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• Computational Models of Evolution and Development
• Analysis of Gene Regulatory Networks
• Evolutionary Synthesis of Gene Regulatory Dynamics
• Evolution of Morphological Development
• Evolution of Neural Development
• Computational Brain-Body Co-Evolution
• Evolutionary Morphogenetic Self-Organization of Swarm Robots
• Towards Evolutionary Developmental Systems

This book provides a basic yet unified overview of theory and methodologies for evolutionary developmental systems. Based on the authors extensive research into the synergies between various approaches to artificial intelligence including evolutionary computation, artificial neural networks, and systems biology, it also examines the inherent links between biological intelligence and artificial intelligence. The book begins with an introduction to computational algorithms used to understand and simulate biological evolution and development, including evolutionary algorithms, gene regulatory network models, multi-cellular models for neural and morphological development, and computational models of neural plasticity. Chap. 2 discusses important properties of biological gene regulatory systems, including network motifs, network connectivity, robustness and evolvability. Going a step further, Chap. 3 presents methods for synthesizing regulatory motifs from scratch and creating more complex regulatory dynamics by combining basic regulatory motifs using evolutionary algorithms. Multi-cellular growth models, which can be used to simulate either neural or morphological development, are presented in Chapters 4 and 5. Chap. 6 examines the synergies and coupling between neural and morphological evolution and development. In turn, Chap. 7 provides preliminary yet promising examples of how evolutionary developmental systems can help in self-organized pattern generation, referred to as morphogenetic self-organization, highlighting the great potentials of evolutionary developmental systems. Finally, Chap. 8 rounds out the book, stressing the importance and promise of the evolutionary developmental approach to artificial intelligence. Featuring a wealth of diagrams, graphs and charts to aid in comprehension, this book offers a valuable asset for graduate students, researchers and practitioners who are interested in pursuing a different approach to artificial intelligence

• Beyond Watson-Crick
• DNA nanotechnology out of equilibrium
• The Evolution of DNA-Based Molecular Computing
• DNA Nanotechnology Research in Japan
• Reminiscences from the Trenches
• Beyond DNA
• Controlling single molecule conjugated oligomers and polymers with DNA
• Organizing charge flow with DNA
• DNA Assembly of Dye Aggregates
• Building with DNA
• From Molecules to Mathematics
• Origami Life
• Ok: a kinetic model for locally reconfigurable molecular systems
• Implementing a Theoretician’s Toolkit for Self-Assembly with DNA Components
• Reasoning As If
• Scaling up DNA computing with array-based synthesis and high-throughput sequencing
• Sequenceable Event Recorders
• Computational Design of Nucleic Acid Circuits
• Parallel computations with DNA-encoded chemical reaction networks
• Social DNA Nanorobots
• Models of Gellular Automata
• Patterning DNA origami on membranes through protein self-organization

This open access book provides a unique and state-of-the-art view on DNA nanotechnology with an eye toward future developments. Intended as a tribute to Nadrian C. Seeman, who founded the field of DNA nanotechnology, the content is an exciting mixture of technical and non-technical material, reviews, tutorials, perspectives, new findings, and open questions. The book aims to inspire current researchers to sit back and think about the big picture, while also enticing new researchers to enter the field. Most of all, the book captures voices from a unique moment in time: 40 years after the publication of the first paper that envisioned DNA nanotechnology. From this vantage point, what are the untold stories, the unspoken concerns, the underlying fundamental issues, the overlooked opportunities, and the unifying grand challenges? What will help us see more clearly, see more creatively, or see farther? What is transpiring right now that could pave the way for the future? To address these questions, leading researchers have contributed 22 chapters, grouped into five sections: perspectives, chemistry and physics, structures, biochemical circuits, and spatial systems. This book will be an important reference point in the field of DNA nanotechnology, both for established researchers looking to take stock of the field and its future, and for newcomers such as graduate students and researchers in other fields who are beginning to appreciate the power and applicability of its methods

• No Free Lunch Theorems: Limitations and Perspectives of Metaheuristics.- Convergence Rates of Evolutionary Algorithms and Parallel Evolutionary Algorithms.- Rugged and Elementary Landscapes.- Single-Funnel and Multi-funnel Landscapes and Subthreshold Seeking Behavior.- Black-Box Complexity for Bounding the Performance of Randomized Search Heuristics.- Designing an Optimal Search Algorithm with Respect to Prior Information.- The Bayesian Search Game.- Principled Design of Continuous Stochastic Search: From Theory to Practice.- Parsimony Pressure Made Easy: Solving the Problem of Bloat in GP.- Experimental Analysis of Optimization Algorithms: Tuning and Beyond.- Formal Search Algorithms + Problem Characterizations = Executable Search Strategies.
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Metaheuristics, and evolutionary algorithms in particular, are known to provide efficient, adaptable solutions for many real-world problems, but the often informal way in which they are defined and applied has led to misconceptions, and even successful applications are sometimes the outcome of trial and error. Ideally, theoretical studies should explain when and why metaheuristics work, but the challenge is huge: mathematical analysis requires significant effort even for simple scenarios and real-life problems are usually quite complex. In this book the editors establish a bridge between theory and practice, presenting principled methods that incorporate problem knowledge in evolutionary algorithms and other metaheuristics. The book consists of 11 chapters dealing with the following topics: theoretical results that show what is not possible, an assessment of unsuccessful lines of empirical research; methods for rigorously defining the appropriate scope of problems while acknowledging the compromise between the class of problems to which a search algorithm is applied and its overall expected performance; the top-down principled design of search algorithms, in particular showing that it is possible to design algorithms that are provably good for some rigorously defined classes; and, finally, principled practice, that is reasoned and systematic approaches to setting up experiments, metaheuristic adaptation to specific problems, and setting parameters. With contributions by some of the leading researchers in this domain, this book will be of significant value to scientists, practitioners, and graduate students in the areas of evolutionary computing, metaheuristics, and computational intelligence.
(source: Nielsen Book Data)

• Monotony and Surprise: Pattern Discovery Under Saturation Constraints.- Information Content of Sets of Biological Sequences Revisited.- Duplication in DNA Sequences.- Sequence and Structural Analysis for Functional Non-coding RNAs.- Strategies for RNA-Guided DNA Recombination.- Reality-and-Desire in Ciliates.- Template-Guided Recombination: From Theory to Laboratory.- DNA Cages with Icosahedral Symmetry in Bionanotechnology.- Applying Symmetric Enumeration Method to One-Dimensional Assembly of Rotatable Tiles.- The Perils of Polynucleotides Revisited.- On Nonuniversal Symport/Antiport P Systems.- Spiking Neural P Systems: Recent Results, Research Topics.- Membrane Computing Schema: A New Approach to Computation Using String Insertions.- Understanding Network Behaviour by Structured Representations of Transition Invariants: A Petri Net Perspective on Systems and Synthetic Biology.- Quantitative Verification Techniques for Biological Processes.- A New Mathematical Model for the Heat Shock Response.- Artificial Biochemistry.- Process Calculi Abstractions for Biology.- Deriving Differential Equations from Process Algebra Models in Reagent-Centric Style.- A Multivolume Approach to Stochastic Modelling with Membrane Systems.- Log-Gain Principles for Metabolic P Systems.- Hybrid Method for Simulating Small-Number Molecular Systems.- Formal Models of the Calyx of Held.- On Involutions Arising from Graphs.- Parallel Computing by Xeroxing on Transparencies.- Some Undecidable Dynamical Properties for One-Dimensional Reversible Cellular Automata.- On Using Divide and Conquer in Modeling Natural Systems.- Niching Methods: Speciation Theory Applied for Multimodal Function Optimization.
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A fundamental understanding of algorithmic bioprocesses is key to learning how information processing occurs in nature at the cell level. The field is concerned with the interactions between computer science on the one hand and biology, chemistry, and DNA-oriented nanoscience on the other. In particular, this book offers a comprehensive overview of research into algorithmic self-assembly, RNA folding, the algorithmic foundations for biochemical reactions, and the algorithmic nature of developmental processes. The editors of the book invited 36 chapters, written by the leading researchers in this area, and their contributions include detailed tutorials on the main topics, surveys of the state-of-the-art in research, experimental results, and discussions of specific research goals. The main subjects addressed are sequence discovery, generation, and analysis; nanoconstructions and self-assembly; membrane computing; formal models and analysis; process calculi and automata; biochemical reactions; and other topics from natural computing, including molecular evolution, regulation of gene expression, light-based computing, cellular automata, realistic modelling of biological systems, and evolutionary computing. This subject is inherently interdisciplinary, and this book will be of value to researchers in computer science and biology who study the impact of the exciting mutual interaction between our understanding of bioprocesses and our understanding of computation.
(source: Nielsen Book Data)

• Part I - Fundamentals.- Introduction.- Evolutionary Music.- Evolutionary Art.- Historical Perspective.- Part II - Evolutionary Music.- Interactive Evolutionary Music.- Cellular Automata, Artificial Life and Music.- Self-organised Music.- Part III - Evolutionary Art, Video and Design.- Interactive Evolutionary Art.- Electric Sheep.- Creative Evolutionary Design Tools.- Part IV - Artistic Perspectives.- Artists and Artificial Music.- Action Painting and Evolution.- Evolving Virtual Ecosystems.- Part V - Future Perspectives.- Coevolutionary Art.- Evolutionary Art and Hybrid Societies.- Open Problems.
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While improvements in computer performance are dramatically changing the computer-generated art industry, scientists in natural computing have teamed up with artists to examine how bioinspired systems can influence art, technology and even aesthetic appreciation. This comprehensive book gives an up-to-date survey of the relevant bioinspired computing research fields - such as evolutionary computation, artificial life, swarm intelligence and ant colony algorithms - and examines applications in art, music and design. The editors and contributors are researchers and artists with deep experience of the related science, tools and applications, and the book includes overviews of historical developments and future perspectives. The book will be of interest to computer scientists and artists engaged with natural computing techniques applied to art, music and design. The accompanying DVD contains image, video and music samples, as well as source code and demonstrations, providing a good starting point for beginners. The book is supported with a dedicated website.
(source: Nielsen Book Data)

• pt. 1. Evolutionary art
• pt. 2. Evolutionary music
• pt. 3. Real-world applications
• pt. 4. Artistic perspectives
• pt. 5. Future perspectives.

• pt. 1. Biology
• pt. 2. Art
• pt. 3. Embryogeny
• pt. 4. Engineering.

• pt. I. Exploiting multiple objectives : from problems to solutions
• pt. II. Machine learning with multiple objectives
• pt. III. Multiple objectives in design and engineering
• pt. IV. Scaling up multiobjective optimization.

• Introduction.- Fundamentals of Search, Optimization and Decision Making.- Multiobjective EA Basics.- Multiobjective Cybernetics - Does Nature Solve Problems and Are They Multiobjective?.- Modularity Causes Multiple Objectives in Natural and Computational Systems.- Problem Decomposition, Modularity and their relation to Multiple Objectives.- Spatial Predator-Prey Models of Multiobjective Optimization.- Solution Concepts in Co-evolution and Multi-objective Search.- How Multiple Objectives are Used and their Effects on Solution Selection Methods.- Ill-Defined Problem Spaces.- Constrained Optimization via MOEAs.- Multiobjectivization.- Helper Objectives.- Learning Evaluation Functions for Global Optimization.- Assessing the Intrinsic Number of Objectives.- Assessing the Intrinsic Number of Decision Variables.- Fuzzy Dominance, Favour and other Relations beyond Pareto Optimality.- Multiobjective Clustering.- Reducing Bloat in GP with Multiple Objectives.- Multiobjective GP for Human-Understandable Models.- Multiobjective Supervised Learning.- Multiobjective Association Rule Mining.- Protein-folding via MOEAs and Solution Selection.- Unveiling Salient Insights in Engineering Designs with MOEAs.- Conclusions.- MOEA: Triumph of Natural Computing.- References.- Index.
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Multiobjective problems involve several competing measures of solution quality, and multiobjective evolutionary algorithms (MOEAs) and multiobjective problem solving have become important topics of research in the evolutionary computation community over the past 10 years. This is an advanced text aimed at researchers and practitioners in the area of search and optimization. The book focuses on how MOEAs and related techniques can be used to solve problems, particularly in the disciplines of science and engineering. Contributions by leading researchers show how the concepts of multi objective optimization can be used to reformulate and resolve problems in broad areas such as constrained optimization, co evolution, classification, inverse modeling and design. The book is distinguished from other texts on MOEAs in that it is not primarily about the algorithms, nor specific applications, but about the concepts and processes involved in solving problems using a multi objective approach. Each chapter contributes to the central, deep concepts and themes of the book: evaluating the utility of the multi objective approach; discussing alternative problem formulations; showing how problem formulation affects the search process; and examining solution selection and decision making. The book will be of benefit to researchers, practitioners and graduate students engaged with optimization-based problem solving. For multi objective optimization experts, the book is an up-to-date account of emerging and advanced topics; for others, the book indicates how the multi objective approach can lead to fresh insights.
(source: Nielsen Book Data)

• Biological Foundations of Swarm Intelligence.- Swarm Intelligence in Optimization.- Swarm Robotics.- Routing Protocols for Next-Generation Networks Inspired by Collective Behaviors of Insect Societies: An Overview.- Applications.- Evolution, Self-organization and Swarm Robotics.- Particle Swarms for Dynamic Optimization Problems.- An Agent-Based Approach to Self-organized Production.- Organic Computing and Swarm Intelligence.
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The book's contributing authors are among the top researchers in swarm intelligence. The book is intended to provide an overview of the subject to novices, and to offer researchers an update on interesting recent developments. Introductory chapters deal with the biological foundations, optimization, swarm robotics, and applications in new-generation telecommunication networks, while the second part contains chapters on more specific topics of swarm intelligence research.
(source: Nielsen Book Data)

• Introduction.- Basics of Molecular Biology.- Basic Concepts: Strings, Graphs, and Algorithms.- String Algorithms.- Alignment Methods.- Introduction and Overview.- Physical Mapping.- DNA Sequencing.- Shotgun Sequencing.- Finding Signals in DNA Sequences.- Genome Rearrangements.- Phylogenetic Trees.- Haplotyping.- Molecular Structures.- References.- Index.
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Advances in bioinformatics and systems biology require improved computational methods for analyzing data, while progress in molecular biology is in turn influencing the development of computer science methods. This book introduces some key problems in bioinformatics, discusses the models used to formally describe these problems, and analyzes the algorithmic approaches used to solve them. After introducing the basics of molecular biology and algorithmics, Part I explains string algorithms and alignments; Part II details the field of physical mapping and DNA sequencing; and, Part III examines the application of algorithmics to the analysis of biological data. Exciting application examples include predicting the spatial structure of proteins, and computing haplotypes from genotype data. This book describes topics in detail and presents formal models in a mathematically precise, yet intuitive manner, with many figures and chapter summaries, detailed derivations, and examples. It is well suited as an introduction into the field of bioinformatics, and will benefit students and lecturers in bioinformatics and algorithmics, while also offering practitioners an update on current research topics.
(source: Nielsen Book Data)

• Introduction.- Genetic Algorithms.- Supervised Classification Using Genetic Algorithms.- Theoretical Analysis of the GA-Classifier.- Variable String Lengths in GA-Classifier.- Chromosome Differentiation in VGA-Classifier.- Multi-objective VGA-Classifier and Quantitative Indices.- Genetic Algorithms in Clustering.- Genetic Learning in Bioinformatics.- Genetic Algorithms and Web Intelligence.- Appendices.- References.- Index.
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This book provides a unified framework that describes how genetic learning can be used to design pattern recognition and learning systems. The book is unique in the sense of describing how a search technique, the genetic algorithm, can be used for pattern classification mainly through approximating decision boundaries, and it demonstrates the effectiveness of the genetic classifiers vis-a-vis several widely used classifiers, including neural networks. It provides a balanced mixture of theories, algorithms and applications, and in particular results from the bioinformatics and Web intelligence domains. This book will be useful to graduate students and researchers in computer science, electrical engineering, systems science, and information technology, both as a text and reference book. Researchers and practitioners in industry working in system design, control, pattern recognition, data mining, soft computing, bioinformatics and Web intelligence will also benefit.
(source: Nielsen Book Data)

• Part 1 Introduction: Introduction to Membrane Computing.-
• Part 2 Bioapplications: P System Models for Mechanosensitive Channels.- P Systems for Biological Dynamics.- Modeling Respiration in Bacteria and Respiration/Photosynthesis Interaction in Cyanobacteria Using a P System Simulator.- Modeling Cell-Mediated Immunity by Means of P Systems.- A Membrane Computing Model of Photosynthesis.- Modeling p53 Signaling Pathways by Using Multiset Processing.-
• Part 3 Computer Science Applications: Static Sorting P Systems.- Membrane-Based Devices Used in Computer Graphics.- An Analysis of a Public-Key Protocol with Membranes.- Membrane Algorithms: Approximate Algorithms for NP-Complete Optimization Problems.- Computationally Hard Problems Addressed Through P Systems.-
• Part 4 Linguistics Applications: Linguistics Membrane Systems and Applications.- Parsing with P Automata.-
• Part 5 Membrane Software: Available Membrane Computing Software.-
• Part 6 Selective Bibliography of Membrane Computing.
• (source: Nielsen Book Data)

Membrane computing is a branch of natural computing which investigates computing models abstracted from the structure and functioning of living cells and from their interactions in tissues or higher-order biological structures. The models considered, called membrane systems (P systems), are parallel, distributed computing models, processing multisets of symbols in cell-like compartmental architectures. In many applications membrane systems have considerable advantages - among these are their inherently discrete nature, parallelism, transparency, scalability and nondeterminism. In dedicated chapters, leading experts explain most of the applications of membrane computing reported so far, in biology, computer science, computer graphics and linguistics. The book also contains detailed reviews of the software tools used to simulate P systems.
(source: Nielsen Book Data)

• Methodologies.- Neural Network Methodologies.- Evolutionary Methodologies.- Grammatical Evolution.- The Particle Swarm Model.- Ant Colony Models.- Artificial Immune Systems.- Model Development.- Model Development Process.- Technical Analysis.- Case Studies.- Overview of Case Studies.- Index Prediction Using MLPs.- Index Prediction Using a MLP-GA Hybrid.- Index Trading Using Grammatical Evolution.- Adaptive Trading Using Grammatical Evolution.- Intra-day Trading Using Grammatical Evolution.- Automatic Generation of Foreign Exchange Trading Rules.- Corporate Failure Prediction Using Grammatical Evolution.- Corporate Failure Prediction Using an Ant Model.- Bond Rating Using Grammatical Evolution.- Bond Rating Using AIS.- Wrap-up.
• (source: Nielsen Book Data)

Predicting the future for financial gain is a difficult, sometimes profitable activity. The focus of this book is the application of biologically inspired algorithms (BIAs) to financial modelling. In a detailed introduction, the authors explain computer trading on financial markets and the difficulties faced in financial market modelling. Then Part I provides a thorough guide to the various bioinspired methodologies b" neural networks, evolutionary computing (particularly genetic algorithms and grammatical evolution), particle swarm and ant colony optimization, and immune systems. Part II brings the reader through the development of market trading systems. Finally, Part III examines real-world case studies where BIA methodologies are employed to construct trading systems in equity and foreign exchange markets, and for the prediction of corporate bond ratings and corporate failures. The book was written for those in the finance community who want to apply BIAs in financial modelling, and for computer scientists who want an introduction to this growing application domain.
(source: Nielsen Book Data)

• Basics.- Research in Evolutionary Computation.- The New Experimentalism.- Statistics for Computer Experiments.- Optimization Problems.- Designs for Computer Experiments.- Search Algorithms.- Results and Perspectives.- Comparison.- Understanding Performance.- Summary and Outlook.
• (source: Nielsen Book Data)

This book introduces the new experimentalism in evolutionary computation, providing tools to understand algorithms and programs and their interaction with optimization problems. It develops and applies statistical techniques to analyze and compare modern search heuristics such as evolutionary algorithms and particle swarm optimization. The book bridges the gap between theory and experiment by providing a self-contained experimental methodology and many examples.
(source: Nielsen Book Data)

• Global optimization.- The Motivation for Differential Evolution.- Critical values for the control parameters of differential evolution algorithms.- The Differential Evolution Algorithm.- Fast Evolution Strategies.- Benchmarking Differential Evolution.- On the usage of differential evolution for function optimization.- Problem Domains.- Shape design and optimization by genetic algorithm.- Architectural Aspects and Computing Environments.- Computer Code.- Computer Code.- Applications.- Genetic Algorithms and Related Techniques for Optimizing Si-H Clusters: A Merit Analysis for Differential Evolution.- Non-Imaging Optical Design Using Differential Evolution.- Optimization of an Industrial Compressor Supply System.- Minimal Representation Multi-Sensor Fusion Using Differential Evolution.- Determination of the Earthquake Hypocenter: A Challenge for the Differential Evolution Algorithm.- Parallel Differential Evolution: Application to 3-D Medical Image Registration.- Design of Efficient Erasure Codes with Differential Evolution.- FIWIZ - A Versatile Program for the Design of Digital Filters Using Differential Evolution.- Optimization of Radial Active Magnetic Bearings by Using Differential Evolution and the Finite Element Method.- Application of Differential Evolution to the Analysis of X-Ray Reflectivity Data.- Inverse Fractal Problem.- Active Compensation in RF-Driven Plasmas by Means of Differential Evolution.
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Problems demanding globally optimal solutions are ubiquitous, yet many are intractable when they involve constrained functions having many local optima and interacting, mixed-type variables. The differential evolution (DE) algorithm is a practical approach to global numerical optimization which is easy to understand, simple to implement, reliable, and fast. Packed with illustrations, computer code, new insights, and practical advice, this volume explores DE in both principle and practice. It is a valuable resource for professionals needing a proven optimizer and for students wanting an evolutionary perspective on global numerical optimization.
(source: Nielsen Book Data)