Differential Evolution: In Search of Solutions

By V. Feoktistov. 2006

475 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/a1ec4be11607663fbbf8803a5d0e9b2ae46c5a8d

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Citations in the corpus

Bio-inspired computation: Where we stand and what's next (2019, background): “However, it is also known that some loss of diversity is required for some heuristics to converge properly [68, 69].”
LSHADE with semi-parameter adaptation hybrid with CMA-ES for solving CEC 2017 benchmark problems (2017, background): “However, it may slow down the search [20].”
Recent advances in differential evolution: a survey and experimental analysis (2010, methodology): “Subsequently, the Q-test (Q stands for Quality) described in Feoktistov (2006) has been applied.”
Population size reduction for the differential evolution algorithm (2008, methodology): “Differential Evolution (DE) is a floating-point encoding evolutionary algorithm for global optimization over continuous spaces [11, 18, 22, 27], which can also work with discrete variables.”
Enhancing Differential Evolution Utilizing Proximity-Based Mutation Operators (2011, background): “Several DE variants that either introduce new mutation strategies or new self-adaptive techniques to tune the control parameters have been recently proposed [12], [15], [18], [22], [25]–[27], [31], [34], [35], [39]–[44].”
Mortality surface by means of continuous time cohort models (2013, background): “A practical and detailed exposition of Differential Evolution can be found in (Feoktistov, 2006).”
Optimal power flow using differential evolution algorithm (2009, methodology): “The main features of the DE algorithm can be stated as follows [24,25]:”