Paper Session I-B - Reverse Engineering of Biological Gravity-Sensing Organs: Neurocomputational and Biomedical Implications

As humans began to project themselves into the environment of interplanetary space during the early 1960s, it was clear that the opening of this new frontier would require a comprehensive understanding of the effects of near-weightlessness (microgravity) on biological organisms. After all, life on planet Earth has evolved under the stable and pervasive influence of gravity. In terrestrial ecosystems, a force of one gravitational unit represents a continuous epigenetic agent that affects living systems at levels ranging from the morphogenetic to the behavioral2. However, an unexpected, beneficial outcome of research in gravitational biology and medicine is that it not only improves the conditions and prospects for space travelers, but it also results in enhanced knowledge that could contribute to the solution of physiological and biomedical problems for humans here on Earth3. Several Space Shuttle missions over the past decade have included experiments aimed at improving our understanding of the effect of microgravity on living organisms. For instance, the recent orbiter Columbia mission Neurolab (STS-90), proposed at the beginning of this ÒDecade of the BrainÓ, focused on basic neuroscience questions which will not only expand our understanding of how the nervous system develops and functions in space, but also increase our knowledge about how it develops and functions on Earth, thus contributing to the study and treatment of neurological diseases and disorders

From evolutionary computation to the evolution of things

Evolution has provided a source of inspiration for algorithm designers since the birth of computers. The resulting field, evolutionary computation, has been successful in solving engineering tasks ranging in outlook from the molecular to the astronomical. Today, the field is entering a new phase as evolutionary algorithms that take place in hardware are developed, opening up new avenues towards autonomous machines that can adapt to their environment. We discuss how evolutionary computation compares with natural evolution and what its benefits are relative to other computing approaches, and we introduce the emerging area of artificial evolution in physical systems

Kartezio: Evolutionary Design of Explainable Pipelines for Biomedical Image Analysis

An unresolved issue in contemporary biomedicine is the overwhelming number and diversity of complex images that require annotation, analysis and interpretation. Recent advances in Deep Learning have revolutionized the field of computer vision, creating algorithms that compete with human experts in image segmentation tasks. Crucially however, these frameworks require large human-annotated datasets for training and the resulting models are difficult to interpret. In this study, we introduce Kartezio, a modular Cartesian Genetic Programming based computational strategy that generates transparent and easily interpretable image processing pipelines by iteratively assembling and parameterizing computer vision functions. The pipelines thus generated exhibit comparable precision to state-of-the-art Deep Learning approaches on instance segmentation tasks, while requiring drastically smaller training datasets, a feature which confers tremendous flexibility, speed, and functionality to this approach. We also deployed Kartezio to solve semantic and instance segmentation problems in four real-world Use Cases, and showcase its utility in imaging contexts ranging from high-resolution microscopy to clinical pathology. By successfully implementing Kartezio on a portfolio of images ranging from subcellular structures to tumoral tissue, we demonstrated the flexibility, robustness and practical utility of this fully explicable evolutionary designer for semantic and instance segmentation.Comment: 36 pages, 6 main Figures. The Extended Data Movie is available at the following link: https://www.youtube.com/watch?v=r74gdzb6hdA. The source code is available on Github: https://github.com/KevinCortacero/Kartezi

A Field Guide to Genetic Programming

xiv, 233 p. : il. ; 23 cm.Libro ElectrónicoA Field Guide to Genetic Programming (ISBN 978-1-4092-0073-4) is an introduction to genetic programming (GP). GP is a systematic, domain-independent method for getting computers to solve problems automatically starting from a high-level statement of what needs to be done. Using ideas from natural evolution, GP starts from an ooze of random computer programs, and progressively refines them through processes of mutation and sexual recombination, until solutions emerge. All this without the user having to know or specify the form or structure of solutions in advance. GP has generated a plethora of human-competitive results and applications, including novel scientific discoveries and patentable inventions. The authorsIntroduction -- Representation, initialisation and operators in Tree-based GP -- Getting ready to run genetic programming -- Example genetic programming run -- Alternative initialisations and operators in Tree-based GP -- Modular, grammatical and developmental Tree-based GP -- Linear and graph genetic programming -- Probalistic genetic programming -- Multi-objective genetic programming -- Fast and distributed genetic programming -- GP theory and its applications -- Applications -- Troubleshooting GP -- Conclusions.Contents xi 1 Introduction 1.1 Genetic Programming in a Nutshell 1.2 Getting Started 1.3 Prerequisites 1.4 Overview of this Field Guide I Basics 2 Representation, Initialisation and GP 2.1 Representation 2.2 Initialising the Population 2.3 Selection 2.4 Recombination and Mutation Operators in Tree-based 3 Getting Ready to Run Genetic Programming 19 3.1 Step 1: Terminal Set 19 3.2 Step 2: Function Set 20 3.2.1 Closure 21 3.2.2 Sufficiency 23 3.2.3 Evolving Structures other than Programs 23 3.3 Step 3: Fitness Function 24 3.4 Step 4: GP Parameters 26 3.5 Step 5: Termination and solution designation 27 4 Example Genetic Programming Run 4.1 Preparatory Steps 29 4.2 Step-by-Step Sample Run 31 4.2.1 Initialisation 31 4.2.2 Fitness Evaluation Selection, Crossover and Mutation Termination and Solution Designation Advanced Genetic Programming 5 Alternative Initialisations and Operators in 5.1 Constructing the Initial Population 5.1.1 Uniform Initialisation 5.1.2 Initialisation may Affect Bloat 5.1.3 Seeding 5.2 GP Mutation 5.2.1 Is Mutation Necessary? 5.2.2 Mutation Cookbook 5.3 GP Crossover 5.4 Other Techniques 32 5.5 Tree-based GP 39 6 Modular, Grammatical and Developmental Tree-based GP 47 6.1 Evolving Modular and Hierarchical Structures 47 6.1.1 Automatically Defined Functions 48 6.1.2 Program Architecture and Architecture-Altering 50 6.2 Constraining Structures 51 6.2.1 Enforcing Particular Structures 52 6.2.2 Strongly Typed GP 52 6.2.3 Grammar-based Constraints 53 6.2.4 Constraints and Bias 55 6.3 Developmental Genetic Programming 57 6.4 Strongly Typed Autoconstructive GP with PushGP 59 7 Linear and Graph Genetic Programming 61 7.1 Linear Genetic Programming 61 7.1.1 Motivations 61 7.1.2 Linear GP Representations 62 7.1.3 Linear GP Operators 64 7.2 Graph-Based Genetic Programming 65 7.2.1 Parallel Distributed GP (PDGP) 65 7.2.2 PADO 67 7.2.3 Cartesian GP 67 7.2.4 Evolving Parallel Programs using Indirect Encodings 68 8 Probabilistic Genetic Programming 8.1 Estimation of Distribution Algorithms 69 8.2 Pure EDA GP 71 8.3 Mixing Grammars and Probabilities 74 9 Multi-objective Genetic Programming 75 9.1 Combining Multiple Objectives into a Scalar Fitness Function 75 9.2 Keeping the Objectives Separate 76 9.2.1 Multi-objective Bloat and Complexity Control 77 9.2.2 Other Objectives 78 9.2.3 Non-Pareto Criteria 80 9.3 Multiple Objectives via Dynamic and Staged Fitness Functions 80 9.4 Multi-objective Optimisation via Operator Bias 81 10 Fast and Distributed Genetic Programming 83 10.1 Reducing Fitness Evaluations/Increasing their Effectiveness 83 10.2 Reducing Cost of Fitness with Caches 86 10.3 Parallel and Distributed GP are Not Equivalent 88 10.4 Running GP on Parallel Hardware 89 10.4.1 Master–slave GP 89 10.4.2 GP Running on GPUs 90 10.4.3 GP on FPGAs 92 10.4.4 Sub-machine-code GP 93 10.5 Geographically Distributed GP 93 11 GP Theory and its Applications 97 11.1 Mathematical Models 98 11.2 Search Spaces 99 11.3 Bloat 101 11.3.1 Bloat in Theory 101 11.3.2 Bloat Control in Practice 104 III Practical Genetic Programming 12 Applications 12.1 Where GP has Done Well 12.2 Curve Fitting, Data Modelling and Symbolic Regression 12.3 Human Competitive Results – the Humies 12.4 Image and Signal Processing 12.5 Financial Trading, Time Series, and Economic Modelling 12.6 Industrial Process Control 12.7 Medicine, Biology and Bioinformatics 12.8 GP to Create Searchers and Solvers – Hyper-heuristics xiii 12.9 Entertainment and Computer Games 127 12.10The Arts 127 12.11Compression 128 13 Troubleshooting GP 13.1 Is there a Bug in the Code? 13.2 Can you Trust your Results? 13.3 There are No Silver Bullets 13.4 Small Changes can have Big Effects 13.5 Big Changes can have No Effect 13.6 Study your Populations 13.7 Encourage Diversity 13.8 Embrace Approximation 13.9 Control Bloat 13.10 Checkpoint Results 13.11 Report Well 13.12 Convince your Customers 14 Conclusions Tricks of the Trade A Resources A.1 Key Books A.2 Key Journals A.3 Key International Meetings A.4 GP Implementations A.5 On-Line Resources 145 B TinyGP 151 B.1 Overview of TinyGP 151 B.2 Input Data Files for TinyGP 153 B.3 Source Code 154 B.4 Compiling and Running TinyGP 162 Bibliography 167 Inde

Artificial in its own right

Artificial Cells, , Artificial Ecologies, Artificial Intelligence, Bio-Inspired Hardware Systems, Computational Autopoiesis, Computational Biology, Computational Embryology, Computational Evolution, Morphogenesis, Cyborgization, Digital Evolution, Evolvable Hardware, Cyborgs, Mathematical Biology, Nanotechnology, Posthuman, Transhuman

The application of evolutionary computation towards the characterization and classification of urothelium cell cultures

This thesis presents a novel method for classifying and characterizing urothelial cell cultures. A system of cell tracking employing computer vision techniques was applied to a one day long time-lapse videos of replicate normal human uroepithelial cell cultures exposed to different concentrations of adenosine triphosphate (ATP) and a selective purinergic P2X antagonist (PPADS) as inhibitor. Subsequent analysis following feature extraction on both cell culture and single-cell demonstrated the ability of the approach to successfully classify the modulated classes of cells using evolutionary algorithms. Specifically, a Cartesian Genetic Program (CGP) network was evolved that identified average migration speed, in-contact angular velocity, cohesivity and average cell clump size as the principal features contributing to the cell class separation. This approach provides a non-biased insight into modulated cell class behaviours

Natural Computational Architectures for Cognitive Info-Communication

Recent comprehensive overview of 40 years of research in cognitive architectures, (Kotseruba and Tsotsos 2020), evaluates modelling of the core cognitive abilities in humans, but only marginally addresses biologically plausible approaches based on natural computation. This mini review presentsa set of perspectives and approaches which have shaped the development of biologically inspired computational models in the recent past that can lead to the development of biologically more realistic cognitive architectures. For describing continuum of natural cognitive architectures, from basal cellular to human-level cognition, we use evolutionary info-computational framework, where natural/ physical/ morphological computation leads to evolution of increasingly complex cognitive systems. Forty years ago, when the first cognitive architectures have been proposed, understanding of cognition, embodiment and evolution was different. So was the state of the art of information physics, bioinformatics, information chemistry, computational neuroscience, complexity theory, selforganization, theory of evolution, information and computation. Novel developments support a constructive interdisciplinary framework for cognitive architectures in the context of computing nature, where interactions between constituents at different levels of organization lead to complexification of agency and increased cognitive capacities. We identify several important research questions for further investigation that can increase understanding of cognition in nature and inspire new developments of cognitive technologies. Recently, basal cell cognition attracted a lot of interest for its possible applications in medicine, new computing technologies, as well as micro- and nanorobotics. Bio-cognition of cells connected into tissues/organs, and organisms with the group (social) levels of information processing provides insights into cognition mechanisms that can support the development of new AI platforms and cognitive robotics