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A human analogy can help you here (a variance). Initialize all the agents with an initial value $x$; we will call this energyUnits. I Will talk later more about this. Now, add some value, as an incentive, whenever the agent eats good food, to the energyUnits. You need to add a function that will keep decrementing the value of the agent's energyUnits, as ...

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Genetic algorithms work best when given a scalar fitness value that increases smoothly, so that you can compare two population members regardless of whether they failed or succeeded at the task. That usually requires you to analyse the problem, and come up with a measure that would improve as an individual gets closer to solving a task. It generally helps ...

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You can probably get away with a relatively low X for two reasons: The Central Limit Theorem. This tells us that the accuracy in the estimate of an agent's fitness will improve as the square root of the number of games played. In a GA, you don't need an absolute ranking of individuals, because your selection mechanism (see "related articles" here) ...

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The typical way you'll see a GA measured is that an algorithm with a population size of $N$ is ran $K$ times from new random seeds each time. That gives you $K$ total runs of the algorithm, each of which, at the end, had a final population of $N$ individuals. If you take the best of those $N$ from each run, you get $K$ "best" solutions found. The ...

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What I notice is that the network's fitness keeps climbing up and falling down again. It seems that my current approach only evolves certain patterns on placing signs on the board and once random mutation interrupts current pattern new one emerges. My network goes in circles without ever evolving actual strategy. I suspect solution for this would be to pit ...

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You do not always need an explictly coded fitness function to perform genetic algorithm searches. The more general need is for a selection process that favours individuals that perform better at the core tasks in an environment (i.e. that are "more fit"). One way of assessing performance is to award a numerical score, but other approaches are ...

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Here you can find an example of how to apply genetic algorithms to solve the 8-queens problem. The proposed fitness function is based on the chessboard arrangement, and in particular, it is inversely proportional to the number of clashes amongst attacking positions of queens; thus, a high fitness value implies a low number of clashes.

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Really you're entering the world in which you probably want to develop genetic operators that have meaning in your domain. You mention TSP, and correctly point out that the absolute position within the chromosome doesn't matter. There are other permutation problems where this isn't true. The Quadratic Assignment Problem (QAP) is one example. Like TSP, QAP ...

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In many cases, a fitness function (FF) is indeed similar to a reward function (RF), but, in other cases, it's more similar to a cost function (CF) as used in supervised learning (SL), and I explain below why. The FF, RF, and CF are used to evaluate the individuals, actions, and predictions, respectively, hence they can all be thought of as evaluation ...

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How can you assess the quality of any solution without a measure of quality, which, in the context of genetic algorithms, is known as fitness function? The term fitness function is due to the well-known phrase "Survival of the Fittest", which is often used to describe the Darwinian theory of natural selection (which genetic algorithms are based on)....

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Your fitness function has two objectives that are added together, but they are not necessarily on the same scale. The component cos(drone_angle) must have a value from 0..1. The component 1/distToTarget will have a range that depends on how you measure distToTarget; e.g. if distToTarget has a range 0..1000, then this part of the fitness function will always ...

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Main answer To answer your question as directly as possible: No, deep learning does not make that "assumption". But you're close. Just swap the word "assumption" with "imposition". Deep learning sets things up such that the landscape is (mostly) smooth and always continuous*, and therefore it is possible to do some sort of ...

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I'm going to take the fitness landscape to be the graph of the loss function, $\mathcal{G} = \{\left(\theta, L(\theta)\right) : \theta \in \mathbb{R}^n\}$, where $\theta$ parameterises the network (i.e. it is the weights and biases) and $L$ is a given loss function; in other words, the surface you would get by plotting the loss function against its ...

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No magical formula As already stated in this answer, the definition of the fitness function depends on the problem, given that it essentially determines the solutions that you are looking for, and it raises similar issues to the ones you would encounter while defining a reward function in reinforcement learning, such as fitness misspecification (in fact, the ...

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In my experience, the fitness function is a way to define the goal of a genetic algorithm. It provides a way to compare how "good" two solutions are, for example, for mate selection and for deleting "bad" solutions from the population. The fitness function can also be a way to incorporate constraints, prior knowledge you may have about the shape of the ...

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There are a couple of ways of dealing with this. Very often, the approach is just to design your representation and operators in a way to account for the fact that the world changes. The idea is to give the algorithm something that can be used to learn general behaviors or solutions rather than specific ones. Take an example of learning to steer a race car ...

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Consider what would the outcome be if offspring was assigned on the basis of fitness (unadjusted). Sum of A fitnesses would be 40 and B=18. Fitness ratio for both species would be 2.(2):1. In case of adjusted fitness the numbers are A=15 and B=9, which gives ratio of 1.(6):1, thus A is assigned less offspring based on adjusted fitness then unadjusted. Also ...

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Is this a viable replacement to the Fitness function? Sure, the fitness is 1 for the winner and 0 for the loser. You're using some kind of the Tournament selection. It might be better to use more chromosomes and let A play against B, C, D... and define the fitness as the number of wins. Or not, as such an evaluation is more precise but also more time-...

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Seem like time is a good fitness, though you need it to engage into learning side sensor inputs and side movement. I would consider adding a bit of randomness to the environment. How about adding some random mild forces that might sway it left, right, front and rear a bit so that that bots are forced to use other sensors and inputs to stay in the center. ...

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