Due to their structure, metaheuristics such as parallel evolutionary algorithms (PEA) are well suited to be run on parallel and distributed infrastructure, e.g. supercomputers. However, there are still many issues that are not well researched in ...

Practitioners frequently encounter the challenge of selecting the best optimization algorithm from a pool of options. However, why not, rather than selecting a single algorithm, let evolution determine the optimal combination of all algorithms? In this ...

As their underlying models are becoming larger and data-driven, an increasing number of modern real-world applications can be mathematically formulated as large-scale continuous optimization. In this paper, we propose a distributed evolution strategy (...

Many optimization problems tackled by evolutionary algorithms are not only computationally expensive, but also complicated with one or more sources of noise. One technique to deal with high computational overhead is parallelization. However, though the ...

Local optima networks (LONs) model the global distribution and connectivity pattern of local optima under given search operators. Recent research has looked at how recombination operators can jump from a pair of parents that are locally optimal to a new ...

In this paper, we make a first assessment of the performance of ActoDatA, a novel actor-based software library for distributed data analysis and machine learning in Java that we have recently developed. To do so we have implemented an evolutionary ...

Currently, there is a considerable variety of Evolutionary Algorithms (EAs) and due to their performances some of them become more popular. EAs can be implemented in different ways, such as the Island Model (IM). However, despite the good performance of ...

High-Intensity Focused Ultrasound (HIFU) is a modern and still evolving technique used to treat a variety of solid malignant cells in a well-defined volume. Using HIFU treatment allows a noninvasive and non-ionising approach, in comparison to more ...

A parallel ensemble of Genetic Algorithms for the Traveling Salesman Problem (TSP) is proposed. Different TSP solvers perform efficiently on different instance types. However, finding the best solver for all instances is challenging. A hybrid of the ...

As the availability of high-performance computing systems rises to unprecedented levels in the exascale era, vastly scalable parallelism is now an accessible, viable option for a wide range of scientific fields and algorithmic implementations. This ...

The need to improve the scalability of Genetic Algorithms (GAs) has motivated the research on Parallel Genetic Algorithms (PGAs), and different technologies and approaches have been used. Hadoop MapReduce represents one of the most mature technologies to ...

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. ...

In this paper, we present a distributed island model implementation of biogeography-based optimization for classification rule mining (island BBO-RM). Island BBO-RM is an evolutionary algorithm for rule mining that uses Pittsburgh style classification ...

Recently, an idea of evolutionary multi-tasking has been proposed and applied to various types of optimization problems. The basic idea of evolutionary multi-tasking is to simultaneously solve multiple optimization problems (i.e., tasks) in a ...

We propose a heterogeneous island model where each of the islands can run a different optimization algorithm. The distributed computation is managed by a central planner, that re-plans the methods during the run of the algorithm - less successful ...

Bent Boolean functions are cryptographic primitives essential for the safety of cryptographic algorithms, providing a degree of non-linearity to otherwise linear systems. The maximum possible non-linearity of a Boolean function is limited by the number ...

The Island-Cellular Model (ICM) is an important population distribution approach for Evolutionary Algorithms (EAs). This hybrid approach combines the Island Model (IM) and Cellular Model (CM) in a two-layer hierarchical model. Although the ICM has been ...

Particle Swarm Optimization (PSO) algorithm is a metaheuristic. This method has been used for solving optimization problems. As many other metaheuristics, several modifications in this method have been carried out in order to improve the performance of ...

In this paper, a hybrid approach for the parallelization of a genetic algorithm for a distributed/parallel computing environment is described. The genetic algorithm is the main part of the method for the division of road traffic networks for distributed ...

Different works have shown how crossover can help with building block assembly. Typically, crossover might get lucky to select good building blocks from each parent, but these lucky choices are usually rare. In this work we consider a crossover operator ...