introduction to evolutionary computing (ec)

Introduction to Evolutionary Computing (EC)

Chapter 1

A.E. Eiben and J.E. Smith, Introduction to Evolutionary ComputingIntroduction

Contents (I)

What is EC? Positioning of EC and the basic EC metaphor

Historical perspective Biological inspiration:

– Darwinian evolution theory (simplified!)– Genetics (three fundamental features of

biological evolution) Particulate genes and population genetics The adaptive genetic code The Genotype/Phenotype Dichotomy


Contents (II)

Traditional variants of EC Motivation for EC What can EC do: examples of application areas Demo: evolutionary magic square solver


Er …

Nothing (if you are not in this field)?A lot (if you are in this field)?Sometimes it can be very useful (for most people).But what “times”?Maybe we should look at a broaderpicture …

What Can EvolutionaryComputing Do For You?


Evolutionary Computing is a relatively new research direction, of great importance both theoretically and especially in terms of applications viewpoint.

Although the history of evolutionary computing dates back to the 1950s and 1960s, only within the last decade have evolutionary algorithms became practicable for solving real-world problems on desktop computers.

What is Evolutionary Computing (EC)? -1


Inspiration: Mother Nature


What is EC? (2)

It is the study of computational systems which use ideas and get inspirations from natural evolution.

Evolutionary computation (EC) techniques can be used in optimisation, learning and creative design.

EC techniques do not require rich domain knowledge to use. However, it helps a lot if domain knowledge can be incorporated into EC techniques.


Evolutionary computing began by lifting ideas from biological evolutionary theory into computer science, and continues to look toward new biological research findings for inspiration.

Darwin’s principle “Survival of the fittest” and „Natural selection and genetic inheritance!” can be used as a starting point in introducing evolutionary computation.

What is EC? (3)


Natural selection process


Evolutionary Computation is the research field belonging to Artificially Intelligence.

Fogel (1995) declared Artificial Intelligence as “They solve problems, but they do not solve the problem of how to solve problems.”

In contrast, Evolutionary Computation: “provides a method for solving the problem of how to solve problems”.

What is EC? (4)


EC deals with a range of problem-solving techniques based on principles of biological evolution, such as natural selection and genetic inheritance.

EC studies basic principles of genetic algorithms (GA), evolutionary programming (EP), evolution strategy (ES), genetic programming (GP).

Other researchs: Genetic Chromodynamics, Linear Classification Systems, Codificarea Delta, Algoritmi Genetici Dezordonați

Traditional variants of EC.


Different Views of EC/EA/GA/EP

The techniques and technology that is discussed in this course can be viewed as:

An approach to computational intelligence and for soft computing

A search / optimization paradigm As an approach for machine learning As a method to simulate biological systems As a subfield of artificial life As generators for new ideas, new designs

and for music and computer art


Bo rg Vog o ns

Bio top

Art

L ife Scien ces Social Scien ces

M athem atic s Physics

Softw are Eng in eering

Neu ral Nets Evo lu tio nary Com pu ting F uzzy System s

Co m p utatio nal In telligence etc

Co m p uter Scienc e etc

Exact Scien ce s etc

Scien c e Po litics Spo rts etc

Society Ston es & Sea s etc

Earth etc

Un ivers e

You are here


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


EC as Search

Search Techniques

Backtracking HillClimbing Simulated A* EC Annealing


EC as Machine Learning

Machine Learning

Learning from Examples Reinforcement Learning Classifier Systems


EC as Randomized Algorithms

Algorithms

Randomized Algorithms

EC

Deterministic Algorithms


EVOLUTION

Environment

Individual

Fitness

Population

Chromosome

Gene

The Main Evolutionary Computing Metaphor

PROBLEM SOLVING

Problem

Candidate Solution

Quality

Set of potential solutions

Encoding of potential solutions

Part of encoding

Quality chance for seeding new solutions

Fitness chances for survival and reproduction


Brief History 1: the ancestorsDarwin, Mendel and the Modern Synthesis

1859 - publication of the ‘Origin of Species’ by Charles Darwin

– Explained evolution as due to natural selection acting on heritable variation– Very similar ideas proposed by A. R. Wallace at about the same time

1865 - Gregor Mendel presents his work on ‘Experiments in Plant Hybridisation’

– Demonstrated the particulate nature of inheritance (Darwin had assumed it was blending)

These two achievements together paved the way for the ‘Modern Synthesis’…


The Modern Synthesis (aka neo-Darwinism)

One problem that had worried Darwin was ‘regression’ of traits– Darwin assumed blending of heritable material– Hence the ‘value’ of any trait would tend to

converge in a population as evolution progressed– The resulting lack of variation in the population

would give natural selection nothing to act on, so evolution would stall

Mendel’s results went un-noticed by evolutionists for about 30 years


The Modern Synthesis (continued)

Once rediscovered, Mendel’s laws of particulate (i.e. genetic) inheritance were incorporated into Darwinian evolutionary theory

The result was the ‘modern synthetic theory of evolution’, or ‘neo-Darwinism’

– Emphasizes natural selection acting on genetic variation in populations

– Primarily mathematical in nature, modeling gene spread in populations

Population genetics Quantitative genetics

Subsequently, the physical basis of particulate inheritance was found

Discovery of DNA by James Watson and Francis Crick, 1962 Nobel Laureates in Medicine


The Emergence of Evolutionary Computing

Evolutionary ideas (using simulated evolution to solve engineering and design problems) were being applied in optimization as early as the mid 20th century, e.g. – Box (1957) Evolutionary operation: a method for increasing industrial productivity.

Applied Statistics 6, 81-101– Bremermann (1962) Optimization through evolution and recombination. In: Self-

Organizing Systems.

In the 1970’s Genetic Algorithms became a prominent research area– John Holland presented a mathematical definition of GAs, and theory explaining

their performance Holland (1975) Adaptation in Natural and Artificial Systems

– Holland was mainly interested in adaptive systems, but his student Ken De Jong catalyzed research on for GAs for optimization, formulating a test suite of problems

De Jong (1975) An Analysis of the Behavior of a Class of Genetic Adaptive Systems. PhD thesis, University of Michigan


Brief History 2: the ancestors

• 1948, Turing:proposes “genetical or evolutionary search”

• 1962, Bremermannoptimization through evolution and recombination

• 1964, Rechenberg introduces evolution strategies

• 1965, L. Fogel, Owens and Walsh introduce evolutionary programming

• 1975, Holland introduces genetic algorithms

• 1992, Kozaintroduces genetic programming


A Brief History (2) of Evolutionary Computation(cont.)

The designers of each of the EC techniques saw that their particular problems could be solved via simulated evolution.– Fogel was concerned with solving prediction

problems.– Rechenberg & Schwefel were concerned with

solving parameter optimization problems.– Holland was concerned with developing robust

adaptive systems.


A Brief History (2) of Evolutionary Computation(cont.)

Each of these researchers successfully developed appropriate ECs for their particular problems independently.

In the US, Genetic Algorithms have become the most popular EC technique due to a book by David E. Goldberg (1989) entitled, “Genetic Algorithms in Search, Optimization & Machine Learning”.

This book explained the concept of Genetic Search in such a way the a wide variety of engineers and scientist could understand and apply.


A Brief History of Evolutionary Computation(cont.)

However, a number of other books helped fuel the growing interest in EC:

– Lawrence Davis’, “Handbook of Genetic Algorithms”, (1991),– Zbigniew Michalewicz’ book (1992), “Genetic Algorithms +

Data Structures = Evolution Programs”.– John R. Koza’s “Genetic Programming” (1992)– D. B. Fogel’s 1995 book entitled, “Evolutionary

Computation: Toward a New Philosophy of Machine Intelligence”.

These books not only fueled interest in EC but they also were instrumental in bringing together the EP, ES, and GA concepts together in a way that fostered unity and an explosion of new and exciting forms of EC.


Brief History 3: 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


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


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. Competition-based selection is one of the two cornerstones of evolutionary progress.

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


Darwinian Evolution 2: Diversity drives change

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

If phenotypic traits:– Lead to higher chances of reproduction– Can be inherited

then they will tend to increase in subsequent generations, leading to new combinations of traits …


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”


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


Example with two traits


Adaptive landscape metaphor (cont’d)

•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


Natural Genetics

The information required to build a living organism is coded in the DNA of that organism.

The main role of DNA molecules is the long-term storage of information.

O moleculă de ADN conține zone numite gene Structura ADN-ului este unică nu numai pentru o

specie anume ci și pentru orice individ al oricărei specii animale sau vegetale.

La om ADN-ul conține circa 3,27 miliarde de perechi de baze (3,27 miliarde de „trepte” în helixul dublu).


Genotype vs. Phenotype

"Genotype" is an organism's full hereditary information, even if not expressed.

"Phenotype" is an organism's actual observed properties, such as morphology, development, or behavior.

Genotype (DNA inside) determines phenotype Genes are the functional units of inheritance encoding

phenotypic traits (characteristics); 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)


The 8 queens problem: representation

1 23 45 6 7 8

Genotype: a permutation of the numbers 1 - 8

Phenotype: a board configuration

Obvious mapping


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 individual’s genotype is called the Genome

Within a species, most of the genetic material is the same


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:


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


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


Fertilisation

Sperm cell from Father Egg cell from Mother

New person cell (zygote)


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


Genetic code (I)

• All proteins in life on earth are composed of sequences built from 20 different amino acids (alanină, valină, leucină, izoleucină, prolină, triptofan, fenilalanină, metionină, glicocol, serină, treonină, tirozină, asparagină, glutamină, cisteină, acid aspartic, acid glutamic, arginină, lisină, histidină (acesta din urmă constituie un aminoacid esențial pentru copiii cu vârsta sub 1 an). Dintre aceștia, 8 sunt esențiali, adică nu pot fi produși de organismul uman și trebuie aduși din exterior, prin alimentație (valina, leucina,

izoleucina, triptofanul, fenilalanina, metionina, lisina și treonina).)• DNA is built from four nucleotides in a double helix

spiral: purines A,G; pyrimidines T,C• Triplets of these form codons, each of which codes for

a specific amino acid.


Genetic code (II)

• 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 form 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 !


Genetic code (III)

The Genetic Code (DNA)

TTT Phe TCT Ser TAT Tyr TGT Cys

TTC Phe TCC Ser TAC Tyr TGC Cys

TTA Leu TCA Ser TAA STOP TGA STOP

TTG Leu TCG Ser TAG STOP TGG Trp

CTT Leu CCT Pro CAT His CGT Arg

CTC Leu CCC Pro CAC His CGC Arg

CTA Leu CCA Pro CAA Gln CGA Arg

CTG Leu CCG Pro CAG Gln CGG Arg

ATT Ile ACT Thr AAT Asn AGT Ser

ATC Ile ACC Thr AAC Asn AGC Ser

ATA Ile ACA Thr AAA Lys AGA Arg

ATG Met* ACG Thr AAG Lys AGG Arg

GTT Val GCT Ala GAT Asp GGT Gly

GTC Val GCC Ala GAC Asp GGC Gly

GTA Val GCA Ala GAA Glu GGA Gly

GTG Val GCG Ala GAG Glu GGG Gly


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!

Transcription, translation


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


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’ Hitch-Hikers Guide)

– the evolution mechanism that created the human brain (after Darwin’s Origin of Species)

Answer 1 neurocomputing Answer 2 evolutionary computing


Motivations for EC: 2 – technical view

• 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

Paralel and distributed systems evaluated on HPC or LAN


Problem types solved by EC

In terms of starting ideas and applications, Evolutionary Computing highlights two aspects:– use ideas and concepts of complex adaptive systems to solve computational problems (search, optimization)– use computers and computational models in order to model complex adaptive systems

EC developed methods are general, independent of the problem, and the criterion function

The advantage of evolutionary algorithms includes its ability to address problems for which there is no human expertise.

From the optimization point of view, the main advantage of EC techniques is that they do not have much mathematical requirements about the optimization problems. All they need is an evaluation of the objective function.


Problem type 1 : Optimisation

We have a model of our system and seek inputs that give us a specified goal

e.g. – Traveling Salesman Problem– time tables for university, call center, or hospital– design specifications, Automated DSE, etc


Optimisation example 1: University timetabling

Enormously big search space

Timetables must be good

“Good” is defined by a large number of competing criteria (students view, professors view, university management view)

Timetables must be feasible (no clashes)

Vast majority of search space is infeasible


Optimisation example 2: Satellite structure

Optimised satellite designs for NASA to maximize vibration isolation

Evolving: design structures

Fitness: vibration resistance

Evolutionary “creativity”


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


Modelling example: loan applicant creditability

British bank evolved creditability model to predict loan paying behavior of new applicants

Evolving: prediction models

Fitness: model accuracy based on historical data


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


Simulation example: evolving artificial societies

Simulating trade, economic competition, etc. to calibrate models

Use models to optimize strategies and policies

Evolutionary economy

Survival of the fittest is universal (big/small fish)


Incest prevention keeps evolution from rapid degeneration

(we knew this)

Multi-parent reproduction, makes evolution more efficient

(this does not exist on Earth)

Simulation example 2: biological interpretations

Pictu

re c

enso

red


Application of Evolutionary Computation

Evolutionary Computing has been successfully applied to a wide range of problems including:– Aircraft Design,– Routing in Communications Networks,– Tracking Windshear,– Game Playing (Checkers)– Medicine (for example in breast cancer detection)– Engineering application (including

electrical,mechanical, civil, production, aeronautical and robotics)


Application of Evolutionary Computing (cont.)

Air Traffic Control, (Automatic) Design (Space Exploration) in Computer

Architecture, Scheduling (Traveling salesman problem) Machine Learning, Expert system, Machine intelligence Pattern Recognition, Job Shop Scheduling, VLSI Circuit Layout, Strike Force Allocation (strategy problem),


Application of Evolutionary Computing (cont.)

Theme Park Tours (Disney Land/World) http://www.TouringPlans.com

Market Forecasting, Egg Price Forecasting, Design of Filters and Barriers, Data-Mining, User-Mining, Resource Allocation, Path Planning, Etc.


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



Evolutionary approach to solving this puzzle: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



Application

• 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)

introduction to evolutionary computing (ec)

Documents

evolutionary algorithms

evolutionary programmin

biological evolutionary

positioning of ec

research field

genetic inheritance

realworld problems

natural evolution