Artificial Life Books

This book is based on the author's PhD dissertation and it shows (you can download the dissertation from the web). There is page after page of mind numbing step-by-step derivations that do not add too much to the discussion. I would have enjoyed the book more if Spears had shortened some of his derivations. I found the results interesting. Although some of the conclusions seem fairly obvious after reading the book, I think it is important that someone took the time to come up with the mathematical models to formalize things.

The empirical approach is very interesting, and I wish more people would follow and improve on Spears' ideas. Empirical studies of evolutionary algorithms are justly critized for being too limited to a few "standard" functions that do not show much about the capabilities and limitations of the algorithms. Spears took a good step in emulating the machine learning comunity and using test problem generators. With these generators, the experimenters can play around with parameters such as the multimodality or noise in a problem and make systematic empirical studies of the algorithms. Unfortunately, it is difficult to translate from those systematic studies to real life. For example, how much noise or how many peaks are in real-life problems?

Still, I would recommend to go and read this book (or the free dissertation). Skip the equations, though, and get to the point.

BTW, Dr Gordon (the first reviewer) is married to Spears, which may explain some of the excitement in her review...

This book is an essential resource for anyone studying the theoretical underpinnings of evolutionary algorithms (EAs). The book very carefully analyzes the effects of two fundamental evolutionary operators, recombination and mutation, and their interaction with evolutionary selection. This analysis significantly enhanced my understanding of EAs because of the fundamental role that these operators play. The book begins with the more traditional static analysis approach, but soon it transitions to a very exciting dynamic analysis. Just as neurophysiologists have discovered that when studying the brain it helps to view it as a dynamic process, Spears illustrates how much better we can understand EAs when using dynamic models, such as the popular Markov chain model approach. One of the best parts of the book was the creative use of problem generators for empirically testing the theory and for characterizing the classes of problems for which each EA operator is more effective. This was exciting for two reasons. For one, it encourages EA researchers to break away from myopic use of the same old test suites. Secondly, the problem characterization has tremendous potential value for practical applications of EAs.

Another of my favorite parts of the book was Spears' novel algorithm for compressing Markov chains. I particularly liked the mathematical analysis, which was both elegant and clear. Because Markov chains are widely used, e.g., in operations research, control theory, and artificial intelligence, this compression algorithm has wide-reaching implications for reducing the complexity of modeling a variety of systems.

The intended audience for Spears' book is computer scientists, mathematicians, and biologists, as well as students of evolutionary processes. To make the book accessible to such a diverse audience, the presentation is exceptionally clear and devoid of excessive jargon and obscure mathematics. Only an undergraduate level math background is required. One thing that I found mildly distracting was the repetition between chapters. The reason for the repetition was to make the chapters as self-sufficient as possible. Nevertheless, I read the book as a continuous whole and for anyone who does this I recommend skimming or skipping over the redunant portions. If this is done, the reader can maintain a high level of interest.

In conclusion, because of the valuable insights I gleaned from this book I believe it should be required reading for anyone who wishes to gain a better understanding of evolution as simulated by EAs. Spears' rigorous analyses and lucid explanations make this a delightful book to read.

The subject of this book would seem a natural one, given the evolutionary paradigm in biology. Genetic algorithms and evolutionary programming have now found use in many different fields such as physics, financial engineering, network modeling, and computational radiology, to name a few. This use will no doubt continue as computer processing power increases in the future. Although genetic/evolutionary approaches are still much more effective from a computational point of view than strict combinatorial ones, they are still very time intensive, and for many problems have yet to compete with ordinary Monte Carlo techniques. This book gives a brief overview of how evolutionary algorithms are used in bioinformatics, with emphasis on genetic sequence alignment and protein folding. The book does not offer in-depth discussion on these algorithms, but does give references where more information can be obtained. Therefore the book could be described as a literature survey, at least for the chapters that I read, which did not include those on protein folding.

The book is written for the computer scientist who wants to move into bioinformatics, and the biologist, who needs more background in these types of algorithms. Therefore, the editors of the book include two introductory chapters, one introducing bioinformatics for computer scientists, the other an introduction to evolutionary computation for biologists. The latter is more detailed, and the authors introduce the biologist to some of the elementary aspects of evolutionary computation. One interesting, but too short discussion is on the "No Free Lunch Theorem", which implies that evolutionary programs are not in any sense "universal", in that the choice of such a program will depend on the problem at hand, and in fact there may be many such programs for the problem, each with their own performance properties. The theorem is not proved in this book, but references to the proof are given. However, the proof involves a level of mathematics that a biologist would probably not have knowledge of, and so this reference would not be accessible to such a reader. In addition, the theorem has generated a lot of controversy, but the authors do point this out. The authors also discuss effectively the difference between the analytical and heuristic approaches to sequence alignment, setting the stage for later chapters in the book. The problem of local search algorithms getting "trapped" in local minima is also given a very intuitive and understandable treatment by the authors.

The book also includes a discussion on the "DNA sequence reconstruction problem". Algorithms for dealing with this problem are recommended and the the problem is presented as one in integer programming. The authors present a hybrid evolutionary algorithm for dealing with this problem. They characterize this algorithm as being hybrid since it does make use of "crossover" operators and a heuristic "greedy-improvement" method. The discussion of this algorithm is only brief, but references are given. However the main reference is not yet available as it is very recent and in press, and, although the authors do include a fairly lengthy discussion of computational experiments, without a detailed description of the algorithm or source code, their results cannot be checked or validated.

The contrast between optimization theory and evolutionary algorithms is a common theme in the book, with emphasis on the use of evolutionary algorithms to design scoring schemes for sequence alignment where optimization issues can be ignored. The difference between the optimal alignment obtained by various mathematical techniques and the correct (biological) alignment is carefully pointed out. Thus one must be able to tell whether an objective function is relevant from a biological standpoint. In chapter 5 of the book for example, the author introduces an alignment algorithm based on a combination of simulated annealing (SA), and genetic algorithms (GA), called appropriately SAGA. This chapter is the most helpful one in the book, for the author gives pseudocode for this algorithm, with Web links given for obtaining the source code. This allows the interested reader to study the efficacy of the SAGA algorithm in doing muliple sequence alignment.

The use of simulated evolution to find optimal neural networks for identifying coding regions is discussed in chapter 9 of the book. The use of genetic algorithms to assign the weights in a neural network is well-known. The authors point out a further advantage in their use, namely that evolutionary neural networks can adapt to unexpected inputs on their own, and thus do not require any intervention on the part of the user. References are given that elaborate on the power of this approach. Readers who have worked with neural networks will understand fully the need for improvements over back-propagation and the need for automatic topology selection. The authors do not show however that the function-approximation ability of neural networks, so important from both a mathematical and applications standpoint, is improved by their approach.

I like this book. Bioinformatics is a ripe area for applying evolutionary algorithms and the book provides a good overview of many different applications. Some chapters are more polished than others, but that's to be expected. The editors do an excellent job of introducing both bioinformatics and evolutionary computation to their respective audiences. I can't think of another book that makes such an effort to integrate the two communities.

I see another reviewer gave the book 3 stars. I've no idea why. The book is excellent, and has encouraged me to take a look at other papers in this area.

This is a grat book because it is multidiciplinary, but maybe content wise, it is not very original-nor focused in consciousness. There are papers dealing with squitzofrenia and ageing. Greenfield contribution presents her neuronal assemblie theory. Rose writes a great introduction to consciousness studies, and it alone pays for the entrance ticket. It is always interesting to read about Aleksanders work on artificial consciousness, and Penrose on Quantum Consciousness. The collection as a whole covers many topics, and it is a valuable contribution to consciousness studies. It is also not at all technical, so it can serve as an introductory work of the field. Again, originality and content do will not live to many expectations, but I certialy recomend the book.

While every section of science studies brain, mind, culture and psychopathology on its own grounds, this collection of essays shows how all disciplines together can shed light on each other's field of interest and solve some tough question. When I purchased this book I was looking for a reflection of mr. Rose's ideology of science, which it turned out not to be. Nonetheless, it is very relevant and quite interesting!
(I later found more of mr. Rose's thoughts in a book he edited with a Hillary Rose - his wife? - called "Alas Poor Darwin". It shows the untenability of Evolutionary Psychology. His own article in that collection is by far the best of all. Also, his "Not in Our Genes" with Richard Lewontin is supposed to be a reflection of his philosophy of science.)

Good Book and useful for research and as a course work

The book is recognition of the fact that computational biology is only now starting to emerge as an important scientific discipline in its own right. The book addresses 2 audiences whose research intersects. One is those doing other computational work and who perhaps already use these kernel methods, and who are unaware of issues in biology that need to be studied. While the other is those already in computational biology, but who have never used kernel methods. Essentially, the early chapters address these needs.

Then the bulk of the book gives examples where kernel methods are already being used in computational biology. The diversity of the examples should prove inspiring to some readers.

The book also goes somewhat briefly into using support vector machines. If this interests you, try consulting "Support Vector Machines for Pattern Classification" by S Abe, Springer 2005, 1-85233-929-2. It has a fuller treatment of the idea.

There are 8 sections, and 70 papers.
1. Foundations and Epistemology
2. Origins of Life and Evolution
3. Adaptive and Cognitive Systems
4. Artificial Worlds
5. Robotics and Emulation of Animal Behavior
6. Aoxiwriwa ns Xollwxricw Vwhcioe
7. Biocomputing
8. Applications and Common Tools.
I did not know all of them.
I checked "Evaluation of Learning Performance of Situated Embodied Agents." by Maja J Mataric in Robotics sections.

Was a very good book for those with iterest on how repoduction in animals works. The first chapters help to better understand the book rather then assuming that everyone knows all the tec. terms.

Remember Conway's Game of Life? Surely you must, if you are interested in this book. The Game has been around since the 70s. The editors have cultivated recent research papers that demonstrate how far the field has advanced. Reinforced by some pretty colour plates that depict artificial entities [dare we call them living?] in some surroundings. These include the modelling of bee flights through a garden, and simulated trajectories of a group of bacteria.

Nor is the Game of Life ignored. One plate shows it in three dimensions. The Game is played in 2 dimensions, with time as the third dimension. An obvious choice that gives interesting trajectories of the cells.

The narrative adds to the illustrations. By describing a variety of computer simulations [worlds?]. Where the experimenter can tweak many parameters, and watch her world unfold. Some worlds are impressively rich in complexity of observed behaviours.

The only drawback in the book is its skimpy index. A mere two pages. It should have been more detailed.

This is a great book to address an unfilled need. If you want to know every step in implementing embryo recovery and transfer, this book is for you.

John does a great job of putting together many simple but detailed steps into one book. Anyone who is considering learning the art and science of embryo transfer in cattle should first read this book!

Sarah Kember's Cyberfeminism and Artificial Life follows in the footsteps of Donna Haraway, N. Katherine Hayles and Alison Adams in extending substantial feminist theoretical engagements with the realm of science and technology. Unlike Haraway and Hayles, Kember's focus on artificial life no longer centres the work on human subjectivity per se, but rather broadens the realm of inquiry to life more generally. Moreover, while Alison Adam's Artificial Knowing: Gender and the Thinking Machine (1998) focused mainly on the scientific development and cultural resonances surrounding artificial intelligence (AI), Kember's work takes a similar political project but focuses on artificial life (ALife). The key difference between the two is that AI primarily focuses on electronically replicating a human-like mind, working from the top-down, whereas ALife attempts to simulate evolution in a digital system, starting from the smallest byte-size computational programs, attempting to synthesise 'life' from the bottom-up. Kember's stated aims in her book are clear: 'to trace the development of identities and entities within the global information network encompassing both human and non-human environments, and to offer a pluralised cyberfeminist engagement with artificial life as both a discipline and cultural discourse' (p.vii). The differentiation between the scientific discipline and more popular cultural articulation of ALife ideas is particular important, allowing Kember to make specific and separate analyses of the work of scientists and of ALife as imagined more broadly. However, this separation does not prevent a broad picture of ALife being constructed, and it significantly maps areas of both cultural and scientific intersection and divergence.

In her brief first chapter, Kember outlines two key points which will guide her reading of ALife. Firstly, that while ALife simulations may hold great potential for revealing information about life-as-we-know-it by examining the natural world's operations (weak ALife), ALife research often slips into arguing that the digital experiments actually illustrate life-as-it-could-be or real 'life' (the strong ALife claim). Secondly, Kember argues that in recent years there has been a 'biologisation of computer science' which entails digital and computational simulations being guided mainly by the biological sciences. While past scientific efforts, such as AI design, tended to view the body as a machine - the brain as a computer, heart as a pump, and so forth - ALife design appears to have come full circle. Kember argues further that this instils a 'new biological hegemony' in the computational and technosciences (pp.6-7). Chapter two, 'The meaning of life part I: The new biology' immediately explores Kember's claims, focusing on the well-known work of Richard Dawkins and his thesis on the selfish gene. Kember reads Dawkins as arguing from a perspective of genetic determinism. Moreover, she argues further that the shift in Dawkins' work from genes to memes--seemingly self-driven culturally replicating ideas--is just a slight of hand which attempts to escape the eugenic overtones of genetic determinism, while actually reinscribing those idea en masse. Dawkins' work is highly influential upon ALife designers as their goal is similarly to cause the spontaneous evolution of life from basic originary units (digital genes), and Kember concludes that the sociobiological genetic determinism of Dawkins is intrinsic to many current ALife design projects. The third chapter, 'Artificial Life', looks more specifically at scientific ALife designers and their work. While many of the ALifers that Kember discusses do appear to hold Dawkinsesque views, Kember makes a number of strong points about inconsistencies between such views and the actual operation of ALife simulations. Key among these is the role of the creator: while evolutionary theory may have 'killed God', ALife designers who purport to model evolution necessarily involve the scientist-as-creator setting the original Garden-of-Eden-like parameters, in effect acting as God for their digital subjects. Similarly, Kember charts the more traditional feminist reading of ALife scientists as enacting parthogenic fantasies of masculine reproduction and birth without the need for women or mothers. The chapter concludes with a carefully balanced call for feminist engagement with ALife which is not exclusively about resisting the hegemony of the biological, but works productively with these trends.

Chapter four shares considerable ground with The Video Game Theory Reader as Kember examines contemporary computer games which use ALife theories, such as Maxis Inc's range of Sim games and Creatures which was actually designed by ALife scientist Steve Grand. Kember looks at most of the Sims franchise, but focuses on SimEarth, which is a planetary evolution simulator, and SimLife which emphasises genetics and evolution in more specific ways. Kember concludes that what 'Sim games do most effectively is naturalise genetic and evolutionary determinism in an environmentalist educational scenario and - in the case of SimLife - introduce ALife in to one area of popular culture' (p.91). Steve Grand's Creatures also provided some insights into the tensions between ALife/game designers and the public at large. Kember notes that while Grand's game was designed to emphasize kinship with the artificial life creatures, often the biggest appeal to gameplayers was to create hybrid creatures or to torture existing ones. These observations, Kember concludes, show a lack of kinship with ALife in the public consciousness. Chapter five, 'Network identities' expands the ideas of ALife beyond science and specific games to look at proto-ALife, such as 'Bots', which are tiny software agents spread across the internet for various purposes and which are sometimes self-editing. Kember also analyses Nick Gessler's computational anthropology work and his 'artificial culture' simulations which seek not only to synthesize life, but culture per se as well. Chapter six, 'The meaning of life part 2: Genomics', goes a step further, analysing transgenic organisms and so forth which Kember defines as 'wetware artificial life-forms' (p.147). Cloning (both human and non-human), the human genome project, as well as popular films such as Alien: Resurrection and Gattaca are all analysed as part of the broader cultural and genomic imaginary which is, in part, informed by ALife discourses. Kember is careful in these last two chapters to emphasise the importance of dialogue between feminism and ALife (and related discourses) rather than make strongly judgemental claims.

The final two chapters attempt to bridge the so-called Science Wars, in which humanities and literary writing was (sometimes rightly) accused of engaging with scientific writing without taking the time to understand the scientific concepts. Kember argues strongly for a cyberfeminist engagement with ALife discourses and technoscience in general which keeps dialogue open and ethics firmly in sight. Kember concludes that it is at times necessary to escape the nature versus culture debates which have characterised the Science Wars, and which much feminist writing has relied upon, in favour of a 'bioethics of posthuman identity within alife discourse which cyberfeminism might productively contribute to' (p.216). While Kember's conclusions are certainly pragmatic in terms of keeping dialogue open, they may be a bit open ended for some readers. However, there can be no doubting the significant contribution Kember has made in articulating the important dialogue between feminism and artificial life discourses. Moreover, Kember's work has considerable insights beyond its immediate target audience, making this an important text for those involved in research into posthumanism, cybercultural studies, feminist theory and ideas of subjectivity as they are rearticulated in the early twenty-first century.

Design Principles was written by a collection of authors specializing in diverse fields from computer scientists, theoretical biologist, pathologist, chemists and neurologists. The book began as a workshop held at the Sante Fe Institute in 1999 by the same name. While it is not a collection of abstracts and papers from this workshop, it did serve as the motivating factor to write the book. Design Principles starts with a description of the immune system that serves as a basic introduction both to the topic and to the biases of the multiple authors. Steven Hofmeyr offers a "gentle introduction to the immune system for researchers who do not have much background in immunology." (p.3) The chapter is titled "Introduction to the Immune System". Right off the biases of the book are exposed as Hofmeyr has a Ph.D. in computer sciences with a focus on information detection and distribution. Hofmeyr does an excellent job describing very complex biology without assuming that the reader has a background in either immunology or systems. While the author is gentle in his presentation the chapter is very dense with information which one hopes will be reiterated as one needs the information further in the book. Overall, I would recommend this book to anyone who is interested in pursuing agent based modeling of biological systems. This book would be particularly interesting to those pursing interests in modeling the process of immunity. My final criticism would be that the title is a bit misleading as I would suggest that the book only gives limited mention and thought to other types of autonomous systems with the exception of Bonabeau's description of control mechanisms learned from social insects and Gordon's chapter titled, "Task Allocation in Ant Colonies."