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Found 19 papers, 11 papers with code
Evolving Benchmark Functions to Compare Evolutionary Algorithms via Genetic Programming
In this study, we use Genetic Programming (GP) to compose new optimization benchmark functions.
AbCD: A Component-wise Adjustable Framework for Dynamic Optimization Problems
Using this framework, we investigate components that were proposed in several popular DOP algorithms.
Multiobjective Evolutionary Component Effect on Algorithm behavior
In terms of decision space behavior, we see a diverse set of the trajectories of the STNs in the analytical artificial problems.
Co-evolving morphology and control of soft robots using a single genome
While our approach is more realistic and doesn't require an arbitrary separation of processes during evolution, it also makes the problem more complex because the search space for this single genome becomes larger and any mutation to the genome affects "brain" and the "body" at the same time.
An agent-based approach to procedural city generation incorporating Land Use and Transport Interaction models
We apply the knowledge of urban settings established with the study of Land Use and Transport Interaction (LUTI) models to develop reward functions for an agent-based system capable of planning realistic artificial cities.
Knowledge-Driven Program Synthesis via Adaptive Replacement Mutation and Auto-constructed Subprogram Archives
We compare the proposed method with PushGP, as well as a method using subprograms manually extracted by a human.
A Novel Weighted Ensemble Learning Based Agent for the Werewolf Game
This communication game requires the playing agents to be very sophisticated to win.
Component-wise Analysis of Automatically Designed Multiobjective Algorithms on Constrained Problems
This study introduces a new methodology to investigate the effects of the final configuration of an automatically designed algorithm.
Search Trajectories Networks of Multiobjective Evolutionary Algorithms
Understanding the search dynamics of multiobjective evolutionary algorithms (MOEAs) is still an open problem.
Faster Convergence in Multi-Objective Optimization Algorithms Based on Decomposition
MOEA/D with Partial Update can mitigate common problems related to population size choice with better convergence speed in most MOPs, as shown by the results of hypervolume and number of unique non-dominated solutions, the anytime performance and Empirical Attainment Function indicates.
MOEA/D with Adaptative Number of Weight Vectors
Thus, our adaptive mechanism mitigates problems related to the choice of the number of weight vectors in MOEA/D, increasing the final performance of MOEA/D by filling empty areas of the objective space while avoiding premature stagnation of the search progress.
Exploring Constraint Handling Techniques in Real-world Problems on MOEA/D with Limited Budget of Evaluations
Finding good solutions for Multi-objective Optimization (MOPs) Problems is considered a hard problem, especially when considering MOPs with constraints.
Solving Portfolio Optimization Problems Using MOEA/D and Levy Flight
Portfolio optimization is a financial task which requires the allocation of capital on a set of financial assets to achieve a better trade-off between return and risk.
MOEA/D with Random Partial Update Strategy
Recent studies on resource allocation suggest that some subproblems are more important than others in the context of the MOEA/D, and that focusing on the most relevant ones can consistently improve the performance of that algorithm.
Classification of EEG Signals using Genetic Programming for Feature Construction
The analysis of electroencephalogram (EEG) waves is of critical importance for the diagnosis of sleep disorders, such as sleep apnea and insomnia, besides that, seizures, epilepsy, head injuries, dizziness, headaches and brain tumors.
Data Augmentation Using GANs
In this paper we propose the use of Generative Adversarial Networks (GAN) to generate artificial training data for machine learning tasks.
Classification of X-Ray Protein Crystallization Using Deep Convolutional Neural Networks with a Finder Module
We compared our crystallization image recognition method with a high precision method using Inception-V3.
The MOEADr Package - A Component-Based Framework for Multiobjective Evolutionary Algorithms Based on Decomposition
We introduce the MOEADr package, which offers many of these variants as instantiations of a component-oriented framework.
