Titlebook: Applications of Evolutionary Computing; EvoWorkshops 2009: E Mario Giacobini,Anthony Brabazon,Penousal Machado Conference proceedings 2009

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Evolving High-Speed, Easy-to-Understand Network Intrusion Detection Rules with Genetic Programminghave to make decisions regarding the nature of the activity observed in a system. This has traditionally been one of the central areas of research in the field, and most of the solutions proposed so far have relied in one way or another upon some form of data mining–with the exception, of course, of

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Ant Routing with Distributed Geographical Localization of Knowledge in Ad-Hoc Networkss is connected with geographical locations and exchanged between nodes as they move across the network. The proposed scheme refers to the usage of a pheromone by real ants: the pheromone is left on the ground and used by ants in its surroundings. Our experiments show that the proposed solution may i

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Discrete Particle Swarm Optimization for Multiple Destination Routing Problemsn to be NP-complete and the traditional heuristics (e.g., the SPH, DNH and ADH) are inefficient in solving it. The particle swarm optimization (PSO) is an efficient global search algorithm and is promising in dealing with complex problems. This paper extends the PSO to a discrete PSO and uses the DP

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Combining Back-Propagation and Genetic Algorithms to Train Neural Networks for Ambient Temperature M not available. Indeed, we combine the Back-Propagation (BP) algorithm and the Simple Genetic Algorithm (GA) in order to effectively train neural networks in such a way that the BP algorithm initialises a few individuals of the GA’s population. Experiments have been performed over all the available

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Estimating the Concentration of Nitrates in Water Samples Using PSO and VNS Approachesestimation of the concentration of nitrates in water. Our study starts from the definition a model for the Ultra-violet spectrophotometry transmittance curves of water samples with nitrate content. This model consists in a mixture of polynomial, Fermi and Gaussian functions. Then, optimization algor

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Optimal Irrigation Scheduling with Evolutionary Algorithmsopulation’s food supply. In this paper, we compare five Evolutionary Algorithms (real valued Genetic Algorithm, Particle Swarm Optimization, Differential Evolution, and two Evolution Strategy-based Algorithms) on the problem of optimal deficit irrigation. We also introduce three different constraint

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Jean-Louis Vincent,Jesse B. Hall internal structure may change in order to adapt to the environment, according to an adaptation plan. To formalize this approach, we propose the Adaptive Evolutionary Framework (AEF). Moreover, we apply it to the problem of sharing consumable resources, such as CPU, RAM, and disk space.

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Australia, Coastal Paleolakes of “Swanland” handling strategies that deal with the constraints which arise from the limited amount of irrigation water. We show that Differential Evolution and Particle Swarm Optimization are able to optimize irrigation schedules achieving results which are extremely close to the theoretical optimum.