Improving the Performance of NEAT Related Algorithm via Complexity Reduction in Search Space

Mohabeer, Heman; Soyjaudah, K. M. S.

doi:10.1007/978-3-319-00551-5_1

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…Mutations can happen in a variety of ways. The discussion in [1] is centered on making the search space less complex. Reducing the crossover rate in the algorithm facilitates learning.…”

Section: Neuroevolution Of Augmenting Topologies (Neat)mentioning

confidence: 99%

A Review on NEAT and Other Reinforcement Algorithms in Robotics

Anil

2024

IJSREM

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Artificial neural networks, or ANNs, find widespread use in real-world scenarios ranging from pattern recognition to robotics control. Choosing an architecture (which includes a model for the neurons) and a learning algorithm are important decisions when building a neural network for a particular purpose. An automated method for resolving these issues is provided by evolutionary search techniques.Genetic algorithms are used to artificially evolve neural networks in a process known as neuroevolution (NE), which has shown great promise in solving challenging reinforcement learning problems. This study offers a thorough review of the state-of-the-art techniques for evolving artificial neural networks (ANNs), with a focus on optimizing their performance-enhancing capabilities. Key Words: Artificial neural networks,Neuroevolution, NeuroEvolution of Augmenting Topologies,Brain computer interface.

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“…Mutations can happen in a variety of ways. The discussion in [1] is centered on making the search space less complex. Reducing the crossover rate in the algorithm facilitates learning.…”

Section: Neuroevolution Of Augmenting Topologies (Neat)mentioning

confidence: 99%

A Review on NEAT and Other Reinforcement Algorithms in Robotics

Anil

2024

IJSREM

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“…In case of gene mismatch, the contribution is made by the more fit parent. In case they are equally fit, the gene is inherited from the parents randomly [26]. For the NEAT algorithm, this process is carried out within the network weight vectors to optimize the connection weights that determine the functionality of a network [25].…”

Section: Crossovermentioning

confidence: 99%

An Efficient Application of Neuroevolution for Competitive Multiagent Learning

Menon

2021

TMLAI

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Multiagent systems provide an ideal environment for the evaluation and analysis of real-world problems using reinforcement learning algorithms. Most traditional approaches to multiagent learning are affected by long training periods as well as high computational complexity. NEAT (NeuroEvolution of Augmenting Topologies) is a popular evolutionary strategy used to obtain the best performing neural network architecture often used to tackle optimization problems in the field of artificial intelligence. This paper utilizes the NEAT algorithm to achieve competitive multiagent learning on a modified pong game environment in an efficient manner. The competing agents abide by different rules while having similar observation space parameters. The proposed algorithm utilizes this property of the environment to define a singular neuroevolutionary procedure that obtains the optimal policy for all the agents. The compiled results indicate that the proposed implementation achieves ideal behaviour in a very short training period when compared to existing multiagent reinforcement learning models.

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