On the performance of the hybridisation between migrating birds optimisation variants and differential evolution for large scale continuous problems

Migrating Birds Optimisation (mbo) is a nature-inspired approach which has been shown to be very effective when solving a variety of combinatorial optimisation problems. More recently, an adaptation of the algorithm has been proposed that enables it to deal with continuous search spaces. We extend this work in two ways.Firstly, a novel leader replacement strategy is proposed to counter the slow convergence of the existing mbo algorithms due to low selection pressure. Secondly, mbo is hybridised with adaptive neighbourhood operators borrowed from Differential Evolution (de) that promote exploration and exploitation. The new variants are tested on two sets of continuous large scale optimisation problems. Results show that mbo variants using adaptive, exploration-based operators outperform de on the cec benchmark suite with 1000variables. Further experiments on a second suite of 19 problems show that mbo variants outperform de on 90% of these test-cases

A comprehensive survey on cultural algorithms

Peer reviewedPostprin

Review and Classification of Bio-inspired Algorithms and Their Applications

Scientists have long looked to nature and biology in order to understand and model solutions for complex real-world problems. The study of bionics bridges the functions, biological structures and functions and organizational principles found in nature with our modern technologies, numerous mathematical and metaheuristic algorithms have been developed along with the knowledge transferring process from the lifeforms to the human technologies. Output of bionics study includes not only physical products, but also various optimization computation methods that can be applied in different areas. Related algorithms can broadly be divided into four groups: evolutionary based bio-inspired algorithms, swarm intelligence-based bio-inspired algorithms, ecology-based bio-inspired algorithms and multi-objective bio-inspired algorithms. Bio-inspired algorithms such as neural network, ant colony algorithms, particle swarm optimization and others have been applied in almost every area of science, engineering and business management with a dramatic increase of number of relevant publications. This paper provides a systematic, pragmatic and comprehensive review of the latest developments in evolutionary based bio-inspired algorithms, swarm intelligence based bio-inspired algorithms, ecology based bio-inspired algorithms and multi-objective bio-inspired algorithms

A review of population-based metaheuristics for large-scale black-box global optimization: Part A

Scalability of optimization algorithms is a major challenge in coping with the ever growing size of optimization problems in a wide range of application areas from high-dimensional machine learning to complex large-scale engineering problems. The field of large-scale global optimization is concerned with improving the scalability of global optimization algorithms, particularly population-based metaheuristics. Such metaheuristics have been successfully applied to continuous, discrete, or combinatorial problems ranging from several thousand dimensions to billions of decision variables. In this two-part survey, we review recent studies in the field of large-scale black-box global optimization to help researchers and practitioners gain a bird’s-eye view of the field, learn about its major trends, and the state-of-the-art algorithms. Part of the series covers two major algorithmic approaches to large-scale global optimization: problem decomposition and memetic algorithms. Part of the series covers a range of other algorithmic approaches to large-scale global optimization, describes a wide range of problem areas, and finally touches upon the pitfalls and challenges of current research and identifies several potential areas for future research

Nature-inspired algorithms for solving some hard numerical problems

Optimisation is a branch of mathematics that was developed to find the optimal solutions, among all the possible ones, for a given problem. Applications of optimisation techniques are currently employed in engineering, computing, and industrial problems. Therefore, optimisation is a very active research area, leading to the publication of a large number of methods to solve specific problems to its optimality. This dissertation focuses on the adaptation of two nature inspired algorithms that, based on optimisation techniques, are able to compute approximations for zeros of polynomials and roots of non-linear equations and systems of non-linear equations. Although many iterative methods for finding all the roots of a given function already exist, they usually require: (a) repeated deflations, that can lead to very inaccurate results due to the problem of accumulating rounding errors, (b) good initial approximations to the roots for the algorithm converge, or (c) the computation of first or second order derivatives, which besides being computationally intensive, it is not always possible. The drawbacks previously mentioned served as motivation for the use of Particle Swarm Optimisation (PSO) and Artificial Neural Networks (ANNs) for root-finding, since they are known, respectively, for their ability to explore high-dimensional spaces (not requiring good initial approximations) and for their capability to model complex problems. Besides that, both methods do not need repeated deflations, nor derivative information. The algorithms were described throughout this document and tested using a test suite of hard numerical problems in science and engineering. Results, in turn, were compared with several results available on the literature and with the well-known Durand–Kerner method, depicting that both algorithms are effective to solve the numerical problems considered.A Optimização é um ramo da matemática desenvolvido para encontrar as soluções óptimas, de entre todas as possíveis, para um determinado problema. Actualmente, são várias as técnicas de optimização aplicadas a problemas de engenharia, de informática e da indústria. Dada a grande panóplia de aplicações, existem inúmeros trabalhos publicados que propõem métodos para resolver, de forma óptima, problemas específicos. Esta dissertação foca-se na adaptação de dois algoritmos inspirados na natureza que, tendo como base técnicas de optimização, são capazes de calcular aproximações para zeros de polinómios e raízes de equações não lineares e sistemas de equações não lineares. Embora já existam muitos métodos iterativos para encontrar todas as raízes ou zeros de uma função, eles usualmente exigem: (a) deflações repetidas, que podem levar a resultados muito inexactos, devido ao problema da acumulação de erros de arredondamento a cada iteração; (b) boas aproximações iniciais para as raízes para o algoritmo convergir, ou (c) o cálculo de derivadas de primeira ou de segunda ordem que, além de ser computacionalmente intensivo, para muitas funções é impossível de se calcular. Estas desvantagens motivaram o uso da Optimização por Enxame de Partículas (PSO) e de Redes Neurais Artificiais (RNAs) para o cálculo de raízes. Estas técnicas são conhecidas, respectivamente, pela sua capacidade de explorar espaços de dimensão superior (não exigindo boas aproximações iniciais) e pela sua capacidade de modelar problemas complexos. Além disto, tais técnicas não necessitam de deflações repetidas, nem do cálculo de derivadas. Ao longo deste documento, os algoritmos são descritos e testados, usando um conjunto de problemas numéricos com aplicações nas ciências e na engenharia. Os resultados foram comparados com outros disponíveis na literatura e com o método de Durand–Kerner, e sugerem que ambos os algoritmos são capazes de resolver os problemas numéricos considerados

Advances in Artificial Intelligence: Models, Optimization, and Machine Learning

The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications

Tracking thicket through space and time : insights into the evolutionary history of the Albany Subtropical Thicket from comparative phylogeography and distribution modelling

Includes abstract.Includes bibliographical references (leaves 191-219).Albany Subtropical Thicket (AST) is a species-rich biome restricted to the coastal lowlands of the southern Cape region of South Africa. Its Quaternary history is poorly understood, but climatic changes associated with Pleistocene glacial cycles may have profoundly affected the distributions, gene flows, and demographies of species. The glacial refugia hypothesis predicts that AST retracted into fragmented refugia during glacial cycles. The evolutionarily discrete drainage basin (EDDB) hypothesis suggests that the prevailing topography played an important population-structuring role. I evaluate these two hypotheses by combining community and species distribution models with multigene comparative phylogeography of three AST species Pappea capensis, Nymania capensis, and Schotia afra. Distribution models support the glacial refugia hypothesis, with highly reduced and fragmented distributions postdicted for the Last Glacial Maximum. These models, projected onto two climate scenarios for 2050, give a positive outlook for the future of AST, with no dramatic shifts or reduction in appropriate climate..

The utility of molecular genetic analysis of museum specimens in studying deep-sea fish

The damaging effect of formalin on DNA and the inhibition of PCR are serious problems in molecular studies. The aims of the project were to investigate the possibility of using formalin-fixed, Steedman's preserved museum specimens in molecular investigations, especially organisms with unstudied genomes. A number of DNA extraction protocols and different pre-washing/drying regimes were tested. These gave different levels of success, but a guanidinium-based protocol developed in this study gave the best results. RAPD-PCR methodology was employed to test its applicability on preserved specimens, and it was used as a test of the efficiency of DNA extraction/amplifications and for developing species-specific PCR primers. Attempts to amplify mitochondrial DNA sequences with the six mitochondrial genes were mostly unsuccessful. Sporadic amplifications were obtained with primers of 16S and COIII genes. This study provided the first molecular data on deep-sea fish (Nezumia aequalis and N. micronychodon) exclusively using formalin-fixed, Steedman's preserved museum specimens. Two genomic sequences of these fishes were determined and submitted to the GenBank database under accession numbers AY826774 - AY826792. Three specific primer sets (RAPD-derived) for Nezumia aequalis and N. micronychodon were designed to amplify PCR product sizes 300 bp - 350 bp. This study has demonstrated that an appropriate strategy and molecular approach could lead to the successful use of museum and other formalin-fixed archival collections even on organisms with unstudied genomes. Supplementary evidence, related to the method of preservation, the usage of particular DNA extraction protocol and PCR marker system, was obtained from ten differently preserved mackerel (Scomber scombrus) specimens. This study confirmed that the DNA extracted from preserved specimens possesses unique characteristics that make molecular investigations very difficult. Because of this, it is proposed that DNA extracted from preserved specimens should be referred to as "archival DNA (arDNA)"

Evolutionary consequences of a catadromous life-strategy on the genetic structure of European eel (<i>Anguilla anguilla</i> L.)

Marine organisms usually exhibit a high genetic diversity, a subtle population structure and a low level of genetic differentiation, compared to freshwater organisms. The subtle genetic differences in time and space reflect the continuity of the marine environment. Marine organisms experience a wide range of intrinsic and extrinsic influences during their life cycle, which considerably impact their biological population size and genetic population structure. Furthermore, genetic variability is crucial for the survival of organisms as it enables evolution while maintaining fitness. Marine species however have a high genetic load, affecting the population even more during a population decline or bottleneck. The European eel Anguilla anguilla (Anguillidae; Teleostei), although inhabiting fresh- and saltwater, represents no exception. Its spawning habitat in the Sargasso Sea and extensive migrations across the North Atlantic Ocean qualify it fully as a marine species. This thesis describes the multiple evolutionary consequences of the catadromous life-strategy on the genetic structure of the European eel.Recent data based on microsatellite markers show a subtle genetic structure in the European eel following an Isolation-by-Distance (IBD) pattern. But since genetic introgression from the American eel into the European eel has been suggested in the North Atlantic Ocean, reliable tests were developed to define the species status of the European eel. In this first part the interspecific conservation of a set of microsatellites was tested on other Anguilla taxa and the power of species discrimination was assessed. We then applied this knowledge by screening Icelandic and European samples for introgression of American eel. Indications of unequal but restricted hybridisation were detected, likely maintained through selection against hybrids and the preservation of migrational cues.In the second part, the genetic variability and differentiation between various glass and silver eel populations was compared over a broad geographical range (Iceland to Morocco; Spain to Turkey) with temporal replications. In the first instance a pattern of Isolation-by- Distance was detected in adult populations using allozyme markers. Following a more extended geographical sampling, the temporal stability of this pattern could not be confirmed; the temporal differentiation between populations clearly exceeded the geographical component. By sampling recruiting glass eels over a three-year period, a stronger genetic differentiation was found between temporally separated cohorts. Inter-annual differentiation was much higher than the geographical differentiation. The population genetic structure of eel is likely determined by a double process: (I) a large scale pattern of Isolation-by-Time (IBT) among spawning cohorts, and (2) a smaller scale variance in adult reproductive success (genetic patchiness) among seasonally separated cohorts, most likely originating from oceanic and climatic influences.In the third part, the relation between multi-locus heterozygosity (MLH) and fitness components was studied. If an association exists between genetic variability and fitness traits, it is even more important to maintain the population size of European eel. The catastrophic decline of the European eel might be the consequence of an accelerated loss of genetic diversity, with extinction as possible outcome. This hypothesis was tested in a polluted natural environment and in an eel farm. Eel from three Belgian drainage basins were screened for fitness, heavy metal bioaccumulation and genetic variation. There was a strong negative correlation between MLH and bioaccumulation in highly polluted eels. In a second study, aquacultured eel were screened for fitness and genetic variation; MLH was correlated to growth rate. In both studies, this effect was mainly attributed to metabolical enzymes important in the energy cycle, which points to the importance o