Exploration and exploitation in evolutionary algorithms: A survey

By Matej Crepinsek, Shih-Hsi Liu, Marjan Mernik. CSUR, 2013

1,391 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/ca3da73a8f12c486cb698061d1ca55181535f5ad

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In Artificial Intelligence

In Computational Theory and Mathematics

Citations in the corpus

SCA: A Sine Cosine Algorithm for solving optimization problems (2016, background): “Regardless of the differences between algorithms in the field of stochastic population-based optimization, the common is the division of optimization process to two phases: exploration versus exploitation [62].”
An improved epsilon constraint-handling method in MOEA/D for CMOPs with large infeasible regions (2017, methodology): “Compared with the original ε-constrained method (Takahama and Sakai 2006), the proposed method can keep a good balance in the search between the feasible and infeasible regions (Črepinšek et al. 2013).”
Many-Objective Evolutionary Algorithms (2015, background): “…selection pressure, three grid-based criteria—grid ranking, grid crowding distance, and grid coordinate point distance—are introduced into GrEA [Yang et al. 2013] to help maintain the distribution among solutions, as it is important to balance convergence and diversity [ ˇCrepinˇsek et al. 2013].”
An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges (2023, background): “These are diametrically opposed to one another, so how do you balance them for the best performance? (Črepinšek et al. 2013)”
Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems (2020, methodology): “Nevertheless, in the absence of direct exploration-exploitation measurement approach [42], this study follows the method proposed in [41].”
Optimizing connection weights in neural networks using the whale optimization algorithm (2016, methodology): “In contrast to gradient algorithms, meta-heuristics show higher efficiency in avoiding local minima (Črepinšek et al. 2013; Gang 2013; Mirjalili et al. 2012).”
Equilibrium optimizer: A novel optimization algorithm (2020, positive): “From exploration– exploitation viewpoint, an increase in diversity means that solutions are very different, which indicates that an algorithm is in exploration phase, while a decrease means solutions are within a certain neighborhood, thus showing exploitation behavior [30].”
Honey Badger Algorithm: New metaheuristic algorithm for solving optimization problems (2022, background): “Exploration and exploitation are the two major components of any search strategy [9].”
Bio-inspired computation: Where we stand and what's next (2019, background): “the balance between their diversification (exploration) and intensification (exploitation) capabilities [65,66].”
Evolutionary algorithms and their applications to engineering problems (2020, background): “A survey about exploration and exploitation in EAs can be found in [114].”
Parameter Control in Evolutionary Algorithms: Trends and Challenges (2015, background): “Some authors recognized the importance of these issues and offered a (partial) solution, see [9] and [197], but these attempts did not receive the attention they deserved.”