A MOS-based dynamic memetic differential evolution algorithm for continuous optimization: a scalability test
Continuous optimization is one of the areas with more activity in the field of heuristic
optimization. Many algorithms have been proposed and compared on several benchmarks …
optimization. Many algorithms have been proposed and compared on several benchmarks …
Integrating estimation of distribution algorithms versus Q-learning into Meta-RaPS for solving the 0-1 multidimensional knapsack problem
Finding near-optimal solutions in an acceptable amount of time is a challenge when
develo** sophisticated approximate approaches. A powerful answer to this challenge …
develo** sophisticated approximate approaches. A powerful answer to this challenge …
[PDF][PDF] A framework for hybrid dynamic evolutionary algorithms: multiple offspring sampling (MOS)
ALT de la Fuente, JMP Sánchez - 2009 - oa.upm.es
Abstract Evolutionary Algorithms (EAs) are a set of optimization techniques that have
become incredibly popular in the last decades. As they are general purpose algorithms, they …
become incredibly popular in the last decades. As they are general purpose algorithms, they …
Benchmarking a hybrid DE-RHC algorithm on real world problems
Continuous optimization is one of the most active research lines in evolutionary and
metaheuristic algorithms. Through CEC 2005 to CEC 2010 competitions, many different …
metaheuristic algorithms. Through CEC 2005 to CEC 2010 competitions, many different …
Bayesian classifiers applied to the Tennessee Eastman process
Fault diagnosis includes the main task of classification. Bayesian networks (BNs) present
several advantages in the classification task, and previous works have suggested their use …
several advantages in the classification task, and previous works have suggested their use …
Hybridizing A Genetic Algorithm With Reinforcement Learning for Automated Design of Genetic Algorithms
The automated design of optimization techniques holds great promise for advancing state-of-
the-art optimization techniques and it has already taken over the manual design by human …
the-art optimization techniques and it has already taken over the manual design by human …
Bayesian Networks for Identifying Semantic Relations in a Never-Ending Learning System
A new paradigm of Machine Learning named Never-Ending Learning has been proposed
through a system known as NELL (Never-Ending Language Learning). The major idea of …
through a system known as NELL (Never-Ending Language Learning). The major idea of …
Learning Bayesian network structures using multiple offspring sampling
Variable Ordering (VO) plays an important role when inducing Bayesian Networks (BNs).
Previous works in the literature suggest that it is worth pursuing the use of evolutionary …
Previous works in the literature suggest that it is worth pursuing the use of evolutionary …
A parallel scheduling algorithm for reinforcement learning in large state space
Q Liu, X Yang, L **g, J Li, J Li - Frontiers of Computer Science, 2012 - Springer
The main challenge in the area of reinforcement learning is scaling up to larger and more
complex problems. Aiming at the scaling problem of reinforcement learning, a scalable …
complex problems. Aiming at the scaling problem of reinforcement learning, a scalable …
A new methodology for the automatic creation of adaptive hybrid algorithms
Abstract Hybrid Evolutionary Algorithms are a promising alternative to deal with the problem
of selecting the most appropriate Evolutionary Algorithm for a specific problem. By means of …
of selecting the most appropriate Evolutionary Algorithm for a specific problem. By means of …