Toward Evolving Neural Networks using Bio-Inspired Algorithms
نویسندگان
چکیده
The SWarm Intelligence-based Reinforcement Learning (SWIRL) method is proposed in this paper to efficiently generate Artificial Neural Network (ANN) based solutions to various problems. Basically, two swarm intelligence based algorithms are combined together in SWIRL to train the ANN models. Ant Colony Optimization (ACO) is applied to optimize ANN topology, while Particle Swarm Optimization (PSO) is applied to adjust ANN connection weights. To evaluate the performance of the ANN models trained by SWIRL, the XOR and double pole balance problem are utilized as case studies. Extensive simulation results successfully demonstrate that SWIRL offers performance that is competitive with modern neuroevolutionary techniques, as well as its viability for realworld problems.
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