Parameter control in evolutionary algorithms: Trends and challenges
G Karafotias, M Hoogendoorn… - IEEE Transactions on …, 2014 - ieeexplore.ieee.org
More than a decade after the first extensive overview on parameter control, we revisit the
field and present a survey of the state-of-the-art. We briefly summarize the development of …
field and present a survey of the state-of-the-art. We briefly summarize the development of …
Hyper-heuristics: A survey of the state of the art
Hyper-heuristics comprise a set of approaches that are motivated (at least in part) by the
goal of automating the design of heuristic methods to solve hard computational search …
goal of automating the design of heuristic methods to solve hard computational search …
A classification of hyper-heuristic approaches: revisited
Hyper-heuristics comprise a set of approaches that aim to automate the development of
computational search methodologies. This chapter overviews previous categorisations of …
computational search methodologies. This chapter overviews previous categorisations of …
[LIVRE][B] Introduction to evolutionary computing
This is the second edition of our 2003 book. It is primarily a book for lecturers and graduate
and undergraduate students. To this group the book offers a thorough introduction to …
and undergraduate students. To this group the book offers a thorough introduction to …
Adaptive operator selection with bandits for a multiobjective evolutionary algorithm based on decomposition
Adaptive operator selection (AOS) is used to determine the application rates of different
operators in an online manner based on their recent performances within an optimization …
operators in an online manner based on their recent performances within an optimization …
Multi-objective optimization techniques: a survey of the state-of-the-art and applications: Multi-objective optimization techniques
In recent years, multi-objective optimization (MOO) techniques have become popular due to
their potentiality in solving a wide variety of real-world problems, including bioinformatics …
their potentiality in solving a wide variety of real-world problems, including bioinformatics …
A systematic literature review of adaptive parameter control methods for evolutionary algorithms
Evolutionary algorithms (EAs) are robust stochastic optimisers that perform well over a wide
range of problems. Their robustness, however, may be affected by several adjustable …
range of problems. Their robustness, however, may be affected by several adjustable …
Evolutionary algorithm parameters and methods to tune them
Finding appropriate parameter values for evolutionary algorithms (EA) is one of the
persisting grand challenges of the evolutionary computing (EC) field. In general, EC …
persisting grand challenges of the evolutionary computing (EC) field. In general, EC …
[HTML][HTML] SATenstein: Automatically building local search SAT solvers from components
Designing high-performance solvers for computationally hard problems is a difficult and
often time-consuming task. Although such design problems are traditionally solved by the …
often time-consuming task. Although such design problems are traditionally solved by the …
Adaptive strategy selection in differential evolution for numerical optimization: an empirical study
Differential evolution (DE) is a versatile and efficient evolutionary algorithm for global
numerical optimization, which has been widely used in different application fields. However …
numerical optimization, which has been widely used in different application fields. However …