Advances in Evolutionary Algorithms

With the recent trends towards massive data sets and significant computational power, combined with evolutionary algorithmic advances evolutionary computation is becoming much more relevant to practice. Aim of the book is to present recent improvements, innovative ideas and concepts in a part of a huge EA field

The Emergence of Diversity and Stability: from Biological Systems to Machine Learning

The observation of emergent properties of biological systems has been the inspiration of successful technologies opening new fields of computer science like artificial neural nets, swarm intelligence algorithms, evolutive algorithms, etc. In this work we focus on the emergence of negative feedback cycles: self-regulatory mechanisms able to react to alterations of some environmental parameters (temperature, gas concentrations, solar light, etc.) in order to compensate, preserving the environment in a state suitable for life. We make the hypothesis that speciation events play a central role for feedback formation and, and in order to select the negative cycles, the arising species need to be strongly connected to the environment, therefore the speciation needs to be sympatric (a speciation mode where new species arise without geographical isolation). As an intermediate result, we propose a simulative model of sympatric speciation and apply it to the field of evolutive algorithms. We propose some variations of the standard island model, a model used in evolutive algorithms to evolve multiple populations, to obtain dynamics similar to the sympatric speciation model, enhancing the diversity and the stability of the evolutive system. Then we propose a technique to define a metric and calculate approximated distances on very complex genetic spaces (a recurring problem for several evolutionary algorithms approaches). Finally, we describe the more complex model of negative feedback cycles emergence and discuss the problems that, in the current model formulation, make it not applicable to real world problems but only to ad hoc defined resource spaces; conclusively we propose possible solutions and some applications

Evolving Models From Observed Human Performance

To create a realistic environment, many simulations require simulated agents with human behavior patterns. Manually creating such agents with realistic behavior is often a tedious and time-consuming task. This dissertation describes a new approach that automatically builds human behavior models for simulated agents by observing human performance. The research described in this dissertation synergistically combines Context-Based Reasoning, a paradigm especially developed to model tactical human performance within simulated agents, with Genetic Programming, a machine learning algorithm to construct the behavior knowledge in accordance to the paradigm. This synergistic combination of well-documented AI methodologies has resulted in a new algorithm that effectively and automatically builds simulated agents with human behavior. This algorithm was tested extensively with five different simulated agents created by observing the performance of five humans driving an automobile simulator. The agents show not only the ability/capability to automatically learn and generalize the behavior of the human observed, but they also capture some of the personal behavior patterns observed among the five humans. Furthermore, the agents exhibited a performance that was at least as good as agents developed manually by a knowledgeable engineer

Evolution from the ground up with Amee – From basic concepts to explorative modeling

Evolutionary theory has been the foundation of biological research for about a century now, yet over the past few decades, new discoveries and theoretical advances have rapidly transformed our understanding of the evolutionary process. Foremost among them are evolutionary developmental biology, epigenetic inheritance, and various forms of evolu- tionarily relevant phenotypic plasticity, as well as cultural evolution, which ultimately led to the conceptualization of an extended evolutionary synthesis. Starting from abstract principles rooted in complexity theory, this thesis aims to provide a unified conceptual understanding of any kind of evolution, biological or otherwise. This is used in the second part to develop Amee, an agent-based model that unifies development, niche construction, and phenotypic plasticity with natural selection based on a simulated ecology. Amee is implemented in Utopia, which allows performant, integrated implementation and simulation of arbitrary agent-based models. A phenomenological overview over Amee’s capabilities is provided, ranging from the evolution of ecospecies down to the evolution of metabolic networks and up to beyond-species-level biological organization, all of which emerges autonomously from the basic dynamics. The interaction of development, plasticity, and niche construction has been investigated, and it has been shown that while expected natural phenomena can, in principle, arise, the accessible simulation time and system size are too small to produce natural evo-devo phenomena and –structures. Amee thus can be used to simulate the evolution of a wide variety of processes

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

An analysis of landscape diversity on the floodplain of a scottish wandering gravel - bed river

This thesis examines landscape diversity within alluvial valley floors using the case study of a Scottish wandering gravel-bed river. The thesis aims are two-fold; firstly to investigate the spatial and temporal patterns of valley floor landscape diversity within semi-natural environments, and secondly to develop a methodology for quantifying alluvial valley floor landscape diversity in space and time. The diversity analysis involves quantifying the spatial patterns of geo-, pedo- and biodiversity (flora) within floodplain zones which have been exposed to approximately 100 years of recovery since flood embankment abandonment along the most active reaches of the river. In addition historical records including aerial photographs, maps and narrative accounts were used to assess the temporal patterns of the diversity of landscape patches and how they have changed through time using a series of landscape indices. The analysis thus accounts for the role of river channel change in producing a complex mosaic of land cover types within alluvial valley floors. The spatial analysis revealed that landscape diversity tends to be greater in the perpendicular orientation to the main channel, i.e. along an aquatic-to-terrestrial environmental gradient. The temporal analysis results revealed that the landscape over the last 50 years has changed from being dominated by few relatively large isodiametric patches to a landscape dominated by small irregular shaped patches. Thus although landscape patch richness has increased along with an increase in land cover types through time, the landscape patches have also become more fragmented. The major outcomes of the research are the deriving of quantitative results of the spatial and temporal patterns of floodplain landscape diversity, an evaluation of the role of channel dynamics in creating the diverse mosaic of land cover types, the identification of the environmental controls and supporting floodplain habitats of a number of rare species and a proposed methodology for assessing landscape diversity to be validated on other river systems

White Paper 2: Origins, (Co)Evolution, Diversity & Synthesis Of Life

Publicado en Madrid, 185 p. ; 17 cm.How life appeared on Earth and how then it diversified into the different and currently existing forms of life are the unanswered questions that will be discussed this volume. These questions delve into the deep past of our planet, where biology intermingles with geology and chemistry, to explore the origin of life and understand its evolution, since “nothing makes sense in biology except in the light of evolution” (Dobzhansky, 1964). The eight challenges that compose this volume summarize our current knowledge and future research directions touching different aspects of the study of evolution, which can be considered a fundamental discipline of Life Science. The volume discusses recent theories on how the first molecules arouse, became organized and acquired their structure, enabling the first forms of life. It also attempts to explain how this life has changed over time, giving rise, from very similar molecular bases, to an immense biological diversity, and to understand what is the hylogenetic relationship among all the different life forms. The volume further analyzes human evolution, its relationship with the environment and its implications on human health and society. Closing the circle, the volume discusses the possibility of designing new biological machines, thus creating a cell prototype from its components and whether this knowledge can be applied to improve our ecosystem. With an effective coordination among its three main areas of knowledge, the CSIC can become an international benchmark for research in this field

Examining The Continuity Of The Long-Lived (Triassic-Recent) Freshwater Mussel Genus Diplodon (family Hyriidae)

This paper addresses the question of how strong the record of contiguity is for the 250 million-year-old Diplodon lineage by examining the geographic and temporal distribution of fossil specimens identified as Diplodon. Diplodon (Mollusca, Bivalvia, Unionoida, Hyriidae) has a fossil record extending back to the Middle Triassic (Anisian Stage). The known distribution of fossil specimens identified as this genus occurs on four continents (North America, South America, Australasia, and Antarctica). The place of origin and pathways of range expansion through time are far from well explained. Both fossil and extant freshwater mussel taxa are subject to evolutionary and phenotypic morphological convergence, which has resulted in problems of identification and classification. Because of the tendency of freshwater mussels to converge toward similar morphologies, project methods focused on metadata rather than the specimens themselves. The biostratigraphic ranges of specimens identified as Diplodon were determined in order to target temporal and geographic gaps in the fossil record. Without a comprehensive taxonomic review, only Diplodon taxa in current use from documented specimen locations are used in this report. This project has produced paleolandscape maps of the regions that have recorded Diplodon specimens. These first-generation maps were used to qualitatively analyze possible avenues of taxon dispersion through time. Production of paleolandscape maps was based on a new methodology that can be expanded for with other taxa on a global scale. The evolutionary lineage represented by use of the name Diplodon is not well supported. Geographic and temporal data suggest that hard-part morphology has been an incorrect basis for classification. Five distinct temporal gaps of at least a single geologic stage in duration were identified in the Diplodon fossil record between 245 Ma (beginning of the Anisian Stage) and 5 Ma (end of the Messinian Stage). These gaps occurred during the 1) Middle Triassic (Ladinian); 2) Late Triassic–Middle Jurassic (Norian–Bathonian); 3) Early Cretaceous (Berriasian–Barremian), 4) late Early Cretaceous–Late Cretaceous (Albian–Cenomanian); and 5) early-middle Eocene (Ypresian–Bartonian) intervals. Gaps in the record are supported by 1) the pattern of additional specimens that lack as much temporal resolution; 2) geographic distances and paleolandscape features between known fossil localities; and 3) the species names applied to these specimens. Continued study of genus-group morphological characters of fossil specimens and molecular analyses of living specimens is necessary to create a Diplodon diagnosis that takes into account morphologic variation (including convergence with other taxa) and the geologic age and geographic relationships among specimens

Remote Sensing of Plant Biodiversity

At last, here it is. For some time now, the world has needed a text providing both a new theoretical foundation and practical guidance on how to approach the challenge of biodiversity decline in the Anthropocene. This is a global challenge demanding global approaches to understand its scope and implications. Until recently, we have simply lacked the tools to do so. We are now entering an era in which we can realistically begin to understand and monitor the multidimensional phenomenon of biodiversity at a planetary scale. This era builds upon three centuries of scientific research on biodiversity at site to landscape levels, augmented over the past two decades by airborne research platforms carrying spectrometers, lidars, and radars for larger-scale observations. Emerging international networks of fine-grain in-situ biodiversity observations complemented by space-based sensors offering coarser-grain imagery—but global coverage—of ecosystem composition, function, and structure together provide the information necessary to monitor and track change in biodiversity globally. This book is a road map on how to observe and interpret terrestrial biodiversity across scales through plants—primary producers and the foundation of the trophic pyramid. It honors the fact that biodiversity exists across different dimensions, including both phylogenetic and functional. Then, it relates these aspects of biodiversity to another dimension, the spectral diversity captured by remote sensing instruments operating at scales from leaf to canopy to biome. The biodiversity community has needed a Rosetta Stone to translate between the language of satellite remote sensing and its resulting spectral diversity and the languages of those exploring the phylogenetic diversity and functional trait diversity of life on Earth. By assembling the vital translation, this volume has globalized our ability to track biodiversity state and change. Thus, a global problem meets a key component of the global solution. The editors have cleverly built the book in three parts. Part 1 addresses the theory behind the remote sensing of terrestrial plant biodiversity: why spectral diversity relates to plant functional traits and phylogenetic diversity. Starting with first principles, it connects plant biochemistry, physiology, and macroecology to remotely sensed spectra and explores the processes behind the patterns we observe. Examples from the field demonstrate the rising synthesis of multiple disciplines to create a new cross-spatial and spectral science of biodiversity. Part 2 discusses how to implement this evolving science. It focuses on the plethora of novel in-situ, airborne, and spaceborne Earth observation tools currently and soon to be available while also incorporating the ways of actually making biodiversity measurements with these tools. It includes instructions for organizing and conducting a field campaign. Throughout, there is a focus on the burgeoning field of imaging spectroscopy, which is revolutionizing our ability to characterize life remotely. Part 3 takes on an overarching issue for any effort to globalize biodiversity observations, the issue of scale. It addresses scale from two perspectives. The first is that of combining observations across varying spatial, temporal, and spectral resolutions for better understanding—that is, what scales and how. This is an area of ongoing research driven by a confluence of innovations in observation systems and rising computational capacity. The second is the organizational side of the scaling challenge. It explores existing frameworks for integrating multi-scale observations within global networks. The focus here is on what practical steps can be taken to organize multi-scale data and what is already happening in this regard. These frameworks include essential biodiversity variables and the Group on Earth Observations Biodiversity Observation Network (GEO BON). This book constitutes an end-to-end guide uniting the latest in research and techniques to cover the theory and practice of the remote sensing of plant biodiversity. In putting it together, the editors and their coauthors, all preeminent in their fields, have done a great service for those seeking to understand and conserve life on Earth—just when we need it most. For if the world is ever to construct a coordinated response to the planetwide crisis of biodiversity loss, it must first assemble adequate—and global—measures of what we are losing

Reticulate Evolution: Symbiogenesis, Lateral Gene Transfer, Hybridization and Infectious heredity

info:eu-repo/semantics/publishedVersio