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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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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Representation of real numbers in Genetic Algorithm

Take a look at section 2.2.2 of this book (from Page-15 to 16). 2.2.2 Representation and Evaluation $$max f (x)= x sin(10πx)+2.0 ... ... ... (2.8)$$ $$s.t. −1 ≤ x ≤ 2$$ We can use a ...
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How can I input tasks in a genetic algorithm with three different orders?

How can I structure my genetic algorithm to output ordered arrays? I have some tasks, let's call them $\{1,2,3,4,5\}$ and I would like to create genes representing these tasks in different orders. ...
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How do mutation and crossover work with real-valued chromosomes?

How exactly are "mutation" and "cross-over" applied in the context of a genetic algorithm based on real numbers (as opposed to just bits)? I think I understood how those two phases are applied in a "...
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What is meant by “reproduction” in a genetic algorithm?

In the following exercise, the word reproduction is mentioned. Your task is to design a simple genetic algorithm, with binary-coded chromosomes, in order to solve a pattern-finding problem in 16-...
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What is the best programming language to learn to implement genetic algorithms?

What is the best and easiest programming language to learn to implement genetic algorithms? C++ or Python, or any other?
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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 ...
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1answer
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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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1answer
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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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The disadvantage of maximum number of fitness function call as stop criteria

I'm studying different stop criteria in genetic algorithm and advantages and disadvantages of each of them for evaluating different algorithms. One of these methods is max number of fitness function ...
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Intelligent crossover for binary chromosomes

I'm studying about genetic algorithm. I'm studying about different crossover operations used for binary chromosomes. These methods usually don't use any intelligence (1-point crossover, uniform ...
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Distance between pointers in Stochastic Universal Sampling (SUS)

I'm studying about different selection methods in genetic algorithm. My question is about Stochastic Universal Sampling (SUS) selection method. I know that each individual will occupy a segment of the ...
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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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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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How to implement a neural network for Flappy Bird in Python?

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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Applications of genetic algorithms in project management

Do you see any GA Application that could support Project Management? I thought about Task Dispatching. I am curious about your ideas.
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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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Training methods for bipedal robot

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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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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5answers
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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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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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What's the difference between biological and artificial evolution?

I am trying to understand the difference between biological and artificial evolution. If we look at it in terms of genetics, in both of them, the selection operation is a key term. What's the ...
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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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How to calculate fitness function of 8-queens problem?

In evolutionary computation and in particular in the context of genetic algorithms, there is a stochastic operation called "fitness function". The better a state, the greater the value of the fitness ...
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What is the difference between memetic algorithms and genetic algorithms?

What is the difference between memetic algorithms and genetic algorithms? Is an individual's lifetime a learning part of memetic algorithms?
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How does adjusted fitness penalize large species in NEAT?

I'm learning about NEAT from the paper Evolving Neural Networks through Augmenting Topologies. I'm having trouble understanding how adjusted fitness penalizes large species and prevents them from ...
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1answer
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In novelty search, are the novel structures or behaviour of the neural network rewarded?

I have been reading a lot lately about some very promising work coming out of Uber's AI Labs using mutation algorithms enhanced with novelty search to evolve deep neural nets. See the paper Safe ...
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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?
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How does novelty search work?

In this article, the author claims that guiding evolution by novelty alone (without explicit goals) can solve problems even better than using explicit goals. In other words, using a novelty measure as ...
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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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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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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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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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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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What are examples of optimization problems that can be solved using a genetic algorithm?

I'm trying to learn how a genetic algorithm can solve optimization problems. I have already learned how a genetic algorithm can solve knapsack, TSP and set cover problems. I'm looking for some other ...
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Question about minimizing sum of remainders

I have a set of integers [$c_1$, $c_2$, $c_3$, ... , $c_N$]. A non-negative integer D, greater than a certain threshold, divides each 𝑐𝑖 and leaves remainder 𝑟𝑖,i.e., $r_i$ can be written as $r_i=...
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Several questions regarding the NEAT algorithm

I've recently read the paper Evolving Neural Networks through Augmenting Topologies which introduces NEAT. I am now trying to prototype it myself in JavaScript. However, I stumbled across a few ...
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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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What kind of algorithm can invent?

Set aside networks, image classification, gradients, and the strength of intelligence for a moment and consider the world before people lit fires. Fires were started periodically just as they are now,...
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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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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

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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1answer
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Disabling of genes during crossover (NEAT)

I am implementing NEAT (Neuroevolution of augmenting topologies) by Stanley, Original Paper. I am facing a problem during crossover of genomes. Suppose two networks with connections ...
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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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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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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-...