https://www2.econ.iastate.edu/tesfatsi/DeepLearningInNeuralNetworksOverview.JSchmidhuber2015.pdf

Deep learning in neural networks

I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

Molecular self-assembly is the spontaneous association of molecules under equilibrium conditions into stable, structurally well-defined aggregates joined by noncovalent bonds. Molecular self-assembly is ubiquitous in biological systems and underlies the formation of a wide variety of complex biological structures. Understanding self-assembly and the associated noncovalent interactions that connect complementary interacting molecular surfaces in biological aggregates is a central concern in structural biochemistry. Self-assembly is also emerging as a new strategy in chemical synthesis, with the potential of generating nonbiological structures with dimensions of 1 to 10(2) nanometers (with molecular weights of 10(4) to 10(10) daltons). Structures in the upper part of this range of sizes are presently inaccessible through chemical synthesis, and the ability to prepare them would open a route to structures comparable in size (and perhaps complementary in function) to those that can be prepared by microlithography and other techniques of microfabrication.

Molecular self-assembly and nanochemistry: A chemical strategy for the synthesis of nanostructures

http://mahdimaherani.persiangig.com/document/Ant%20colony%20optimization%20theory%20A%20survey.pdf

Ant colony optimization theory: a survey

Genetic programming (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 high-fitness 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. This unique overview of this exciting technique is written by three of the most active scientists in GP. See www.gp-field-guide.org.uk for more information on the book.

/pdf/a-field-guide-to-genetic-programming-3wkkmsw81n.pdf

A Field Guide to Genetic Programming

The theory of fitness landscapes has been developed to provide a suitable mathematical framework for studying the evolvability of a variety of complex systems In evolutionary computation the notion of evolvability refers to the efficiency of evolutionary search It has been shown that the structure of a fitness landscape affects the ability of evolutionary algorithms to search Three characteristics specify the structure of landscapes These are the landscape smoothness, ruggedness and neutrality The interplay of these characteristics plays a vital role in evolutionary search This has motivated the appearance of a variety of techniques for studying the structure of fitness landscapes An important feature of these techniques is that they characterize the landscapes by their smoothness and ruggedness, ignoring the existence of neutrality Perhaps, the reason for this is that the role of neutrality in evolutionary search is still poorly understoodIn this chapter some recent results on the spectral properties of the algebraic structures of fitness landscapes are summarized to provide a basis for studying the landscape structure This approach is further employed to introduce an information analysis that characterizes the structure of fitness landscapes in terms of their smoothness, ruggedness and neutrality The findings are finally applied in a study of the fitness landscapes generated by evolving digital circuits using an idealized model of a field-programmable gate array The landscapes of this engineering problem are quite different from many recently studied landscapes that tend to be defined over simplified combinatorial and optimization problems The difference originates from the genotype representation that is a configuration defined over two completely different alphabets This makes the study of the corresponding landscapes much more involved It is shown that the circuit evolution landscapes are products of subspaces with different characteristics They are landscapes with vast neutrality and sharply differentiated plateau

Smoothness, ruggedness and neutrality of fitness landscapes: from theory to application

Cartesian Genetic Programming (CGP) has previously shown capabilities in image processing tasks by evolving programs with a function set specialized for computer vision. A similar approach can be applied to Atari playing. Programs are evolved using mixed type CGP with a function set suited for matrix operations, including image processing, but allowing for controller behavior to emerge. While the programs are relatively small, many controllers are competitive with state of the art methods for the Atari benchmark set and require less training time. By evaluating the programs of the best evolved individuals, simple but effective strategies can be found.

Evolving simple programs for playing atari games

In this paper we present a new chromosome representation for evolving digital circuits. The representation is based very closely on the chip architecture of the Xilinx 6216 FPGA. We examine the effectiveness of evolving circuit functionality by using randomly chosen examples taken from the truth table. We consider the merits of a cell architecture in which functional cells alternate with routing cells and compare this with an architecture in which any cell can implement a function or be merely used for routing signals. It is noteworthy that the presence of elitism significantly improves the Genetic Algorithm performance.

Aspects of Digital Evolution: Geometry and Learning

The use of genetic algorithms for the generation of optimal state assignments for synchronous finite state machines (FSM) is proposed. Results are presented to show that, in all examples attempted, the resulting state assignments are better than or at least as good as those produced by SPECTRAL, NOVA and MUSTANG and also closed partition assignments. On average, the genetic algorithm produced assignments with 33% less logic than the best produced by other algorithms.

State assignment of finite state machines using a genetic algorithm

Intrinsic evolution in evolvable hardware research has hitherto been limited to using standard electronic components as the media for problem solving. However, recently it has been argued that because such components are human designed and intentionally has predictable responses; they may not be the optimal medium to use when trying to get a naturally inspired search technique to solve a problem. Evolution has been demonstrated as capable of exploiting the physical properties of material to form solutions; however, by giving evolution only conventional components, we may be placing arbitrary constraints on our ability to solve certain problems. We have shown for the first time, that liquid crystal can be used as the physical substrate for evolution. We demonstrate that it is possible to evolve various functions, including a tone discriminator, in materio.

Evolution in materio: a tone discriminator in liquid crystal

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