Introduction - Ankara Üniversitesi Bilgisayar Mühendisliği

Introduction - Ankara Üniversitesi Bilgisayar Mühendisliği

Introduction Chapter 1 A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing Introduction Contents Positioning of EC and the basic EC metaphor Historical perspective Biological inspiration: Darwinian evolution theory (simplified!) Genetics (simplified!) Motivation for EC What can EC do: examples of application areas Demo: evolutionary magic square solver

A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing Introduction U n iv e r s e B o rg Vogons A rt L if e S c ie n c e s etc B io t o p

S o c ie t y S to n e s & S e a s etc S c ie n c e P o liti c s S p o rts etc S o c i a l S c ie n c e s M a t h e m a ti c s

E a rth E x a c t S c ie n c e s etc P h y s ic s C o m p u te r S c ie n c e etc S o f t w a r e E n g i n e e r in g C o m p u t a t io n a l I n t e l lig e n c e etc

You are here N e u ra l N e ts E v o lu t io n a ry C o m p u tin g F u z z y S y s te m s A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing Introduction Positioning of EC EC is part of computer science EC is not part of life sciences/biology Biology delivered inspiration and terminology EC can be applied in biological research

A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing Introduction The Main Evolutionary Computing Metaphor EVOLUTION PROBLEM SOLVING Environment Problem Individual Fitness Candidate Solution Quality

Fitness chances for survival and reproduction Quality chance for seeding new solutions A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing Introduction Brief History 1: the ancestors 1948, Turing: proposes genetical or evolutionary search 1962, Bremermann

optimization through evolution and recombination 1964, Rechenberg introduces evolution strategies 1965, L. Fogel, Owens and Walsh introduce evolutionary programming 1975, Holland introduces genetic algorithms 1992, Koza introduces genetic programming A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing Introduction Brief History 2: The rise of EC 1985: first international conference (ICGA) 1990: first international conference in Europe (PPSN) 1993: first scientific EC journal (MIT Press) 1997: launch of European EC Research Network EvoNet

A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing Introduction EC in the early 21st Century 3 major EC conferences, about 10 small related ones 3 scientific core EC journals 750-1000 papers published in 2003 (estimate) EvoNet has over 150 member institutes uncountable (meaning: many) applications uncountable (meaning: ?) consultancy and R&D firms A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing Introduction Darwinian Evolution 1: Survival of the fittest

All environments have finite resources (i.e., can only support a limited number of individuals) Lifeforms have basic instinct/ lifecycles geared towards reproduction Therefore some kind of selection is inevitable Those individuals that compete for the resources most effectively have increased chance of reproduction

Note: fitness in natural evolution is a derived, secondary measure, i.e., we (humans) assign a high fitness to individuals with many offspring A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing Introduction Darwinian Evolution 2: Diversity drives change Phenotypic traits:

If phenotypic traits: Behaviour / physical differences that affect response to environment Partly determined by inheritance, partly by factors during development Unique to each individual, partly as a result of random changes Lead to higher chances of reproduction Can be inherited then they will tend to increase in subsequent

generations, leading to new combinations of traits A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing Introduction Darwinian Evolution:Summary Population consists of diverse set of individuals Combinations of traits that are better adapted tend to increase representation in population Individuals are units of selection

Variations occur through random changes yielding constant source of diversity, coupled with selection means that: Population is the unit of evolution Note the absence of guiding force A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing Introduction Adaptive landscape metaphor (Wright, 1932) Can envisage population with n traits as existing in a n+1-dimensional space (landscape) with height corresponding to fitness

Each different individual (phenotype) represents a single point on the landscape Population is therefore a cloud of points, moving on the landscape over time as it evolves - adaptation A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing Introduction Example with two traits A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing Introduction Adaptive landscape metaphor (contd) Selection pushes population up the landscape Genetic drift: random variations in feature distribution (+ or -) arising from sampling error

can cause the population melt down hills, thus crossing valleys and leaving local optima A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing Introduction Natural Genetics The information required to build a living organism is coded in the DNA of that organism Genotype (DNA inside) determines phenotype Genes phenotypic traits is a complex mapping

One gene may affect many traits (pleiotropy) Many genes may affect one trait (polygeny) Small changes in the genotype lead to small changes in the organism (e.g., height, hair colour) A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing Introduction Genes and the Genome Genes are encoded in strands of DNA called chromosomes In most cells, there are two copies of each chromosome (diploidy)

The complete genetic material in an individuals genotype is called the Genome Within a species, most of the genetic material is the same A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing Introduction Example: Homo Sapiens

Human DNA is organised into chromosomes Human body cells contains 23 pairs of chromosomes which together define the physical attributes of the individual: A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing Introduction Reproductive Cells Gametes (sperm and egg cells) contain 23 individual

chromosomes rather than 23 pairs Cells with only one copy of each chromosome are called Haploid Gametes are formed by a special form of cell splitting called meiosis During meiosis the pairs of chromosome undergo an operation called crossing-over A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing Introduction Crossing-over during meiosis Chromosome pairs align and duplicate Inner pairs link at a centromere and swap parts of themselves

Outcome is one copy of maternal/paternal chromosome plus two entirely new combinations After crossing-over one of each pair goes into each gamete A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing Introduction Fertilisation Sperm cell from Father Egg cell from Mother New person cell (zygote) A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing Introduction

After fertilisation New zygote rapidly divides etc creating many cells all with the same genetic contents Although all cells contain the same genes, depending on, for example where they are in the organism, they will behave differently This process of differential behaviour during development is called ontogenesis All of this uses, and is controlled by, the same

mechanism for decoding the genes in DNA A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing Introduction Genetic code All proteins in life on earth are composed of sequences built from 20 different amino acids DNA is built from four nucleotides in a double helix spiral: purines A,G; pyrimidines T,C Triplets of these from codons, each of which codes for a specific amino acid Much redundancy:

purines complement pyrimidines the DNA contains much rubbish 43=64 codons code for 20 amino acids genetic code = the mapping from codons to amino acids For all natural life on earth, the genetic code is the same ! A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing Introduction

Transcription, translation A central claim in molecular genetics: only one way flow Genotype Phenotype Genotype Phenotype Lamarckism (saying that acquired features can be inherited) is thus wrong! A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing Introduction Mutation

Occasionally some of the genetic material changes very slightly during this process (replication error) This means that the child might have genetic material information not inherited from either parent This can be catastrophic: offspring in not viable (most likely) neutral: new feature not influences fitness advantageous: strong new feature occurs Redundancy in the genetic code forms a good way of error checking A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing Introduction

Motivations for EC: 1 Nature has always served as a source of inspiration for engineers and scientists The best problem solver known in nature is: the (human) brain that created the wheel, New York, wars and so on (after Douglas Adams HitchHikers Guide) the evolution mechanism that created the human brain (after Darwins Origin of Species) Answer 1 neurocomputing

Answer 2 evolutionary computing A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing Introduction Motivations for EC: 2 Developing, analyzing, applying problem solving methods a.k.a. algorithms is a central theme in mathematics and computer science Time for thorough problem analysis decreases

Complexity of problems to be solved increases Consequence: Robust problem solving technology needed A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing Introduction Problem type 1 : Optimisation We have a model of our system and seek inputs that give us a specified goal e.g.

time tables for university, call center, or hospital design specifications, etc etc A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing Introduction Optimisation example 1: University timetabling Enormously big search space Timetables must be good Good is defined by a number of competing criteria Timetables must be feasible Vast majority of search space is infeasible A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing Introduction

A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing Introduction Optimisation example 2: Satellite structure Optimised satellite designs for NASA to maximize vibration isolation Evolving: design structures Fitness: vibration resistance Evolutionary creativity A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing Introduction A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing Introduction

Problem types 2: Modelling We have corresponding sets of inputs & outputs and seek model that delivers correct output for every known input Evolutionary machine learning A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing Introduction Modelling example: loan applicant creditibility British bank evolved creditability model to predict loan paying behavior of new applicants

Evolving: prediction models Fitness: model accuracy on historical data A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing Introduction Problem type 3: Simulation We have a given model and wish to know the outputs that arise under different input conditions Often used to answer what-if questions in evolving dynamic environments e.g. Evolutionary economics, Artificial Life

A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing Introduction Simulation example: evolving artificial societies Simulating trade, economic competition, etc. to calibrate models Use models to optimise strategies and policies Evolutionary economy Survival of the fittest is universal (big/small fish) A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing Introduction Simulation example 2: biological

interpetations Pi ct u re ce ns or ed Incest prevention keeps evolution from rapid degeneration (we knew this) Multi-parent reproduction, makes

evolution more efficient (this does not exist on Earth in carbon) not any more 2nd sample of Life A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing Introduction Demonstration: magic square Given a 10x10 grid with a small 3x3 square in it Problem: arrange the numbers 1-100 on the grid such that all horizontal, vertical, diagonal sums are equal (505)

a small 3x3 square forms a solution for 1-9 A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing Introduction Demonstration: magic square Evolutionary approach to solving this puzzle: Creating random begin arrangement Making N mutants of given arrangement Keeping the mutant (child) with the least error Stopping when error is zero A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing Introduction Demonstration: magic square Software by M. Herdy, TU Berlin Interesting parameters:

Step1: small mutation, slow & hits the optimum Step10: large mutation, fast & misses (jumps over optimum) Mstep: mutation step size modified on-line, fast & hits optimum Start: double-click on icon below Exit: click on TUBerlin logo (top-right) Application

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