Differential evolution algorithm with ensemble of parameters and mutation strategies

By Rammohan Mallipeddi, Ponnuthurai Nagaratnam Suganthan, Quan-Ke Pan, Mehmet Fatih Tasgetiren. Applied Soft Computing, 2011

1,324 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/948d6118ac13ac088074148f813ea14df8b3276b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Ponnuthurai Nagaratnam Suganthan
2. Swagatam Das
3. Pinar Çivicioglu
4. Seyedali Mirjalili
5. Xin-She Yang
6. Ryoji Tanabe
7. Janez Brest
8. Yaochu Jin

In Computational Theory and Mathematics

1. Ponnuthurai Nagaratnam Suganthan
2. Pinar Çivicioglu
3. Swagatam Das
4. Seyedali Mirjalili
5. Xin-She Yang
6. Ryoji Tanabe
7. Sankha Subhra Mullick
8. Yaochu Jin

Citations in the corpus

• Bio-inspired computation: Where we stand and what's next (2019, methodology): “DE [252,253], PSO [254], Artificial Bee Colony algorithms [255] or Biogeography-based optimization [256].”
• Enhancing Differential Evolution Utilizing Proximity-Based Mutation Operators (2011, methodology): “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].”
• Adaptive Operator Selection With Bandits for a Multiobjective Evolutionary Algorithm Based on Decomposition (2014, methodology): “It is also worth noting that some other methods consider the so-called multioperators, such as the ensemble-based method [25] and the composite operators [26].”
• Success-history based parameter adaptation for Differential Evolution (2013, methodology): “This set of comparisons does not include EPSDE (which is included in the CEC2013 comparisons above), because the original paper on EPSDE [6] uses a set of original benchmark”
• Differential evolution based on covariance matrix learning and bimodal distribution parameter setting (2014, background): “Mallipeddi et al. [22] proposed a DE with ensemble of control parameter settings and trial vector generation strategies (EPSDE).”
• Multiple documents summarization based on evolutionary optimization algorithm (2013, background): “According to Mani and Maybury (1999), automatic text summarization takes a partially structured source text from multiple texts written about the same topic, extracts information content from it, and presents the most important content to the user in a manner sensitive to the user’s needs.”
• Differential evolution with multi-population based ensemble of mutation strategies (2016, methodology): “In [31,33], the authors proposed a DE algorithm with an ensemble of mutation strategies and parameter values (EPSDE) which consists of a pool of mutation and crossover strategies and their associated parameter values.”
• Differential Evolution With Composite Trial Vector Generation Strategies and Control Parameters (2011, methodology): “CoDE differs from EPSDE [18] in the following major aspects.”
• An Adaptive Differential Evolution Algorithm With Novel Mutation and Crossover Strategies for Global Numerical Optimization (2012, background): “One or more of the proposed algorithmic components can be integrated with the recently developed ensemble schemes for DE [39], [40], with an objective of enhancing the overall performance of the ensemble DE. and international conference pro- ceedings.”
• Parameter Control in Evolutionary Algorithms: Trends and Challenges (2015, methodology): “A slightly varied approach was taken by Mallipeddi et al. [151] for the EPSDE.”
• Memetic algorithms and memetic computing optimization: A literature review (2012, neutral): “For example, in [119], a DE framework with an ensemble of mutation strategies and parameter setting is proposed.”
• Ensemble Classification and Regression-Recent Developments, Applications and Future Directions [Review Article] (2016, methodology): “We can also investigate the applicability of ensemble methods for evolutionary algorithms [181], [182].”
• Differential Evolution With Ranking-Based Mutation Operators (2013, background): “An ensemble of different mutation operators is also an interesting topic for improving the performance of DE, such as SaDE [18], EPSDE [20], SaJADE [21], CoDE [22], DE-SG [23], etc.”