Rafael Nogueras

• PhD in Computer Science
• University of Malaga

Multimemetic algorithms (MMAs) are a subclass of memetic algorithms in which memes are explicitly attached to genotypes and evolve alongside them. We analyze the propagation of memes in MMAs with a spatial structure. For this purpose we propose an idealized selecto-Lamarckian model that only features selection and local improvement, and study under...

Multimemetic algorithms (MMAs) are memetic algorithms that explicitly exploit the evolution of memes, i.e., non-genetic expressions of problem-solving strategies. We aim to study their deployment on an unstable environment with complex topology and volatile resources. We analyze their behavior and performance on environments with different churn ra...

The use of parallel and distributed models of evolutionary algorithms (EAs) is widespread nowadays as a means to improve solution quality and reduce computational times when solving hard optimization problems. For this purpose, emergent computational environments such as P2P networks and desktop grids are offering a plethora of new opportunities bu...

Multimemetic algorithms (MMAs) are memetic algorithms in which memes (interpreted as non-genetic expressions of problem-solving strategies) are explicitly represented and evolved alongside genotypes. This process is commonly approached using the standard genetic procedures of recombination and mutation to manipulate directly information at the meme...

The design of models efficiently predicting the performance of a particular genetic algorithm on a given fitness landscape is a very important issue of practical interest. Virtual Genetic Algorithms (VGAs) constitute a statistical approach aimed at this objective. This work describes different improvements to the standard VGA model. These improvem...

We consider the use of island-based evolutionary algorithms (EAs) on fault-prone computational settings. More precisely, we consider scenarios plagued with correlated node failures. To this end, we use the sandpile model in order to induce such complex, correlated failures in the system. Several EA variants featuring self-adaptive capabilities aime...

Computational environments emerging from the pervasiveness of networked devices offer a plethora of opportunities and challenges. The latter arise from their dynamic, inherently volatile nature that tests the resilience of algorithms running on them. Here we consider the deployment of population-based optimization algorithms on such environments, u...

We consider the deployment of island-based evolutionary algorithms (EAs) on unstable networks whose nodes exhibit correlated failures. We use the sandpile model in order to induce such complex, correlated failures in the system. A performance analysis is conducted, comparing the results obtained in both correlated and non-correlated scenarios for i...

We consider the deployment of island-based evolutionary algorithms (EAs) on irregular computational environments plagued with different kind of glitches. In particular we consider the effect that factors such as network latency and transient process suspensions have on the performance of the algorithm. To this end, we have conducted an extensive ex...

The performance of island-based evolutionary algorithms is studied on unstable networks whose nodes exhibit complex correlated failures. Simple EAs have a significant performance degradation with respect to networks with uncorrelated failures, but the use of self-⋆ properties allows the EA to increase its resilience in this scenario.

We consider the deployment of island-based memetic algorithms (MAs) endowed with \(\text {self-}{\star }\) properties on unstable computational environments composed of a collection of computing nodes whose availability fluctuates. In this context, these properties refer to the ability of the MA to work autonomously in order to optimize its perform...

Optimization algorithms deployed on unstable computational environments must be resilient to the volatility of computing nodes. Different fault-tolerance mechanisms have been proposed for this purpose. We focus on the use of island-based multimemetic algorithms, namely memetic algorithms which explicitly represent and evolve memes alongside solutio...

The use of volatile decentralized computational platforms such as, e.g., peer-to-peer networks, is becoming an increasingly popular option to gain access to vast computing resources. Making an effective use of these resources requires algorithms adapted to such a changing environment, being resilient to resource volatility. We consider the use of a...

Optimization algorithms deployed on unstable computational environments must be resilient to the volatility of computing nodes. Different fault-tolerance mechanisms have been proposed for this purpose. We focus on the use of dynamic population sizes in the context of island-based multimemetic algorithms, namely memetic algorithms which explicitly r...

We study the behavior and performance of island-based multimemetic algorithms, namely memetic algorithms which explicitly represent and evolve memes alongside solutions, in unstable computational environments whose topology is modeled as scale-free networks, a pattern of connectivity observed in real-world networks, such as peer-to-peer systems. We...

The use of parallel and distributed models of metaheuristics has steadily settled in the last decades into an accessible mechanism for improving both the efficiency and the effectiveness of the search process.While population-based techniques can be readily deployed on dedicated distributed systems, their use on unstable computational environments...

Multimemetic algorithms (MMAs) are memetic algorithms that explicitly represent and evolve memes (computational representations of problem solving methods) as a part of solutions. We consider an island-based model of MMAs and provide a comparative analysis of six migrant selection strategies and two migrant replacement operators. We use a test suit...

This paper studies a self-organized framework for modeling dynamic topologies in spatially structured Evolutionary Algorithms (EAs). The model consists of a 2-dimensional grid of nodes where the individuals interact and self-organize into clusters. During the search process, the individuals move through the grid, following a pre-defined simple rule...

Multimemetic algorithms (MMAs) are memetic algorithms that explicitly exploit the evolution of memes, i.e., non-genetic expressions of problem-solving strategies. We consider a class of MMAs in which these memes are rewriting rules whose length can be fixed during the run of the algorithm or self-adapt during the search process. We analyze this sel...

Multimemetic algorithms (MMAs) are memetic algorithms that explicitly represent and evolve memes (computational representations of problem solving methods) as a part of solutions. We use an idealized selecto-Lamarckian model of MMAs in order to analyze the propagation of memes in spatially structured populations. To this end, we focus on the use of...

The potential fraud problems, international economic crisis and the crisis of trust in markets have affected financial institutions, which have tried to maintain customer trust in many different ways. To maintain these levels of trust they have been forced to make significant adjustments to economic structures, in efforts to recoup their investment...

Multimemetic algorithms (MMAs) are a subclass of memetic algorithms in which memes are explicitly attached to genotypes and evolve alongside them. We analyze the propagation of memes in MMAs with spatial structure. For this purpose we propose an idealized selecto-Lamarckian model that only features selection and local improvement, and study under w...

The potential fraud problems, international economic crisis and the crisis of confidence in markets have affected financial institutions, which have tried to maintain customer trust in many different ways. To maintain the trust level in financial institutions, the implementation of electronic banking for customers has been considered a successful s...

Genetic Algorithms (GAs) constitute a very efficient search model that has provided excellent results in different domains during the last fifty years. However, new methods offering additional possibilities are emerging. Estimation of distribution Algorithms (EDAs) are one of these methods. In this work, we study the combination of both approaches....

A statistical approach aimed at predicting the performance of a GA is presented. This approach is based on trying to mimic the fitness distribution of genetic operators. By modeling such fitness distributions, the effects of genetic operators can be simulated within the framework of virtual genetic algorithms (VGAs). An improved statistical model i...