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Application of Nature Inspired Algorithms for Wireless Multi-hop Ad Hoc Network Optimization Problems in Disaster Response Scenarios
Published in Phan Cong Vinh, Nature-Inspired Networking: Theory and Applications, 2018
Jesús Sánchez-García, José Manuel García-Campos, Daniel Gutiérrez Reina, Sergio Luis Toral, Federico Barrero
Moreover, other nature inspired techniques have not been commonly applied to wireless multi-hop ad hoc networks. Some of these techniques are Cooperative Coevolution [70] and Genetic Programming [71]. There is the challenge in checking if these techniques can outperform the ones presented in this chapter.
Reducing the total tardiness by Seru production: model, exact and cooperative coevolution solutions
Published in International Journal of Production Research, 2020
Wei Sun, Yang Yu, Qi Lou, Junwei Wang, Yuechao Guan
In the cooperative coevolution algorithm, a cooperative mechanism is built between the two evolution algorithms. Evaluation of complete solutions is achieved through cooperation, i.e. all subpopulations share a subset of their current partial solutions (Yanga, Ke, and Xin 2008; Dorronsoro et al. 2013; Shang et al. 2014; Trunfio, Topa, and Was 2016). One population of the two evolution algorithms evolves with the assistance of the best individuals in the other population. The evolutions of the two algorithms are alternate. During the evolution of seru formation, the currently best individual of seru scheduling is used. Subsequently, the currently best individual of seru formation is used to evolve seru scheduling. The finally obtained solution is the combination of the currently best seru formation and the currently best seru scheduling. GA with the local search is used to improve effectiveness (Grosan and Abraham 2007).
Cooperative coevolution of expressions for (r,Q) inventory management policies using genetic programming
Published in International Journal of Production Research, 2020
Rui L. Lopes, Gonçalo Figueira, Pedro Amorim, Bernardo Almada-Lobo
This article proposes a new method for the approximation of the policy parameters, based on closed-form expressions. Contrarily to the numerical optimisation algorithms, these expressions are fast to compute and their time does not increase with the order size. Tree-based Genetic Programming (GP) is used as the representation, a supervised meta-heuristic that models programmes as trees, and applies the principles of natural selection towards the automatic programming of computers. In this case, coevolution learns expressions for both parameters reorder point and order quantity of an inventory-control policy. In particular, cooperative coevolution learns both parameters in parallel populations that adapt to each other along the evolutionary run by sharing the fitness assessment. Given the relationship between the policy parameters, coevolution provides a suitable decomposition for this problem, and the parallel co-adaptation of the parameters is able to successfully capture their interdependence, generating better approximations. To the best of the authors' knowledge, this is the first work that evolves both parameters in parallel.
Scanning the Issue
Published in IETE Journal of Research, 2022
In “Feature Selection and Instance Selection from Clinical Datasets using Co-operative Co-evolution and Classification using Random Forest” the authors present feature selection and instance selection methods for Clinical Decision Support System (CDSS). An approach of cooperative coevolution is taken wherein the two methods are considered as independent sub-problems. Several datasets are used in the paper to determine the accuracy of inferencing.