous connection is disabled and not removed.
Is there any situation in which a connection gene is removed? For example, in the above diagram connection gene with ...
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How does crossover work in a genetic algorithm?
If I had the weights of a certain number of "parents" that I wanted to crossbreed, and I used whatever method to pick out the "best parents" (I used a roulette wheel option, if that's any relevant), ...
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In machine learning, how can we overcome the restrictive nature of conjunctive space?
In machine learning, problem space can be represented through concept space, instance space version space and hypothesis space. These problem spaces used the conjunctive space and are very restrictive ...
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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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How can I develop a genetic algorithm with a constraint on the sum of alleles?
I'm working on a genetic algorithm with a constraint on the sum of the alleles, e.g. if we use regular binary coding and a chromosome is 5-bits long I'd like to constrain it so that the sum of the ...
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How can we design the mutation and crossover operations when the order of the genes in the chromosomes matters?
Consider an optimization problem that involves a set of tasks $T = \{1,2,3,4,5\}$, where the goal is to find a certain order of these tasks.
I would like to solve this problem with a genetic algorithm,...
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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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Reinforcement learning for a 2D game involving two players
I'd like to create an AI for a 2D game involving two players fighting against each other. The map look something like this (The map is a NxN array somehow randomly generated):
Basically the players ...
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How are weights updated in a genetic algorithm with neural network?
Suppose an AI is to play the game flappy bird. And the fitness function is how long the bird has traveled before the game ends.
Would we have multiple neural networks initialized at the beginning ...
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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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Training methods for bipedal robot [closed]
I am looking to train a bipedal robot using unity as a scape with a genetic algorithm. I will import the CAD into unity so the hardware is exact. My questions:
Is Unity physics accurate enough to ...
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Unable to achieve expected outputs using NEAT for the snake game
I am trying to implement NEAT for the snake game. My game logic is ready, which is working properly and NEAT configured. But even after 100 generations with 200 genomes per generation, the snakes ...
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Is there any disadvantage of the maximum number of fitness function call as a stop criterion?
I'm studying different stop criteria in genetic algorithms and the advantages and disadvantages of each of them for evaluating different algorithms. One of these methods is the max number of fitness ...
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An "elevator pitch" breakdown of areas of applications for Reinforcement Learning & Neural Networks vs. Genetic Algorithms
I'm looking for an "elevator pitch" breakdown of areas of applications for Reinforcement Learning & Neural Networks vs. Genetic Algorithms, both actual and theoretical.
Links are welcome, but ...
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How is the distance between pointers in Stochastic Universal Sampling determined?
I'm studying about different selection methods in genetic algorithms. My question is about the Stochastic Universal Sampling (SUS) selection method. I know that each individual will occupy a segment ...
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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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Given a list of integers $\{c_1, \dots, c_N \}$, how do I find an integer $D$ that minimizes the sum of remainders $\sum_i c_i \text{ mod } D$?
I have a set of fixed integers $S = \{c_1, \dots, c_N \}$. I want to find a single integer $D$, greater than a certain threshold $T$, i.e. $D > T \geq 0$, that divides each $c_i$ and leaves ...
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Metrics of quality of parameter space exploration
Considering a black box optimization problem on non-linear, non-convex function where we want to minimize an objective function.
One way to assess the quality of an optimizer is to look at the best ...
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CNN - Visualizing images near decision boundary - Pixels inexplicably tend to edges
We are exploring the images classified by a CNN at its decision boundary, using Genetic Algorithms to generate them. We have created a fine-tuned binary grayscale image classifier for cats. As the ...
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Library for rendering neural network NEAT [closed]
I just finished my implementation of NEAT and I want to see the phenotype of each genome. Is there a library for displaying a neural network like this?
Example of my genome syntax:
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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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Is a neural network the correct approach to optimising a fitness function in a genetic algorithm?
I've written an application to help players pick the optimal heroes during the draft phase of the Heroes of the Storm MOBA. It can be daunting to pick from 80+ characters that have synergies/counters ...
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Can neuroevolution be combined with gradient descent?
Is there any precedent for using a neuroevolution algorithm, like NEAT, as a way of getting to an initialization of weights for a network that can then be fine-tuned with gradient descent and back-...
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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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Can we use the Tierra approach to optimize machine code?
Thomas Ray's Tierra is a computer program which simulates life.
In the linked paper, he argues how this simulation may have real-world applications, showing how his digital organisms (computer ...
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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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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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How does NEAT find the most successful generation without gradients?
I'm new to NEAT, so, please, don't be too harsh. How does NEAT find the most successful generation without gradient descent or gradients?
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Can we evolve 0 and 1? [closed]
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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How to implement a neural network for Flappy Bird in Python? [closed]
I am new in the field of AI. I am working to create the flappy bird using Genetic Algorithm. After reading and seeing some examples, I saw that most implementations use a Neural Network + Genetic ...
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