16 votes
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What exactly are genetic algorithms and what sort of problems are they good for?

Evolutionary algorithms are a family of optimization algorithms based on the principle of Darwinian natural selection. As part of natural selection, a given environment has a population of individuals ...
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12 votes
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How should I encode the structure of a neural network into a genome?

Section 4.2 of "Essentials of Metaheuristics" has a wealth of information on alternative ways of encoding graph structures via Genetic Algorithms. With particular regard to evolving ANNs, I would ...
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10 votes
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What is a Markov chain and how can it be used in creating artificial intelligence?

A Markov model includes the probability of transitioning to each state considering the current state. "Each state" may be just one point - whether it rained on specific day, for instance - or it might ...
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  • 2,559
9 votes
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Is a genetic algorithm an example of artificial intelligence?

An ability that is commonly attributed to intelligence is problem solving. Another one is learning (improving itself from experience). Artificial intelligence can be defined as "replicating ...
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  • 1,999
9 votes

What exactly are genetic algorithms and what sort of problems are they good for?

A genetic algorithm is an algorithm that randomly generates a number of attempted solutions for a problem. This set of attempted solutions is called the "population". It then tries to see how well ...
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  • 1,174
8 votes
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What is a trap function in the context of a genetic algorithm?

"Trap" functions were introduced as a way to discuss how GAs behave on functions where sampling most of the search space would provide pressure for the algorithm to move in the wrong direction (wrong ...
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  • 446
8 votes

How does novelty search work?

As explained in an answer to this AI SE question, GAs are 'satisficers' rather than 'optimizers' and tend not to explore 'outlying' regions of the search space. Rather, the population tends to cluster ...
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8 votes
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What is the difference between reinforcement learning and evolutionary algorithms?

Evolutionary algorithms (EAs) are a family of algorithms inspired by the biological evolution that can be used to solve (constrained or not) optimization problems where the function that needs to be ...
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  • 33.8k
7 votes

What evolutionary algorithms are there that model epigenetics?

Over the last few years, evolutionary computation research has shown increasing interest in including some aspect of epigenetics. For example: A 2008 paper by Tanev and Yuta Work from Lee Spector's ...
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7 votes

How does novelty search work?

Novelty search selects for "novel behavior", by some domain-dependent definition of novelty. For example, novelty in a Maze-solving domain might be "difference of route explored". Eventually, networks ...
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  • 171
7 votes
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Why is cross-over a part of genetic algorithms?

Mutation is usually defined to be a global operator, i.e. iterated mutation is (eventually) capable of reaching every point in the vector space defined by the geneome. In that sense, mutation alone is ...
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7 votes

What exactly are genetic algorithms and what sort of problems are they good for?

There are a number of good answers here explaining what genetic algorithms are, and giving example applications. I'm adding some general purpose advice on what they are good for, but also cases where ...
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  • 1,134
7 votes

What exactly are genetic algorithms and what sort of problems are they good for?

This answer requests a practical example of how one might be used, which I will attempt to provide in addition to the other answers. They seem to due a very good job of explaining what a genetic ...
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  • 171
7 votes

How do mutation and crossover work with real-valued chromosomes?

As @Thomas W said, you can be pretty immaginative when you're developing mutation and crossover methods. Each problem has its own caracteristics and, therefore, requires a different strategy. BUT, ...
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7 votes
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Should I use neural networks or genetic algorithms to solve Gomoku?

For Gomoku, it seems a bit of an overkill to use neural networks or the genetic algorithm as both take a while, and more often than not, don't go how you want it to. The Gomoku game tree is rather ...
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  • 779
6 votes
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Apart from Reinforcement Learning, are there any other machine learning approaches to play video games?

As I see it, it all comes down to game theory, which can be said to form the foundation of successful decision making, and is particularly useful in a context, such as computing, where all parameters ...
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  • 6,067
6 votes

What exactly are genetic algorithms and what sort of problems are they good for?

As observed in another answer, all you need to apply Genetic Algorithms (GAs) is to represent a potential solution to your problem in a form that is subject to crossover and mutation. Ideally, the ...
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6 votes
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How do mutation and crossover work with real-valued chromosomes?

You have a genome with certain genes: genome = { GeneA: value, GeneB: value, GeneC: value } So take for example: ...
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6 votes
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Could GA's determine fitness by "Fighting" against each other?

i'm the main developer of Neataptic, a Javascript neuro-evolution library. Very effective! Realise that this is how real-life evolution happened as well: we kept on improving against other species, ...
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5 votes

Why is cross-over a part of genetic algorithms?

Crossover allows to combine two parents (vs. mutation, which only uses one parent). In some cases, it is useful (e.g., if you train a FPS bot, if one parent is good at shooting and another parent is ...
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5 votes

How does mating take place in NEAT?

NEAT has a constant number of organisms in its population, which prevents overpopulation from happening. The process of mating includes the following steps. The worst networks from every species ...
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  • 151
5 votes
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Is it possible to separately evolve a part of the population?

There have been extensive studies within evolutionary computation in the area of island models and niching for doing exactly this. The advantages of this approach include greater population diversity (...
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5 votes

What is the difference between memetic algorithms and genetic algorithms?

A genetic algorithm is an algorithm, based on natural selection (the process that drives biological evolution), for solving both constrained and unconstrained optimization problems. A memetic ...
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5 votes
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What does "probabilistically" mean?

In a genetic algorithm, crossover (recombination) is the analogy to mating in the real world. For example, you have some genetic information from each parent. In the case of an optimization where you ...
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  • 474
5 votes
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Can genetic algorithms be used to learn to play multiple games of the same type?

Genetic algorithms and Neural Networks both are "general" methods, in the sense that they are not "domain-specific", they do not rely specifically on any domain knowledge of the game of Mario. So yes, ...
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  • 9,379
5 votes

What is the best programming language to learn to implement genetic algorithms?

There is no "best language" for any problem. There are too many variables to consider, even when advising a single person with a single project plan. If the choice is between Python and C++, I would ...
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5 votes

Do genetic algorithms and neural networks really think?

Do genetic algorithm and neural networks really think? Genetic algorithms and neural networks are vastly different concepts. Both of them do not think. I'm aware of those AI programmes which can ...
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  • 1,007
4 votes

How should I encode the structure of a neural network into a genome?

Using evolutionary algorithms to evolve neural networks is called neuroevolution. Some neuroevolution algorithms optimize only the weights of a neural network with fixed topology. That sounds not ...
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4 votes

Why is cross-over a part of genetic algorithms?

When thinking about crossover its important to think about the fitness landscape. Consider a hypothetical scenario where we are applying a genetic algorithm to find a solution that performs well at ...
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4 votes

Is artificial life really life or not?

If you read Steven Levy's book, Artificial Life,you will find, as I did, the distinction between biological and "artificial" life blurred. If you think about it, what exactly is "life", anyway? A ...
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