Continuous Action Learning Automata Game Optimizer Applied to Convolutional Neural Networks

James Lindsay, Sidney Givigi

Abstract: This paper explores the application of the Continuous Action Learning Automata (CALA) game optimizer to Convolutional Neural Networks (CNNs) for image classification tasks. The CALA game optimizer, initially developed for training Artificial Neural Networks (ANNs), offers a non-gradient descent-based optimization approach that can adapt to different network architectures and activation functions. Leveraging the versatility of the CALA game optimizer, we investigate its performance on CNNs, specifically targeting image recognition within the MNIST dataset. The paper discusses the rationale behind using the CALA game optimizer for CNNs, including its ability to accommodate various activation functions and deeper network architectures. Experimental results demonstrate the efficacy of CALA in training CNNs, showcasing its flexibility and effectiveness in optimizing network parameters for image classification tasks.