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evolutionary
computing
This family of computational intelligence techniques is inspired by our scientific understanding of biological evolution and inheritance. However, we shall also see how evolutionary computation gains inspiration from everyday, common-sense models of evolution and the mechanisms of inheritance.
nucleus       .    
Biologists’ attention turned to a small dark blob of material lying
deep within the interior of most cells, known as the nucleus.
chromosomes
When cells were stained, certain parts of the nucleus took up more dye than others, in particular certain strand-like structures that became known as       chromosomes    
genes
individual grains, named   genes   , each responsible for some fundamental characteristic of an organism,      
deoxyribonucleic acid (DNA).
a shadowy molecule lurking amongst the protein
genome       .    
The whole of an organism’s inheritance information held in its DNA is known as its   genome   .    
junk DNA, or, more politely,
non-coding DNA.
The remainder is apparently inert
phylogenetic analysis
differences between the genomes of different species     to determine their relationships to one another    
genetic algorithm (GA).
And theories of biological evolution have had a direct influence on computing, in the invention of the   genetic algorithm   (GA).    
fitness landscape
lanscape based on fitness
bit strings.
GAs use   artificial genes strung along genomes   . But instead of controlling the production of proteins, artificial genomes are strings storing ordered pieces of information                 commonly these are strings of   bits or binary values (0 or 1   ), often just referred to as     bit strings       .    
fitness functions
Programs that evaluate the fitness of a potential solution are essential to the working of any GA.
Mutation
Mutation refers to changes made to an organism’s genes at any point in an
organism’s life. The majority of mutations are the result of the introduction of errors
when DNA is copied
meiosis
your parents’ sperm and eggs were each created in a complex process known as       meiosis    
crossover       or     recombination
corresponding pairs of their chromosomes swapped genetic material in a process known as   crossover   or     recombination       , illustrated in Figure 1.5. Matching regions of each chromosome are repeatedly exchanged between chromosomes in a more-or-less random process,   with the result that the original chromosomes are thoroughly scrambled; but because   crossover almost always exchanges corresponding regions of DNA, each new   chromosome contains all of the genes found in the parent.      
gametes
sex cells
zygote
The full complement of chromosomes will only be restored
when a sperm cell fuses with an egg cell to form a zygote, which will eventually
develop into a new individual.
fitness value
our fitness function will simply add
the values of the two dice; the result will be the fitness value for each genome
(see Table 1.5); those genomes with higher values are said to be fitter than those
with lower values. The fitter the genome, the better the solution it represents.
mating pool
The genes selected for survival will enter a mating pool from which
future generations can be bred.
biased roulette wheel
The GA ‘roulette wheel’ is a similar idea. It will have a number of slots, one for each genome in the population. And although in conventional roulette each slot has an equal chance of being selected, the GA wheel is   biased   towards slots representing genomes with fitter values. A percentage of the wheel is allocated to each of the   genomes. Those genomes with the higher fitness values have greater percentages   of the wheel. The percentages are calculated by dividing the fitness value of   each genome by the total fitnesses of all of the genomes and expressing the value as   a percentage.      
elitism
One method of overcoming this is to cheat somewhat and indulge in   elitism   , ensuring that a certain proportion of high-scoring genomes         always   enter the next generation unscathed. This policy may forsake some of the purity of the original   biological inspiration, but it has been shown to improve the performance of GAs   significantly.      
two-point crossover
in a   two-point crossover   the middle section of a chromosome may cross over and find itself sandwiched in the middle of a new gene.      
Boltzmann selection
An alternative strategy, known as   Boltzmann selection   , begins with a relatively low rate of selection, with weaker strains being given a greater chance of being selected   for future generations, thus allowing the search space to be more thoroughly covered.   As time passes, the selection rate becomes more intense, weaker genomes being   progressively eliminated from the population, and the surviving genomes finally   converging on optimal, or very near optimal, solutions.      
arithmetical crossover
A rather more subtle strategy     perhaps more suitable to problems in which the solutions are combinations of real numbers         is known as   arithmetical crossover    
single-objective

multi-objective       .    
generally there will be trade-offs between most of these features, there is not going to be any single solution combining the maxima of all of them. Rather, there will be a range of solutions, each representing a   compromise   among the various features we want to optimise:    
bit strings
As with neural networks, computing researchers are often uncomfortable with the use
of biological terminology to describe features of computational models. Just as artificial
neurons are often referred to by the more neutral word ‘units’, so artificial genomes
are often termed bit strings
Prisoner’s Dilemma
The premise of the game is extremely straightforward: two suspects (called A and B) are arrested and thrown into separate prison cells. Each is then given a choice     they can testify against the other prisoner (they are said to         defect) or they can remain silent (they cooperate   ). Each prisoner potentially has something to gain and lose from their choice. The rules are these:       c     If a prisoner defects and the other prisoner cooperates then the defector will go free and the cooperator will be imprisoned for ten years.       c     If neither prisoner defects then they both automatically receive a short sentence of six months.       c     If both prisoners defect then each receives a two-year sentence.    
Iterated Prisoner’s Dilemma
the challenge is repeated time and time again in the game
of Iterated Prisoner’s Dilemma, first suggested by Richard Axelrod in 1984. In this
version of the game each player has a memory of previous rounds, which they can
use to guide future decisions. Each player’s aim is to minimise the amount of jail time
they accumulate through the game.
game theory
Within math, game theory reflects calculated circumstances (games) where a person’s success is based upon the choices of others
Aircraft wing design
Four key forces act on a plane:   weight, lift, thrust and drag   (see Figure 1.15). Two of these work in favour of flight, and two against it. Naturally, the         weight   of a plane continually pulls it back down to earth under gravity; but         lift   is an upward force, generated by the plane’s wing, making it airborne.         Thrust   is the force created by the engines, pushing the plane forward and making lift possible through the flow of air over the   wing surfaces; but         drag   , which arises from the friction of the plane as it butts through the air, works in the opposite direction, pulling the plane backwards and slowing it   down      
Single-objective optimisation: evolving an aerofoil with maximum L/D
Akira Oyama used a GA to evolve an aerofoil in which the ratio
of lift L to drag D was a maximum.
Multi-objective optimisation: optimising L/D and weight
Using a multi-objective evolutionary algorithm, Oyama then aimed to evolve
aerofoil designs that would minimise the drag, at the same time as minimising weight.
As you learned earlier in this unit, multi-objective algorithms do not produce a single
solution, but a set of possible solutions from which a winner can be selected by hand,
based on the relative importance of the objectives.
schema
A subset of a genome in a biological organism is called a gene; the subset of a
genome in an artificial organism is called a schema
order
The order of a schema   H, written as o(H   ), is the number of fixed positions in that schema. For instance, the schema         *10**11 is order 4 and *100111   is order 6. Order is a measure of how specific that schema is; high-order schemata match   fewer strings than low-order schemata.       c        
defining length
Defining length, writtenδ (H)   , is the distance from the first to the last fixed position in the schema. The schemata         *10**11 and *100111   both have defining lengths of 5, while the schema         ****0**   has a defining length of zero. Schemata with larger defining lengths are more likely to be disrupted by crossover and mutation.
schema theorem
It shows how GAs favour the growth of schemata with high relative
fitness and low defining lengths
building blocks
schemata with high relative fitness and low defining lengths. Such schemata are known as   building blocks   .    
implicit parallelism
This ability of GAs to process many more schemata than would be apparent at first glance is termed       implicit parallelism     and is touted as the main reason why GAs are so successful at finding optimal or near-optimal solutions.
hitchhiking
The reason for this is that the various schemata interact in the population and
successfully finding one good schema can hamper attempts to find others, an effect
termed hitchhiking.
deception
A deceptive problem is one where the best building blocks (short, fit schemata) come together to form a sub-optimal solution
Markov property Markov process. Markov chain.
Markov property and a
process with the Markov property is called a Markov process. The sequence of states
through which a Markov process goes is called a Markov chain.
transition matrix
 a stochastic matrix (also termed probability matrix, transition matrix, substitution matrix, or Markov matrix) is a matrix used to describe the transitions of a Markov chain.
absorbing states
if a system enters such a state, it can never leave it. An absorbing state can be identified because its column in the transition matrix is all zeros except for a single entry of 1 on the main diagonal. If it is possible to move to an absorbing state, it is obvious that the long-term behaviour of the system is to move to that absorbing state and stay there: given enough time, the system will randomly move to an absorbing state and then be unable to move to another state. A system may have more than one absorbing state, in which case an analysis of the transition matrix and the starting distribution can determine the likelihood of any of the absorbing states being reached.
cumulants
the cumulants κn of a probability distribution are a set of quantities that provide an alternative to the moments of the distribution.
fundamental matrix
Genetic programming   (GP   )    
is a related field of research, where genomes are set up to express and evolve       computer programs      
side effects       :    
actions     like writing something to the output, reading a key press or minimising a window           that is not directly related to their return value.      
functions
a function returns a value; in order to do so it may take in
one or more arguments.
terminals:
Terminals provide specific values to the system and may be variables, constants or functions
closed open
a function should be able to handle all possible argument values: such functions are said to be   closed; those that cannot are open   . Calling an open function with an illegal argument value will cause a crash. It is not possible to close   every function. However, open functions can be replaced by functions that behave   correctly for legal arguments, but return special values when presented with illegal   ones. The program continues to run, but with incorrect values.      
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