2005: natural computing - concepts and applications
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Natural Computing:Natural Computing:A Brief Survey of Ideas and A Brief Survey of Ideas and
ApplicationsApplications
BIC 2005: BIC 2005: International Symposium on Bio-Inspired ComputingInternational Symposium on Bio-Inspired Computing
Johor, MY, 9Johor, MY, 9thth September 2005 September 2005
Dr. Leandro Nunes de Castrolnunes@unisantos.br
http://lsin.unisantos.b/lnunesCatholic University of Santos - UniSantos/Brazil
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Imagine a world where computers can create new universes, and within these universes there are natural forms that reproduce, grow and adapt. Imagine natural patterns, mountains, ant colonies, immune systems and brains, all learning and evolving, and becoming increasingly more adapted to the environment. Imagine if our computers could contain new forms of life. Think how this would affect our lives. Maybe we could automatically create house and music design, new forms of protecting computers against invaders, new forms of solving complex problems, new organisms and new forms of computing.
Now stop imagining.Welcome to Computing in the New Millennium. Welcome to the Natural Computing age!
Foreword
Adapted from Digital Biology, by P. Bentley.
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Outline
Part I: Introduction and Motivation Some ideas and challenges
Part II: Looking at Nature with Different Eyes Nature’s solutions: Some samples
Part III: Natural Computing Computing inspired by nature The simulation and emulation of natural
phenomena in computers Computing with natural materials
Part III: Computing in the New Millennium
Part I
Introduction and MotivationIntroduction and Motivation
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Current Computer Technology
Turing Machines (TM) Computational device idealized by A. Turing in
1936 If a problem can be computed, then it can be
computed by a Turing Machine
J. von Neumman architecture
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Features of Current Computers
General-purpose machines Manipulate precisely precise information* Address-based memory Serial processing* Are not capable of generalizing Are not fault tolerant (robust) Are not adaptable* …
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Are You Ready?
1. Develop a computer program to distribute products of a company throughout the country.
2. Generate a computer model to simulate the evacuation program of a building undergoing fire.
3. What are the new technologies to complement or supplement silicon-based hardware?
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Why Are These Questions Hard?(1. Products Distribution)
How many are the possible routes?
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Why Are These Questions Hard?(2. Behavioral Simulation)
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Why Are These Questions Hard?
(3. New Technologies)
Moore’s Law: The processing power
of silicon-based computers doubles approximately every couple of years
By the end of the next decade (2020) we may have reached the (miniaturization) limit of current computer technology
N.
of
ato
ms
per
bit
Year
2020: 1 atom per bit
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What all these questions have in common?
The answer to all of them require a paradigm shift
Where can we find answers to them?Where can we find answers to them?
• Where all these problems and difficulties have been solved and dealt with from ages: In NATURE!!
Part II
Looking at Nature with Different Eyes----
Nature’s Solutions: Some Samples
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Natural Architects
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Natural Deliverers and Cleaners
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Natural Behavior Animators
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Natural Computer
Part III
Natural ComputingNatural Computing
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From Nature to Computing: Natural Computing
Nature x Computing Natural computing is the terminology
used to encompass three paradigms: Computing inspired by nature The simulation and emulation of natural
phenomena in computers Computing with natural materials
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The Philosophy of Natural Computing
Part III-A
Computing Inspired by NatureComputing Inspired by Nature
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Main Ideas
Nature has evolved through ages in order to solve complex real-world problems
Examples abound: nest building, nest cleaning, main senses (hearing, seeing, touching, smelling, tasting), etc.
Computer algorithms based or inspired by nature have been developed for some time: Either to model nature, Or to solve complex real-world problems
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Main Themes
Neurocomputing Evolutionary Computing Swarm Intelligence Immunocomputing Artificial Chemistry Growth and Developmental Algorithms etc.
Older approaches
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Neurocomputing
Inspiration
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Design principles: Artificial neuron: basic information
processing and storage unit Network architecture: how the artificial
neurons are interconnected Learning algorithm: guides the dynamics
(adaptability) of the system
Neurocomputing
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Neurocomputing
Basic artificial neuron Some activation
functions
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Network architectures Single-layer feedforward network
Neurocomputing
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Network architectures Multi-layer feedforward network
Neurocomputing
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Network architectures Recurrent network
Neurocomputing
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Learning algorithms/rules: Hebb learning Single-layer perceptron Adaline ART Multi-Layer perceptron Self-organizing networks Hopfield networks Grossberg networks …
Neurocomputing
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Why neurocomputing? Learning capability Parallel processing Generalization capability Inherently distributed Robust ...
Neurocomputing
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Scope: Function approximation Clustering Classification Pattern recognition Control …
Mature field with innumerable academic, industrial, commercial and governmental applications
Neurocomputing
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+ Reproduction
+ Genetic Variation
+ Selection
Evolutionary Computing
Inspiration
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The power of (artificial) evolution
Evolutionary Computing
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Evolutionary Computing
The power of evolution
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Design principles: Population of individuals* Reproduction with genetic inheritance Genetic variation Selection
Evolutionary Computing
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Standard evolutionary algorithm
Evolutionary Computing
procedure [P] = standard_EA(pc,pm)
initialize Pf eval(P)P select(P,f)t 1while not_stopping_criterion
do,P reproduce(P,f,pc)P variate(P,pm)f eval(P)P select(P,f)t t + 1
end whileend procedure
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Main types of evolutionary algorithms: Evolutionary programming Evolution strategies Genetic algorithms Genetic programming* Classifier systems*
Evolutionary Computing
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Why evolutionary computing? A population may explore and exploit more
efficiently than a single individual Importance of information (experience)
exchange Maintenance of good quality solutions Diversity and creativity
Evolutionary Computing
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Scope: Search and optimization Planning (e.g. routing, scheduling and packing
) Design (e.g. signal processing) Simulation, identification, control (e.g. general
plant control) Classification (e.g. machine learning, pattern
recognition and classification)
Evolutionary Computing
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Systems based on the collective behavior of social organisms
Two main approaches: Systems based on the collective behavior of
social insects Ant Colony Optimization (ACO) Ant Clustering Algorithm (ACA)
Systems based on sociocognition Particle Swarm Optimization (PSO)
Swarm Intelligence
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An inspiration
Swarm Intelligence
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Swarm Intelligence An ant farm
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An ant farm
Swarm Intelligence
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Another inspiration
Swarm Intelligence
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Robotic autonomous navigation
Swarm Intelligence
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Why swarm intelligence? Again, a multi-agent approach may allow for a
better exploration and exploitation of the space
Simple agents together can perform complicated tasks
It may be easier and cheaper to have many simple agents than a single complex one
Swarm Intelligence
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Scope: Search and optimization:
Discrete and continuous optimization Data analysis (clustering) Robotics (autonomous navigation)
Swarm Intelligence
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Immunocomputing
Inspiration
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Design principles: Representation Architecture Affinity/Fitness functions Dynamics/Metadynamics
Immunocomputing
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Representation Set of coordinates: m = m1, m2, ..., mL, m
SL L Ab = Ab1, Ab2, ..., AbL,
Ag = Ag1, Ag2, ..., AgL Some Types of Shape Space
Hamming Euclidean Manhattan Symbolic
Immunocomputing
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Immunocomputing
Affinities: related to distance/similarity Examples of affinity measures
Euclidean
Manhattan
Hamming
L
iii AgAbD
1
2)(
L
iii AgAbD
1
L
i
ii AgAbD
1 otherwise0
if1δwhereδ,
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Immunocomputing
Algorithms and Processes Generic algorithms based on specific
immune principles, processes or theoretical models
Main Types Bone marrow algorithms Thymus algorithms Clonal selection algorithms Immune network models
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Exemple of application:
Immunocomputing
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Another example of application:
Immunocomputing
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Why immunocomputing? Adaptability Robustness Distributivity Decentralization Fault detection and tolerance Self/Nonself discrimination* ...
Immunocomputing
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Scope: Pattern recognition Fault and anomaly detection, and the security
of information systems Data analysis (knowledge discovery in
databases, clustering, etc.) Agent-based systems Scheduling Machine-learning Autonomous navigation and control Search and optimization problems Artificial life
Immunocomputing
Part III-B
Artificial Life and Fractal GeometryArtificial Life and Fractal Geometry
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Main Ideas
Biosciences: reductionist approach to understanding life
Artificial Life & Fractal Geometry: bottom-up approach to synthesize life patterns and behaviors
Focus on the computational synthesis of natural patterns and behaviors, not problem solving
Widely used in computer graphics and movie making
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Artificial Life
What is life? “The property or quality that distinguishes
living organisms from dead organisms and inanimate matter, manifested in functions such as metabolism, growth, reproduction, and response to stimuli or adaptation to the environment originating from within the organism.” (Dictionary.com)
Are mules alive?
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Some poetical definitions of life “Life is a long process of getting tired”
(Samuel Butler) “Life is a tale told by an idiot - full of sound
and fury, signifying nothing” (Shakespeare)
Artificial Life
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Artificial Life: “Artificial Life is the study of man-made systems
that exhibit behaviors characteristic of natural living systems. It complements the traditional biological sciences concerned with the analysis of living organisms by attempting to synthesize life-like behaviors within computers and other artificial media. By extending the empirical foundation upon which biology is based beyond the carbon-chain life that has evolved on Earth, Artificial Life can contribute to theoretical biology by locating life-as-we-know-it within the larger picture of life-as-it-could-be.” (Chris Langton)
Artificial Life
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“Artificial Life (AL) is the enterprise of understanding biology by constructing biological phenomena out of artificial components, rather than breaking natural life forms down into their component parts. It is the synthetic rather than the reductionist approach.” (Ray, 1994)
Artificial Life
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“Alife is a constructive endeavor: Some researchers aim at evolving patterns in a computer; some seek to elicit social behaviors in real-world robots; others wish to study life-related phenomena in a more controllable setting, while still others are interested in the synthesis of novel lifelike systems in chemical, electronic, mechanical, and other artificial media. Alife is an experimental discipline, fundamentally consisting of the observation of run-time behaviors, those complex interactions generated when populations of man-made, artificial creatures are immersed in real or simulated environments.” (Ronald et al., 1999)
Artificial Life
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Artificial Life
Natural Life: An instance
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Boids: Simple Behavioral Rules Collision avoidance and separation Velocity match and alignment Flock centering or cohesion
Artificial Life
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Boids
Artificial Life
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AIBO ERS 210
Artificial Life
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Artificial Life
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Wasp Nest Building
Artificial Life
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Creatures: Adaptive learning through interaction
Artificial Life
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Artificial fishes: Predator behavior
Artificial Life
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Traffic simulation: What is needed for a jam?
Artificial Life
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Life-as-it-is x life-as-it-could-be
Artificial Life
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Why Artificial Life? Increases our understanding of life Provides new perspectives about ‘life’ and its
many models Development of new technologies: softwares,
robotics, interactive games, computer graphics, educational systems, behavior animation tools
...
Artificial Life
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Fractal Geometry
“Why is geometry often described as ‘cold’ and ‘dry’? One reason lies in its inability to describe the shape of a cloud, a mountain, a coastline, or a tree. Clouds are not spheres, mountains are not cones, coastlines are not circles, and bark is not smooth, nor does lightning travel in a straight line. … The existence of these patterns challenges us to study those forms that Euclid leaves aside as being ‘formless’, to investigate the morphology of the ‘amorphous’.” (Mandelbrot, 1983; p. 1)
A major breakthrough in the process of modeling and synthesizing natural patterns and structures was the recognition that nature is fractal and the development of fractal geometry
Fractal geometry is the geometry of nature with all its irregular, fragmented and complex structures
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Fractal Geometry
Some Tools: Cellular automata Iterated function systems Lindenmayer systems Brownian motion Particle systems Evolutionary design etc.
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Cellular automata Dynamical system that is discrete in both space
and time Prototypical models for complex systems and
processes consisting of a large number of identical, simple, locally interacting components
Formal description: C = (S,s0,G,d,f), S is a finite set of states, s0 S are the initial states of the CA, G is the cellular neighborhood, d Z+ is the dimension of C, and f is the local cellular interaction rule, also referred to
as the transition function or transition rule.
Fractal Geometry
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Cellular automata
Fractal Geometry
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Lindenmayer Systems A formalism to simulate the development of
multicellular organisms A string or word OL-system is defined as the
ordered triplet G = V,,P, where V is the alphabet of the system, V+ is a nonempty word called the axiom, and P V V* is a finite set of productions
The geometric interpretation of the words generated by an L-system can be used to generate schematic images of diverse natural patterns
Fractal Geometry
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Lindenmayer Systems (without rendering)
Fractal Geometry
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Lindenmayer Systems (with rendering)
Fractal Geometry
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Fractal Geometry
A Natural Fern
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(Random) Iterated Function Systems An iterated function system (IFS) consists of a
complete metric space (X,d) together with a finite set of contraction mappings wn : X X, with respective contractivity factors sn, n = 1,2,…N.
Let {X; w1, w2,…, wN} be an IFS, where a probability pi > 0 has been assigned to each wi, i = 1,…,N, i pi = 1
Choose a point x X and then choose recursively and independently a new point x obtained by applying only one of the transformations, chosen according to a given probability, to the current point x
Fractal Geometry
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A Fern Generated with a RIFS
Fractal Geometry
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Brownian Motion To model some natural sceneries, it is
necessary to have curves that look different when magnified but still possess the same characteristic impression
The term fractional Brownian motion (fBm) was introduced to refer to a family of Gaussian random functions capable of providing useful models of various natural time series
Fractal Geometry
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Fractional Brownian Motion (without rendering)
Fractal Geometry
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Fractional Brownian Motion (with rendering)
Fractal Geometry
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Particle Systems Modeling physical phenomena like the flowing,
dripping and pouring of liquids, the liquid mixing with other substances, gases in motion, explosions, clouds, fireworks, etc.
A particle system consists of a collection of particles (objects) with various properties and some behavioral rules they must obey
The precise definition of these properties and laws depends on what is intended to be modeled
Fractal Geometry
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Fractal Geometry
Particle Systems See
http://www.cs.wpi.edu/~matt/courses/cs563/talks/psys.html
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Why fractal geometry? A computationally cheap way of generating
computer models of nature Study natural patterns: extinct vegetation,
design new variety of plants, study growth and developmental processes, aid farmers and decorators, crop prediction, computer graphics and movie making, etc.
Fractal Geometry
Part III-C
Computing with New Natural Computing with New Natural MaterialMaterial
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If current computing technology will reach its limit in the near future, what would be the alternative material with which to compute?
New computing methods based on other natural material than silicon: Molecules Membranes Quantum elements
Computing with Natural Material
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Computing with Natural Material
DNA Computing
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Quantum Computing Quantum bit: |x = c1|0 + c2|1
Computing with Natural Material
Part IV
Computing in the New MillenniumComputing in the New Millennium
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Some ideas that form the basis of natural computing:
Capacity of dealing with complex problems The use of sets of candidate solutions Capacity of dealing imprecisely with imprecise
information Robustness Distributivity Self-repair etc.
Computing in the New Millennium
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Computing in the New Millennium
From singularity to plurality
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The importance of nature has never been so great!
Computing in the New Millennium
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Main Reference
Fundamentals of Natural Computing, Concepts, Algorithms, and Applications; by Leandro de Castro, CRC Press, 2006
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How far can we go?
Questions, comments?
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