Cellular Genetic Algorithms

Alba, Enrique

doi:10.1007/978-0-387-77610-1

Cited by 57 publications

(1 citation statement)

References 134 publications

(306 reference statements)

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“…It can be observed that premature convergence is a severe problem in evolutionary algorithms, which is mainly due to the lack of genetic diversity of the individuals of a population. There are several techniques that address the problem of maintaining a high genetic diversity by either trying to prevent a population to converge [island-diffusion-, or cellular-structured EAs (Whitley 1993;Alba and Dorronsoro 2008;Cohoon et al 1987) as well as crowding (Mahfoud 1992), parallel EAs (Mühlenbein et al 1991), or fitness-sharing (Spears 1994)] or by repairing an already converged population. As for the former, it is necessary to invest effort for setting up and maintaining appropriate structures to, e.g., maintain neighborhoods of cellular-or diffusion-based EAs.…”

Section: Population Injectionmentioning

confidence: 99%

An evolutionary hybrid search heuristic for monitor placement in communication networks

et al. 2019

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In this paper, a heuristic method for the optimal placement of monitors in communication networks is proposed. In order to be able to make informed decisions, a first step towards securing a communication network is deploying an adequate sensor infrastructure. However, appropriate monitoring should take into account the priority of the communication links as well as the location of monitors. The goal is to cover the whole network with the minimum investment and impact on performance, i.e., the optimal amount and positions of monitors in the network. In order to be able to counteract dynamic changes in those networks, e.g., link failures, attacks, or entering and leaving nodes, this work focuses on swiftly obtaining results having an acceptable quality. To achieve this goal, an effective hybrid search heuristic is introduced, combining the computational efficiency of a greedy local search method with the robustness of evolution-based heuristics. It is shown that this approach works well on synthetic benchmark instances and real-world network models, having up to millions of nodes, by comparing the performance of a common evolutionary algorithm (EA) to its hybrid search counterparts. It is observed that the hybrid search heuristics produce good solutions on the instances under study in a reasonable amount of time. Regarding the fitness of the solutions found, the hybrid approach outperforms the common EA in all the experiments. Moreover, on all problem instances, the hybrid EA finds the best solutions significantly earlier in the search process, which is key when monitoring a communication infrastructure which is subject to change.

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Section: Population Injectionmentioning

confidence: 99%

An evolutionary hybrid search heuristic for monitor placement in communication networks

et al. 2019

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Running Genetic Algorithms in the Edge: A First Analysis

Morell¹,

Alba²

2018

Advances in Artificial Intelligence

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Nowadays, the volume of data produced by different kinds of devices is continuously growing, making even more difficult to solve the many optimization problems that impact directly on our living quality. For instance, Cisco projected that by 2019 the volume of data will reach 507.5 zettabytes per year, and the cloud traffic will quadruple. This is not sustainable in the long term, so it is a need to move part of the intelligence from the cloud to a highly decentralized computing model. Considering this, we propose a ubiquitous intelligent system which is composed by different kinds of endpoint devices such as smartphones, tablets, routers, wearables, and any other CPU powered device. We want to use this to solve tasks useful for smart cities. In this paper, we analyze if these devices are suitable for this purpose and how we have to adapt the optimization algorithms to be efficient using heterogeneous hardware. To do this, we perform a set of experiments in which we measure the speed, memory usage, and battery consumption of these devices for a set of binary and combinatorial problems. Our conclusions reveal the strong and weak features of each device to run future algorihms in the border of the cyber-physical system.

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