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Found 12 papers, 4 papers with code
A Discrete Particle Swarm Optimizer for the Design of Cryptographic Boolean Functions
A Particle Swarm Optimizer for the search of balanced Boolean functions with good cryptographic properties is proposed in this paper.
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.
Evolutionary Strategies for the Design of Binary Linear Codes
The design of binary error-correcting codes is a challenging optimization problem with several applications in telecommunications and storage, which has also been addressed with metaheuristic techniques and evolutionary algorithms.
The Influence of Local Search over Genetic Algorithms with Balanced Representations
We continue the study of Genetic Algorithms (GA) on combinatorial optimization problems where the candidate solutions need to satisfy a balancedness constraint.
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.
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.
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.
Tip the Balance: Improving Exploration of Balanced Crossover Operators by Adaptive Bias
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.
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.
Balanced Crossover Operators in Genetic Algorithms
Furthermore, in two out of three crossovers, the "left-to-right" version performs better than the "shuffled" version.
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.
