Hopping, climbing and swimming robots, nano-size neural networks, motorless walkers, slime mould and chemical brains - "Artificial Life Models in Hardware" offers unique designs and prototypes of life-like creatures in conventional hardware and hybrid bio-silicon systems. Ideas and implementations of living phenomena in non-living substrates cast a colourful picture of state-of-art advances in hardware models of artificial life.

This book gives a state-of-the-art overview of modeling growth and form of marine sessile organisms - such as stromatolites, algae, and metazoans including stony corals, hydrocorals, octocorals, and sponges -, using large-scale computing techniques, scientific visualization, methods for analyzing 2D and 3D forms, and particle-based modeling techniques. It originates from the workshop on Modeling Growth and Form of Marine Sessile Organisms, held at the National Center for Ecological Analysis and Synthesis, Santa Barbara, California, August 1999. Experts from various disciplines including developmental biology, ecology, computer science, physics and mathematics, who have research interests in modeling the development of these organisms have been invited to contribute. The book describes all the steps required to develop and experimentally validate morphological models including collecting biological information and methods for specifying and comparing forms. Examples are given of how models are currently being applied to simulate growth and form of marine sessile organisms. Potential applications of growth models and morphological analyses in modern and paleo-bio-monitoring, the detection of environmental change, and the conservation and restoration of marine ecosystems and aquaculture are addressed. The combination of simulation models with laboratory and field experiments provides a powerful tool to obtain insights on how the growth forms of marine organisms emerge from physical, genetic and environmental influences.

The term evolutionary computing (EC) refers to the study of the foundations and applications of certain heuristic techniques based on the principles of natural evolution, and thus the aim when designing evolutionary algorithms (EAs) is to mimic some of the processes taking place in natural evolution.

The advent of powerful processing technologies and the advances in software development tools have drastically changed the approach and implementation of computational research in fundamental properties of living systems through simulating and synthesizing biological entities and processes in artificial media. Nowadays realistic physical and physiological simulation of natural and would-be creatures, worlds and societies becomes a low-cost task for ordinary home computers. The progress in technology has dramatically reshaped the structure of the software, the execution of a code, and visualization fundamentals. This has led to the emergence of novel breeds of artificial life software models, including three-dimensional programmable simulation environment, distributed discrete events platforms and multi-agent systems. This second edition reflects the technological and research advancements, and presents the best examples of artificial life software models developed in the World and available for users.

This book considers a relatively new metric in complex systems, transfer entropy, derived from a series of measurements, usually a time series. After a qualitative introduction and a chapter that explains the key ideas from statistics required to understand the text, the authors then present information theory and transfer entropy in depth. A key feature of the approach is the authors' work to show the relationship between information flow and complexity. The later chapters demonstrate information transfer in canonical systems, and applications, for example in neuroscience and in finance.

The term evolutionary computing (EC) refers to the study of the foundations and applications of certain heuristic techniques based on the principles of natural evolution, and thus the aim when designing evolutionary algorithms (EAs) is to mimic some of the processes taking place in natural evolution.

This book introduces research presented at the “International Conference on Artificial Intelligence: Advances and Applications-2019 (ICAIAA 2019),” a two-day conference and workshop bringing together leading academicians, researchers as well as students to share their experiences and findings on all aspects of engineering applications of artificial intelligence. The book covers research in the areas of artificial intelligence, machine learning, and deep learning applications in health care, agriculture, business and security.

The term evolutionary computing (EC) refers to the study of the foundations and applications of certain heuristic techniques based on the principles of natural evolution, and thus the aim when designing evolutionary algorithms (EAs) is to mimic some of the processes taking place in natural evolution.