New answers tagged genetic-algorithms
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Phillipe's excellent answer covers the crux of the subject, so I'm just going to state the obvious: the key difference is the medium and timescale.
Biological evolution is a function of the natural world, and typically occurs over a long time span, depending on the organisms and how quickly they produce new generations. (We typically think of biological ...
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This can be calculated quite easily in the context of 8-queen problem. Just start with a particular configuration. Starting from the queen in the left-most column just keep on counting the non-attacking positions (pairs) on the right with each queen. Carry on column by column towards your right until you reach the last queen. As a special case for the last ...
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I found my answer in a different post: How to evolve weights of a neural network in Neuroevolution?. Note that the genetic algorithm is a subcategory of the neuroevolution algorithm. Short answer, my original thoughts were correct.
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Philip's answer is good, but I'll add to it.
In a GA, a population of individuals (typically represented by bit strings) is evaluated for its fitness on a particular task. Each individual is evaluated separately by a fitness function than can determine its quality. In the Traveling Salesman Problem, the bit string might represent a sequence of numbers, for ...
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A genetic algorithm is typically a single population designed to optimise to a specific task, say minimising the distance on the travelling salesman problem.
Evolutionary game theory algorithms typically model changes between populations that are in competition, generally by using genetic algorithms as above but framed within a broader competitive ...
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I would recommend learning about Reinforcement learning first. You don't need a dataset as you train your network by letting it play the game over and over again. but knowing how to do so doea mean finding out about markov decision process and how you can use the neural network to solve this.
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In the paper Exploiting Open-Endedness to Solve Problems Through the Search for Novelty (2008), by Joel Lehman and Kenneth O. Stanley, which introduced the novelty search approach, it is written
Thus this paper introduces the novelty search algorithm, which searches with no objective other than continually finding novel behaviors in the search space.
and
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