Questions tagged [genetic-algorithms]

Questions on genetic algorithms, a family of evolutionary search heuristics. Genetic Algorithms are adaptive heuristic search algorithms based on the evolutionary ideas of natural selection and generics.

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668 views

Measuring and maintaining the diversity of individuals in Genetic Algorithm

While I was using the Genetic Algorithm to generate full correct Sudoku grids starting from a population of random grids, I occasionally face the problem of the process being stuck on a local maxima ...
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1answer
890 views

NeuroEvolution: NEAT algorithm innovation numbers

I have been reading up on the NeuronEvolution of Augmented Topologies and there's this little thing that's been bothering me. While reading Kenneth Stanley's Paper on NEAT I came on this figure here: ...
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Direct encoding of the solution into the chromosomes in a Random-Key Genetic Algorithm

In a random key genetic algorithm1, the chromosomes consist in a sequence of real numbers (initially randomly generated) in the interval $[0,1]$. I've been told by my teacher that in these kinds of ...
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342 views

It's necessary to replace all the population each generation in a genetic algorithm?

I'm creating a timetable generator using GA's, and I'm stuck in the crossover part. Each generation, I just basically copy the best individuals (the 50% fittest individuals inside the population), ...
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Using Genetic Algorithms for volatile problems

Suppose I am looking at an optimization problem with a large number of interconnected constraints, but the solution is - in some regions - extremely volatile (With volatile I mean: small mutations ...
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49 views

Genetic algorithms, Pittsburg - Reach a decision from a set of rules

Consider a binary classification problem. In the Pittsburg approach each member of the population represents a set of rules. Each rule encodes information regarding the data features (that is, the ...
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1answer
75 views

Is my implementation of genetic algorithm correct?

In genetic algorithms, we have a function called "mutation". Before we call this function, we choose parents. In my version of the algorithm, when i mutate the child, i don't change the bits which ...
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1answer
572 views

Pittsburgh approach, how does encoding and fitness function work?

I am writing a genetic algorithm for Machine learning and came across Pittsburgh approach in some research papers. The papers didn't explain the algorithms proper or I am too stupid to understand. I ...
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2answers
556 views

What crossover operator should I use for arranging students in classrooms?

I am building a genetic algorithm to solve a "classroom map" problem. The goal is to place correctly students depending on several factors (shortsighted students, turbulent students...). Here, the ...
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1answer
55 views

Measuring Fitness in Genetic Programming Functionology

I recently begun working on a genetic algorithm to solve for patterns in data. More specifically, it solves for functions given a sample of ins and outs. So a possible solution would be X+2 = Y. The ...
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5k views

How to analyze the performance of a genetic algorithm experimentally?

I have a genetic algorithm for an optimization problem. I plotted the running time of the algorithm on several runs on the same input and the same parameters (population size, generation size, ...
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296 views

Does local optimization in a genetic algorithm decrease diversity?

I want to create a hybrid genetic algorithm for a project to solve really high dimensional problems (1000+) One of my ideas is to incorporate a local optimization method within GA so each individual ...
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1answer
594 views

Should you use Genetic algorithm for an extremly large unstructured search space?

My search space is discrete and in the order of $10^{1360}$, with a probably very complex fitness surface. Is it hopeless to attempt to use GA for such a problem? One fitness evaluation could take 1-3 ...
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1answer
243 views

Variation of rank selection in genetic algorithm

I'm looking for a specific variation of rank selection in genetic algorithms. I came across a python program tailored for a specific problem that uses a variant of "rank" selection. In normal rank ...
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Genetic Algorithms: Converge to best solution for one, few or many environments?

Let's say we take someone's garden (A) as the environment. We want a robot to pick up a series of eggs that chickens have laid in the garden, while covering as little ground as possible (those heavy ...
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1answer
80 views

Can the reproduction function of an evolutionary algorithm consider gene strength?

I am trying to solve the Zen Garden puzzle using an evolutionary algorithm. My question relates to evolutionary algorithms in general. Bit about evolutionary algorithms: An evolutionary algorithm is ...
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204 views

Why does my Genetic Algorithm not work for higher number of individuals?

I am using a cooperative coevolutionary genetic algorithm, the algorithm is given in A cooperative coevolutionary approach to function optimization by DeJong et. al. (1994), for optimizing a 62 ...
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1answer
89 views

Fitness functions for low-dimensional parts of cooperative coevolution algorithms

In cooperative coevolution algorithms, a high dimensional vector is broken into smaller vectors, each of which is optimized separately using EAs for fewer dimensions and then recombined. What is the ...
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1answer
2k views

Train a neural network to play tic tac toe using a genetic algorithm

I have an assignment for school, in which I have to build a neural network that will play tic tac toe, using genetic algorithms for training. The thing is that I am clueless on how to connect the two. ...
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1answer
3k views

Genetic Algorithms - Tournament Selection

I know this should be a fairly simple concept but I have been Googling a lot and can't seem to find a definitive definition. If we had the following random population: ...
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1answer
295 views

Genetic Algorithms - Why wouldn't we use the principle of Elitism?

In Genetic Algorithms, the idea of elitism is that we keep our best solution from the generation in our population regardless of what happens throughout the iteration. However, elitism seems to be ...
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1answer
3k views

How to identify when to use Genetic Algorithm/Programming

I have been reading/studying on genetic algorithm/programming, and have implemented Traveling salesman problem. TSP is basically a permutation/combination problem, and I can understand how GA helps ...
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644 views

Why are diploid (dominant/recessive) genes not used widely in genetic algorithms?

In most implementations of genetic algorithms, the focus is on crossover and mutation. But somehow, most of them leave out diploid (dominant/recessive) nature of genes. As far as my (limited) ...
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What use does the predictor stage in a particle filter have?

Im a bit confused about the particle filter. I understand the generic particle filter algorithm but in some literature say particle filter has predictor stage which is not mentioned in the generic ...
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584 views

How do genetic algorithms work exactly?

I was looking at the basic genetic algorithm here http://www.ai-junkie.com/ga/intro/gat1.html But I have some questions about things I didn't get. To reiterate: You have a problem you want a good ...
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Is Differential Evolution a genetic algorithm?

I am trying to classify the Differential Evolution algorithm according to the framework in the book: Introduction to Evolutionary Computing The authors classify the field of evolutionary ...
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1answer
59 views

Comparing different implementations of genetic algorithms

I am looking up some material for my thesis in CS (development of a module to integrate a genetic algorithm in a system developed by other students). My actual current task is to make a comparative ...
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Evolving artificial neural networks for solving NP problems

I've recently read a really interesting blog entry from Google Research Blog talking about neural network. Basically they use this neural networks for solving various problems like image recognition. ...
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1answer
2k views

Classification of job shop scheduling problems

I'm writing a program (using genetic algorithms) that finds sort-of-optimal scheduling plan for a factory. The factory has several types of machines (say, ...
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85 views

Programming a genetic algorithm with a non-fixed size

I am trying to write a genetic algorithm for a program. Most examples for genetic algorithms use something like this as the input: aaaaaaaaaa and mutate/...
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1answer
197 views

Lamarckian and Genetic algorithm

Yesterday I've done some research how to optimize genetic algorithm and I've encountered a very interesting theory that we can use Lamarckian theory (adaptive theory) to optimize the neural network. ...
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1answer
1k views

Disadvantages to using simple step functions for activation in neural networks?

From what I have read, the main advantage to using tanh(x) or sigmoid(x) as an activation function for neural networks is that it is very easily differentiable. I am trying to implement a neural ...
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811 views

Why are syntax trees used in genetic programming?

Reading a course on genetic programming, the first chapter describes the syntax tree as the basic representation of programs in genetic programming. What are the reasons leading to the choice of a ...
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1answer
2k views

TSP genetic algorithm: what mutation function for adjacency representation?

When implementing TSP GA I decided for adjacency representation (i.e. $j$ value in $i$-th index means that node $j$ goes right after node $i$), as it enables interesting heuristical crossover ...
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1answer
99 views

A clarification on the taxonomy of Evolutionary Algorithms

A rather basic question but I am confused about the characterization of a certain local search method which I want to describe in the framework of EAs. In particular, consider an EA which in every ...
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1k views

Genetic algorithm crossover technique for solving graph colouring problem

I am trying to develop a genetic algorithm to solve a graph colouring problem. The problem is the standard graph colouring problem, given a graph $G = (V,E)$ where $V$ is the set of vertices $V=\{0 \...
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1answer
210 views

Extended Compact Genetic Algorithm question

I have a question regarding the ECGA algorithm. This is what I'm currently studying. If ECGA builds a model of the parent pool using the 'minimum description length' (MDL) measure, why cannot we ...
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396 views

Crossover method for genetic algorithm

I got an amount of numbers, they are shuffled and represent the individuals. For example (3,1,3,2,5,22,5) is one individual or (22,3,1,3,5,5,2). Mutation is done quite easy by permutation within an ...
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1answer
110 views

Mutations as a crossover by product

Let's say I'm writing a GA to find an optimal path to travel from point $A$ to point $B$. Genotypes are a list of directions (north, south, east, west) to follow. So a genotype "NENWEE" will move ...
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4k views

Selection, crossover and mutation function choice in genetic algorithms

I have been developing a GA for one of the projects I'm working on. I have everything implemented with no problems and I really like how GAs work in general, it's a really cool and relatively new ...
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2answers
164 views

Evolutionary algorithm - how to select the parents

I try to solve Physical Travelling Salesman Problem using evolutionary algorithm and I have diffucult to detemine how to choose the parent , on which we do the crossover. Assume I have popultion of ...
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4answers
6k views

Standard Parameters for Genetic Algorithms

I'm currently writing my thesis, which uses genetic algorithms at some point. Now I need to define some parameters for the genetic algorithm I know that, because of the No Free Lunch Theorem there ...
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1k views

Genetic Algorithm Minimum Population Size

Is there a minimum limit to a pool (population) size when using the genetic algorithm to solve an optimization problem? For example a population of size 2.
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1answer
1k views

Genetic Algorithm, Neural Network, Deep Learning, Machine Learning Similarities and Applications? [closed]

I am a computer engineering student and trying to get the idea behind all these Artificial Intelligence Concepts and applications. I know little theoretically about machine learning and some high ...
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769 views

Improving MSE as fitness function for a genetic algorithm

I am implementing an autoencoder neural network in matlab, the weights of which are being optimised by a genetic algorithm. At the moment I am working on the first layer, trying to get an improved ...
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1answer
75 views

Bit distance and disruption [duplicate]

Earlier, I asked a question defining disruption in Genetic Algorithms. Given that definition, I'm still confused on how to answer the following question. True or false? For 1-point and 2-point ...
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1answer
146 views

Disruption in the context of Genetic Algorithms

I'm trying to do a homework problem which references "disruption" in Genetic Algorithms. True or false? For 1-point and 2-point crossover, the schemata which have bits that are close ...
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1answer
62 views

Progressive discrete multifunction optimization

I have a set of functions $F$. The functions effectively take a set $S$ that is always a subset of a global set of all possible values $G$, where $|G|>1000$. (alternatively, they could take a $|G|$...
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1answer
2k views

How to use Sigma Scaling in a genetic algorithm

I have a genetic algorithm in Java and I'm testing new types of selections. For my tests I'm using the De Jong Half Sphere, my fitness function is $x^2 + y^2$. The selection method used is Sigma ...
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1answer
5k views

How to stop genetic algorithm population converging to a single value

I've written a genetic algorithm (GA) that solves a 7-dimensional optimisation problem. All seven variables are floating point numbers. The problem is that the entire population seems to converge to ...