Geometric Semantic Genetic Programming (GSGP) is a state-of-the-art machine learning method based on evolutionary computation.
In the crowded environment of bio-inspired population-based meta-heuristics, the Salp Swarm Optimization (SSO) algorithm recently appeared and immediately gained a lot of momentum.
Tasks related to Natural Language Processing (NLP) have recently been the focus of a large research endeavor by the machine learning community.
In the second experiment, we train a GP convolutional predictor on two degraded images, removing around 20% of their pixels.
The objective of this paper is to define an effective strategy for building an ensemble of Genetic Programming (GP) models.
Genetic algorithms (GAs) are an optimization technique that has been successfully used on many real-world problems.
The usage of the proposed semantic stopping criteria in conjunction with the computation of optimal mutation/learning steps also results in small trees and neural networks.
One of the most challenging tasks when adopting Bayesian Networks (BNs) is the one of learning their structure from data.
Gene and protein networks are very important to model complex large-scale systems in molecular biology.