In the crowded environment of bio-inspired population-based metaheuristics, 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.
Experiments show that improving the exploration of the search space with this adaptive bias strategy is beneficial for the GA performances in terms of the number of optimal solutions found for the balanced nonlinear Boolean functions problem.
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.