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Training Feedforward Neural Networks Using Symbiotic Organisms Search Algorithm.

Haizhou Wu¹, Yongquan Zhou², Qifang Luo²

¹College of Information Science and Engineering, Guangxi University for Nationalities, Nanning 530006, China.

Computational Intelligence and Neuroscience

|January 21, 2017

Summary

This summary is machine-generated.

Symbiotic Organisms Search (SOS) effectively trains feedforward neural networks (FNNs). This novel metaheuristic algorithm demonstrates superior convergence speed and accuracy compared to other methods for FNN training.

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Area of Science:

Artificial Intelligence
Machine Learning
Optimization Algorithms

Background:

Training feedforward neural networks (FNNs) presents significant challenges in supervised learning.
Existing training algorithms often lack satisfactory efficiency and performance.
Metaheuristic algorithms offer potential solutions for complex optimization problems in machine learning.

Purpose of the Study:

To introduce and evaluate the Symbiotic Organisms Search (SOS) algorithm as a novel method for training FNNs.
To assess the performance of SOS in supervised learning tasks.
To compare SOS against established metaheuristic algorithms for FNN training.

Main Methods:

The Symbiotic Organisms Search (SOS) algorithm, inspired by ecological symbiotic interactions, was applied to train FNNs.
Eight diverse datasets from the UCI machine learning repository were utilized for empirical evaluation.
Performance was benchmarked against seven other metaheuristic algorithms.

Main Results:

SOS demonstrated a faster convergence speed in training FNNs compared to the seven other metaheuristic algorithms.
FNNs trained using the SOS method achieved higher accuracy than those trained by most of the compared algorithms.
The robustness and effectiveness of SOS for FNN training were empirically validated.

Conclusions:

Symbiotic Organisms Search (SOS) is a powerful and efficient metaheuristic algorithm for training feedforward neural networks.
SOS offers significant advantages in terms of convergence speed and predictive accuracy for supervised learning tasks.
The findings suggest SOS as a promising alternative for optimizing FNNs in machine learning applications.