Research Paper On Genetic Algorithm

Research Paper On Genetic Algorithm-40
It has the benefit of reduced memory and network transmission costs.With crossover, a new more complex method would be needed to encode the individuals. Representing millions of neural network parameters with a comparatively tiny number of seeds.[novelty search] was designed for deceptive domains in which reward-based optimization mechanisms converge to local optima.It is for this reason that non gradient based methods such as GAs can perform well compared to other popular algorithms in RL.

It has the benefit of reduced memory and network transmission costs.With crossover, a new more complex method would be needed to encode the individuals. Representing millions of neural network parameters with a comparatively tiny number of seeds.[novelty search] was designed for deceptive domains in which reward-based optimization mechanisms converge to local optima.It is for this reason that non gradient based methods such as GAs can perform well compared to other popular algorithms in RL.

State-of-the-art Encoding We propose a novel method to store large parameter vectors compactly by representing each parameter vector as an initialization seed plus the list of random seeds that produce the series of mutations applied to theta.

This innovation was essential to enabling GAs to work at the scale of deep neural networks, and we thus call it a Deep GA.

It is intended for those with some basic familiarity in topics related to machine learning.

Concepts such as ‘genetic algorithms’ and ‘gradient descent’ are prerequisite knowledge.

The scalability of the GA was demonstrated by evolving a DNN with over four million parameters, the largest network ever evolved with an evolutionary algorithm.

The ability to parallelise means that the computation for GAs can be distributed across many CPUs, creating the potential to train DNNs much more quickly compared to gradient based methods.

Deep Neural Networks (DNN) are typically optimised using gradient based methods such a back-propagation.

This paper shows that using a simple Genetic Algorithm (GA) it is possible to optimise DNNs and that GAs are a competitive alternative to gradient based methods when applied to Reinforcement Learning (RL) tasks such as learning to play Atari games, simulated humanoid locomotion or deceptive maze problems.

It’s so simple that it doesn’t even use cross-over, a technique so common in GAs that at first it felt strange to even call this algorithm a GA.

After initialising the population, the top T individuals (in this case neural network parameter vectors) are selected to be potential parents.

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