Reinforcement learning versus evolutionary computation: A survey on hybrid algorithms

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@Article{Drugan:2019:SwarmEC,

• author = "Madalina M. Drugan",
• title = "Reinforcement learning versus evolutionary computation: A survey on hybrid algorithms",
• journal = "Swarm and Evolutionary Computation",
• year = "2019",
• volume = "44",
• pages = "228--246",
• month = feb,
• keywords = "genetic algorithms, genetic programming, Reinforcement learning, Evolutionary computation, Natural paradigms, Hybrid algorithms, Survey",
• ISSN = "2210-6502",
• DOI = "doi:10.1016/j.swevo.2018.03.011",
• URL = "http://www.sciencedirect.com/science/article/pii/S2210650217302766",
• abstract = "A variety of Reinforcement Learning (RL) techniques blends with one or more techniques from Evolutionary Computation (EC) resulting in hybrid methods classified according to their goal, new focus, and their component methodologies. We denote this class of hybrid algorithmic techniques as the evolutionary computation versus reinforcement learning (ECRL) paradigm. This overview considers the entire spectrum of algorithmic aspects and proposes a novel methodology that analyses the technical resemblances and differences in ECRL. Our design analyses the motivation for each ECRL paradigm, the underlying natural models, the sub-component algorithmic techniques, as well as the properties of their ensemble.",
• notes = "Also known as \cite{DRUGAN2019228}",

}

Genetic Programming entries for Madalina M Drugan

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