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Found 12 papers, 3 papers with code
Local Search, Semantics, and Genetic Programming: a Global Analysis
In recent years, a new mutation operator, Geometric Semantic Mutation with Local Search (GSM-LS), has been proposed to include a local search step in the mutation process based on the idea that performing a linear regression during the mutation can allow for a faster convergence to good-quality solutions.
Combining Genetic Programming and Particle Swarm Optimization to Simplify Rugged Landscapes Exploration
The proposed algorithm, called the GP-FST-PSO Surrogate Model, achieves satisfactory results in both the search for the global optimum and the production of a visual approximation of the original benchmark function (in the 2-dimensional case).
The Effect of Multi-Generational Selection in Geometric Semantic Genetic Programming
Among the evolutionary methods, one that is quite prominent is Genetic Programming, and, in recent years, a variant called Geometric Semantic Genetic Programming (GSGP) has shown to be successfully applicable to many real-world problems.
GSGP-CUDA -- a CUDA framework for Geometric Semantic Genetic Programming
Geometric Semantic Genetic Programming (GSGP) is a state-of-the-art machine learning method based on evolutionary computation.
Salp Swarm Optimization: a Critical Review
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.
CoInGP: Convolutional Inpainting with Genetic Programming
In the second experiment, we train a GP convolutional predictor on two degraded images, removing around 20% of their pixels.
Towards an evolutionary-based approach for natural language processing
Tasks related to Natural Language Processing (NLP) have recently been the focus of a large research endeavor by the machine learning community.
Pruning Techniques for Mixed Ensembles of Genetic Programming Models
The objective of this paper is to define an effective strategy for building an ensemble of Genetic Programming (GP) models.
A Distance Between Populations for n-Points Crossover in Genetic Algorithms
Genetic algorithms (GAs) are an optimization technique that has been successfully used on many real-world problems.
Unsure When to Stop? Ask Your Semantic Neighbors
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.
Learning the structure of Bayesian Networks: A quantitative assessment of the effect of different algorithmic schemes
One of the most challenging tasks when adopting Bayesian Networks (BNs) is the one of learning their structure from data.
Combining Bayesian Approaches and Evolutionary Techniques for the Inference of Breast Cancer Networks
Gene and protein networks are very important to model complex large-scale systems in molecular biology.
