Carlos Segura

Optimizing the Configuration of a Broadcast Protocol through Parallel Cooperation of Multi-objective Evolutionary Algorithms

This work presents an optimization approach for the broadcast operation in MANETs based on the DFCN protocol. Such approach involves a multi-objective optimization that has been tackled through the cooperation of a team of evolutionary algorithms. The proposed optimization model is a hybrid algorithm that combines a parallel island-based scheme with a hyperheuristic approach. The model includes an adaptive property to dynamically change the algorithms being executed on each island. More computational resources are granted to the most suitable algorithms.The computational results obtained for a highway MANETs instance demonstrate the validity of the proposed model.

Gradient subspace approximation: a direct search method for memetic computing

The hybridization of evolutionary algorithms and local search techniques as, e.g., mathematical programming techniques, also referred to as memetic algorithms, has caught the interest of many researchers in the recent past. Reasons for this include that the resulting algorithms are typically robust and reliable since they take the best of both worlds. However, one crucial drawback of such hybrids is the relatively high cost of the local search techniques since many of them require the gradient or even the Hessian at each candidate solution. Here, we propose an alternative way to compute search directions by exploiting the neighborhood information. That is, for a given point within a population

The importance of diversity in the application of evolutionary algorithms to the Sudoku problem

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The importance of the individual encoding in memetic algorithms with diversity control applied to large Sudoku puzzles

P, the neighboring solutions in

A fuzzy logic controller applied to a diversity-based multi-objective evolutionary algorithm for single-objective optimisation

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The Gradient Subspace Approximation as Local Search Engine within Evolutionary Multi-objective Optimization Algorithms

P are used to compute the most greedy search direction out of the given data. The method is hence particularly interesting for the usage within population-based search strategies since the search directions come ideally for free in terms of additional function evaluations. In this study, we analyze the novel method first as a stand-alone algorithm and show further on its benefit as a local searcher within differential evolution.