The exploration/exploitation tradeoff in dynamic cellular genetic algorithms

By Enrique Alba, Bernabé Dorronsoro. IEEE Transactions on Evolutionary Computation, 2005

426 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/6e6b48acbc60544367bf7cbc9a183b380194d8d7

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In Artificial Intelligence

1. Gaurav Dhiman
2. Vijay Kumar
3. Jun Zhang
4. Zhi-hui Zhan
5. Yun Li
6. Jingjing Li
7. Yaochu Jin
8. Yue-jiao Gong

In Computational Theory and Mathematics

1. Gaurav Dhiman
2. Vijay Kumar
3. Zhi-hui Zhan
4. Jun Zhang
5. Amandeep Kaur
6. Yun Li
7. Yaochu Jin
8. Jingjing Li

Citations in the corpus

• Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm (2015, background): “Exploration and exploitation are two conflicting milestones where promoting one results in degrading the other [14].”
• Tuna Swarm Optimization: A Novel Swarm-Based Metaheuristic Algorithm for Global Optimization (2021, background): “ere is a common feature of all these metaheuristic algorithms that rely on exploration and exploitation in the search space to find the optimal solution [41, 42].”
• Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems (2019, background): “Every optimization algorithm needs to address the exploration and exploitation of a search space [17] and maintains a good balance between exploration and exploitation.”
• STOA: A bio-inspired based optimization algorithm for industrial engineering problems (2019, background): “Every optimization algorithm needs to address the exploration and exploitation of a search space (Alba and Dorronsoro, 2005) and sustains a good balance between exploration and exploitation.”
• A Survey on the Vehicle Routing Problem and Its Variants (2012, methodology): “The authors [49] propose a cellular Genetic Algorithm (cGA) which is a kind of decentralized population based heuristic, which is used for solving CVRP, improving several of the best existing results so far in the literature.”
• Emperor penguin optimizer: A bio-inspired algorithm for engineering problems (2018, background): “The values of parameter M are varied from [1, 2, 3, 4].”
• Spotted hyena optimizer: A novel bio-inspired based metaheuristic technique for engineering applications (2017, background): “The main components of metaheuristic algorithms are exploration and exploitation [2,49] .”
• Parallel metaheuristics: recent advances and new trends (2012, methodology): “Some of the papers in this field run purely sequential , and try to design an efficient dynamics that makes a good trade - off between exploration and exploitation ( Alba and Dorronsoro , 2005 ; Nguyen et al .”
• Distributed evolutionary algorithms and their models: A survey of the state-of-the-art (2015, methodology): “Furher, an adaptive cellular GA is developed in [1], which adaptively djusts the neighborhood-to-topology ratio during the search proess according to some rules defined on the average fitness (AF), opulation entropy (PH), and their combination (AF + PH).”