Questions tagged [evolutionary-algorithms]
For questions about evolutionary algorithms (EAs): a subset of evolutionary computation, a generic population-based metaheuristic optimization algorithm. An EA uses mechanisms inspired by biological evolution, such as reproduction, mutation, recombination, and selection. (Wikipedia)
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Multi-objective optimization problem - fitness
My task is to find a solution for a multiobjective optimization problem, which has two objectives. I'm solving this with a genetic algorithm by using a fitness function, which works fine.
My question ...
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Examples in the Real World where Evolutionary Algorithms/Genetic Algorithms Outperform other Classes of Optimization Algorithms
I have been trying to do some research to find out if there are certain industries/types of problems or even specific examples in applied research paper where Evolutionary Algorithms (e.g. Genetic ...
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How to tune hyperparameters/architecture of networks that are expensive to train?
What are some recommended ways to tune hyperparameters and/or develop domain-specific architectures for a large neural network model? That is, how to further tune a large neural network that already ...
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Connection between mean update in CMA-ES and gradient of expected fitness
I currently learn about black-box optimization and CMA-ES. Now, I try to understand some of the theoretical foundations of it. The update of the mean in classic CMA-ES is as follows:
$$m \leftarrow m +...
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Measuring solution quality and allele impact on population fitness for a genetic algorithm
Situation:
Let's say you're running a genetic algorithm to improve the way people are interacting with an online service. The alleles in each individual determine the exact behaviour of the service, ...
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What metric should be minimized when searching from a subset of points that are as uniformly distributed across the space as possible?
Given a set of n points, I have to find a subset of given size m<n, so that the m points are as uniformly distributed as possible across the volume enclosed by the convex hull of set n. See example ...
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Why is OpenAI's evolution strategy a natural evolution strategy?
Natural Evolution Strategies follow the natural gradient using the Fisher Information Matrix $\mathbf{F}_\theta$ of a search distribtion $p_\theta$. That is, parameters in natural evolution strategies ...
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Agent based modeling: binding 2 agents together to form a H2 molecule in NetLogo (or Agents.jl)? [closed]
Note: I got the message:
This question appears to be off-topic because it is not about probability, statistics, machine learning, data analysis, data mining, or data visualization.
Agent based ...
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Models for Long-Term Time-Series Forecasting and Pattern Recognition
I'm trying to find a solution for long-term electricity hourly prices forecasting. Explaining simply, I have some data from 2018 - 2021 containing Demand, Renewable Generation, Hydropower Generation, ...
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Single optimum parameter output for multi-objective optimization (genetic algorithms)
If this question is out of scope for this forum, before closing it please advise me on a better platform to ask my question!
I'm very new to this field so apologies if my questions are not clear.
I am ...
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Sampling from distribution parametrized by $ \log(1-p) $ given $ \log (p) $
The context for the problem is that I'm working with a modified genetic algorithm where the fitness score of each chromosome is given by a log-probability $ \log(p) \in (-\infty, 0) $. Thus, I have a ...
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How to minimise a quantity using a Genetic Algorithm
I'm somewhat new to Genetic Algorithms and I've been exploring the use of them to minimize a quantity (Y), where my optimum value is a small negative value.
I've implemented an algorithm that has a ...
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Crossover and mutation with constraints
I had some experience with genetic algorithms during my computer science studies. I wanted to refresh my knowledge and decided to write a simple prototype for automated seating people at tables (e.g. ...
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How to derive the gradient of the reparameterized score function estimator?
In the paper Evolution Strategies as a Scalable Alternative to Reinforcement Learning, the authors derive the following gradient of the score function estimator
$$
\begin{align}
\nabla_\psi\mathbb E_{...
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Conducting Statistical Analysis on Evolving Populations
I am writing a paper on the evolution of altruism. To do so, I have written a computer program that simulates the environment and allows a population to evolve. Organisms are either altruists or are ...
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What is the reason behind the weight updates in Evolution Strategies?
OpenAI introduced Evolution Strategies as an alternative to reinforcement learning technique without backpropagation. A sample code from their website,
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Is Crossover only connected to Genetic algorithm?
Is the idea of crossover only restricted to Genetic Algorithms ?
Are there any other evolutionary algorithms that uses crossover(even under another name ) ?
If an algorithm uses crossover but does ...
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How do I design this multi-objective fitness function for use with genetic algorithm?
I have a multi-objective optimization problem that I am applying genetic algorithm (GA) to solve. Currently, there are only 2 objectives:
minimize cost
maximize validity
The cost minimization is ...
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What is the difference between the study of Evolutionary algorithm vs. Optimization?
I have a course named "Evolutionary Algorithm". But, our teacher is always mentioning the word "Optimization" in his lectures.
I am confused. Is he actually teaching Optimization? If yes, why is the ...
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Evolution strategies global recombination question
In evolution strategies the genotype has object variables and strategy variables as alleles (and sometimes the alpha values).
I'm reading the book Introduction to Evolutionary Computing (A.E. Eiben, ...
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How to compare the performance of Evolutionary Algorithms
I need to compare the performance of 3 Evolutionary algorithms on some Benchmark functions.
Evolutionary algorithms are a heuristic-based approach which are used to solve optimization problems.
I ...
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Sensitivity of Evolutionary algorithms to underlying random number generators
Techniques of evolutionary algorithms (EA) rely heavily on the use of random number generators (RNGs). From initial population generation, through the specific canonical operators applied to create ...
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How is the equation in "Evolution Strategies as a Scalable Alternative to Reinforcement Learning" derived?
In the OpenAI paper "Evolution Strategies as a Scalable Alternative to Reinforcement Learning", how is the equation in page 3 derived? Thanks.
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What is rotationally invariant crossover in Evolutionary optimization algorithms?
This article "Enhancing Differential Evolution Utilizing Eigenvector-Based Crossover Operator" design a rotationally invariant crossover, what is, in simple words, rotationally invariant crossover in ...
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Which statistical test would be most appropriate for this data?
I conducted an experiment in an evolutionary program called AVIDA-ED contrasting the adaptive evolution in different world sizes (30x30,40x40,50x50).
I performed 10 repetitions of each world size and ...
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Sample a probability distribution with an evolutionary algorithm?
I've been doing some initial level reading on Markov chain Monte Carlo (MCMC).
For what I can tell given a probability distribution $P(x_1, x_2, ..., x_N)$ (dependent on $N$ parameters), MCMC ...
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Forecasting Using Genetic Algorithms
I have come across some papers discussing the use of genetic algorithms as a forecasting tool. I don't however understand how a genetic algorithm, which to my (limited) knowledge is used to solve ...
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Can Metropolis be considered as evolutionary algorithm?
If we compare simple 1+1 evolutionary algorithm (e.g. Droste, Jansen, and Wegener, 2002)
1+1 evolutionary algorithm
Set $p_m := 1/n$.
Choose randomly an initial bit string $x \in \{0,1\}^...
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