Learning classifier systems: a complete introduction, review, and roadmap
RJ Urbanowicz, JH Moore - Journal of Artificial Evolution and …, 2009 - Wiley Online Library
If complexity is your problem, learning classifier systems (LCSs) may offer a solution. These
rule‐based, multifaceted, machine learning algorithms originated and have evolved in the …
rule‐based, multifaceted, machine learning algorithms originated and have evolved in the …
Evolutionary computation for reinforcement learning
S Whiteson - Reinforcement Learning: State-of-the-art, 2012 - Springer
Algorithms for evolutionary computation, which simulate the process of natural selection to
solve optimization problems, are an effective tool for discovering high-performing …
solve optimization problems, are an effective tool for discovering high-performing …
Reusing building blocks of extracted knowledge to solve complex, large-scale boolean problems
Evolutionary computation techniques have had limited capabilities in solving large-scale
problems due to the large search space demanding large memory and much longer training …
problems due to the large search space demanding large memory and much longer training …
Multimodal data and machine learning for surgery outcome prediction in complicated cases of mesial temporal lobe epilepsy
N Memarian, S Kim, S Dewar, J Engel Jr… - Computers in biology and …, 2015 - Elsevier
Background This study sought to predict postsurgical seizure freedom from pre-operative
diagnostic test results and clinical information using a rapid automated approach, based on …
diagnostic test results and clinical information using a rapid automated approach, based on …
A metaheuristic perspective on learning classifier systems
Within this book chapter we summarize Learning Classifier Systems (LCSs), a family of rule-
based learning systems with a more than forty-year-long research history, and differentiate …
based learning systems with a more than forty-year-long research history, and differentiate …
Theoretical XCS parameter settings of learning accurate classifiers
XCS is the most popular type of Learning Classifier System, but setting optimum parameter
values is more of an art than a science. Early theoretical work required the impractical …
values is more of an art than a science. Early theoretical work required the impractical …
Learning optimality theory for accuracy-based learning classifier systems
Evolutionary computation has brought great progress to rule-based learning but this
progress is often blind to the optimality of the system design. This article theoretically reveals …
progress is often blind to the optimality of the system design. This article theoretically reveals …
A systematic procedure to optimize integrated solar combined cycle power plants (ISCCs)
MT Mabrouk, A Kheiri, M Feidt - Applied Thermal Engineering, 2018 - Elsevier
This study aims to find the optimal way to integrate a parabolic trough solar field into a gas
fired combined cycle. The combined cycle studied here is composed of a gas turbine and a …
fired combined cycle. The combined cycle studied here is composed of a gas turbine and a …
A survey of formal theoretical advances regarding XCS
Learning Classifier Systems (LCSs) are a unique machine learning paradigm. The probably
most well-known and investigated instance of these is XCS. LCSs, and with them, XCS …
most well-known and investigated instance of these is XCS. LCSs, and with them, XCS …
Range-adaptive standoff recognition of explosive fingerprints on solid surfaces using a supervised learning method and laser-induced breakdown spectroscopy
I Gaona, J Serrano, J Moros, JJ Laserna - Analytical chemistry, 2014 - ACS Publications
The distance between the sensor and the target is a particularly critical factor for an issue as
crucial as explosive residues recognition when a laser-assisted spectroscopic technique …
crucial as explosive residues recognition when a laser-assisted spectroscopic technique …