26. Adedeji Generic Concept

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2nd Annual Civil Engineering Conference, University of Ilorin, Nigeria, 26 28 July 2010

International Conference on Sustainable Urban Water Supply in Developing Countries 196

Forget it: Generic Concept and Application in Engineering

Optimal Design

Adeola A. A

Department of Civil Engineering, University of Ilorin, PMB 1515, Ilorin, Nigeria

[email protected];[email protected]

Abstract

Many efforts have been made to use genetic algorithms to solve symbolic regression problems by generating

symbolic functions to model data. One of the problems that plagues most of the efforts is finding a way to

efficiently mutate and cross-breed symbolic expressions so that the resulting expressions have a valid

mathematical syntax. The problem with this approach is that if limited mutations are used, the evolution process

is hindered, and it may take a large number of generations to find a solution, or it may be completely unable to

find the optimal solution. This work has suggested that during mutation either all functions and terminals are

removed beneath an arbitrarily determined node and a new branch is randomly created, or a single node is

swapped for another, by the application of Forget it and key in permanently for the material to forget all that

it has learnt to that time, so that it can be reactivated any time the analytical bank is stimulated. By mimicking

the forgetter mechanism, in a moment of intense pain (force), the action of the analytical mind (approach) is

suspended and the mental image (virtual) pictures of the experience (engram) are then recorded in the reactivemind (reaction). When the painful incident (collapse/failure) is over, the analytical mind resumes recording.

The reactive mind however begins recording again if the person (mater) undergoes another painful experience

(failure) and so it goes. Reactive mind is never selective, It faithfully records everything during a moment of

pain and it could be regarded as a survivor mechanism by restimulating the engram . The results of this work

showed that

Keywords: mutation, accelerated aging forgetter mechanism, engram

1. Introduction and ConceptForgetfulness (retention loss), and not

thoughtlessness, refers to apparent loss of

information already encoded and stored in anindividual's long term memory. It is a spontaneous

or gradual process in which old memories are

unable to be recalled from memory storage. It is

subject to delicately balanced optimization that

ensures that relevant memories are recalled.

Forgetting can be reduced by repetition and/or

more elaborate cognitive processing of information.

Reviewing information in ways that involve active

retrieval seems to slow the rate of forgetting.

Forgetting functions (amount remembered as a

function of time since an event was first

experienced) have been extensively analyzed. The

most recent evidence suggests that a powerfunction provides the closest mathematical fit to the

forgetting function. German psychologist Hermann

Ebbinghaus (en.wikipedia.org), who once studied

the mechanisms of forgetting, used himself as the

sole subject in his experiment; he memorized two

consonants and one vowel in the middle. He then

measured his own capacity to re-learn a given list

of words after a variety of given time period. He

found that forgetting occurs in a systematic

manner, beginning rapidly and then leveling off

just as in the experience curve effect that can on

occasion come to an abrupt stop. From this simple

study, basic premises have held true today and have

been reaffirmed by more methodologically sound

methods. The unusual practice, for the recovery of

a patient which depends on the life units freed from

his/her reactive bank (the sum total of pictures that

contain charge, harmful energy or force), is toforget such things, i.e. to forget as soon as

possible to have a complete healing. For instance

when some files or documents are deleted (to be

forgotten) in a computer, they are located in the

recycle (regenerate or reuse) bin and from where

the files are again deleted, but to where? What have

been deleted from the recycle bin would not go into

the swine but will be sitting down in the computer

engram

\In order to use genetic algorithms to solve

symbolic regression problems that is, to generate

symbolic functions to model data, one approach is

to perform a mutation, check the result and then trya different random mutation until a syntactically

valid expression is generated. Obviously, this can

be a time consuming process for complex

expressions. A second approach is to limit what

type of mutations can be performed for example,

only exchanging complete sub-expressions. During

mutation either all functions and terminals are

removed beneath an arbitrarily determined node

and a new branch is randomly created, or a single

node is swapped for another, it is then necessary to

apply Forget it and key in permanently for the

material to forget all that it has learnt to that time,


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so that it can be reactivated any time it is stimulated

to remember what happened at the last time.

2. Theories of ForgetfulnessThe five main theories of forgetting apparent in the

study of psychology as follows (Wikipedia 2009,Underwood 1957, Wixted 2004): Cue-dependent

forgettingor retrieval failure is the failure to recall

a memory due to missing stimuli or cues that were

present at the time the memory was encoded. It

states that a memory is sometimes temporarily

forgotten purely because it cannot be retrieved, but

the proper cue can bring it to mind. A good

example for this is searching for a book in a library

without the reference number, title, author or even

subject. The information still exists, but without

these cues retrieval is unlikely. Furthermore, a

good retrieval cue must be consistent with the

original encoding of the information. It is alsoappropriate to have another direction of cue for a

similar topical book, by checking directly at the

library shelf, by going through the stacked books.

Along the line the same book may be obtained, by

chance, from the shelf or a similar book can also be

obtained; Trace decay is when the memory isphysically no longer present. Forgetting that occurs

through physiological damage or dilapidation to the

brain is referred to as organic causes of forgetting.

These theories encompass the loss of information

already retained in long term memory or the

inability to encode new information again.

Examples include Alzheimer's, Amnesia,Dementia, consolidation theory and the gradual

slowing down of the central nervous system due to

presumed aging;Interference theoryis the idea that

forgetting occurs because the recall of certain items

interferes with the recall of other items. In nature,the interfering items are said to originate from an

over stimulating environment. Interference theory

exists in two branches, Retroactive (when new

information/memories interfere with older

information) and Proactive (when old informationinterferes with the retrieval of new information)

inhibition each referring in contrast to the other;

Decay theory states that when something new islearned, a neuro-chemical, physical "memory trace"

is formed in the brain and over time this trace tends

to disintegrate, unless it is occasionally used, State-dependent cues are governed by the state of mind

and being at the time of encoding. The emotional or

mental state of the person, such as being inebriated,

drugged, upset, anxious, happy, or in love, are the

key cues. State-dependent learning or a state

dependent memory is an idea of learning andrecalling that is based upon the physiological and

mental state of the organism. Factors affecting

state-dependent learning may include:

environment, intoxication, emotional state, and

sensory modality. In neuro-psycho-pharmacology,

state-dependent learning denotes the fact that

information that has been learned while the animal

is under the influence of a certain state of drug,

which can only be recalled and used to solve a task

when the animal is in the same state in which the

information was learned, but not in a differentstate; and Context-dependent cues are dependent

and based on the environment and situation.

Memory retrieval can be facilitated or triggered by

replication of the context in which the memory was

encoded. Such conditions include weather,

company, location, smelling of a particular odour,

hearing a certain song, even taste can sometimes

act as a cue. For example, students sometimes fail

to recall diligently studied material when an

examination room's environmental differs

significantly from the room or place where learning

took place. To improve learning and recall, it is

recommended that students should study underconditions that closely resemble an examination. A

recently identified type of context-dependent

learning is the effect of language. The linguistic

context of a memory may be encoded during

learning. Psychologists that have researched

context dependent recall include Abernathy (1940),as well as Godden & Baddeley (1975).

3. "Cosmic Forgetfulness" in Relation with theExperience curve Effects

Many think of the Big Bang as the "fireball" that

triggered the immensely hot, dense state roughly 14billion years ago to expand into the vast cosmos we

see today. The loss of the singularity, however,

opens up the possibility that the Universe could

have had a state that extended back in time before

the Big Bang. This would mean that the Big Bangdid not mark the beginning of the universe, but was

rather a transition or a "bounce" of the universe

from a prior collapsing state to our familiar

expanding one. But the indication is that

forgetfulness is an attribute to human and as such itis worthwhile to note its importance in life and

mimic the same attribute in optimisation design.

3.1 Experience curve effects

Models of the learning curve effect and the closelyrelated experience curve effect express the

relationship between equations for experience and

efficiency or between efficiency gains and

investment in the effort. The experience of

"learning curves" was first observed by the 19th

Century German psychologist HermannEbbinghaus according to the difficulty of

memorizing varying numbers of verbal stimuli.

The rule used for representing the learning

curve effect states that the more times a task has


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been performed, the less time will be required on

each subsequent iteration. This relationship was

probably first quantified in 1936 at Wright-

Patterson Air Force Base in the United States,

where it was determined that every time total

aircraft production doubled, the required labour

time decreased by 10 to 30 percent, but in mostcases the percentage is constant: It did not vary at

different scales of operation. Learning curve theory

states that as the quantity of items produced

doubles, costs decrease at a predictable rate. This

predictable rate is described by Equations 1. This

equation describes the basis for the cumulative

average or cum average curve. In this equation, Y

represents the average cost of different quantities

(X) of units. The significance of the "cum" in cum

average is that the average costs are computed for

X cumulative units. Therefore, the total cost for X

units is the product of X times the cum average

cost. For example, to compute the total costs of

units 1 to 200, an analyst could compute the

cumulative average cost of unit 200 and multiply

this value by 200.

(1)In general, the experience curve effect states that

the more often a task is performed; the lower will

be the cost of doing it. In Figure 1 shows

competitive cost dynamics: the experience curve

(Hax and Majluf 1982).

Figure 1. Experience curve (Wikipedia, 2009)

The curve is plotted with cumulative unitsproduced on the horizontal axis and unit cost on the

vertical axis. A curve that depicts a 15% cost

reduction for every doubling of output is an 85%

experience curve, indicating that unit costs drop to

85% of their original level. Mathematically theexperience curve is described by a power law

function sometimes referred to as Henderson's

Law:

(2)

Where, is the cost of the first unit of production,

is the cost of the nth unit of production, isthe cumulative volume of production and is the

elasticity of cost with regard to output The

equations for these effects come from the

usefulness of mathematical models for certain

somewhat predictable aspects of those generally

non-deterministic processes. They include: Labour

efficiency; standardization, specialization, and

methods improvements; technology-driven

learning; better use of equipment; changes in the

resource mix; product redesign; network-building

and use-cost reductions; shared experience.

3.2 Experience curve discontinuities

It is worthwhile to note that the experience curve

effect can on occasion come to an abrupt stop.

Graphically, the curve is truncated. Existing

processes become obsolete and the firm mustupgrade to remain competitive. The upgrade will

mean the old experience curve will be replaced by

a new one.

Bojowald (2008) has explored whether we

might be able to glimpse such a pre-Big Bang

universe. He began with a model based on loop

quantum gravity (LQG), which assumes that time

proceeds in finite quantum "jumps", that theuniverse's state is defined by a few parameters,

including how it is currently expanding, the amount

of matter present and the strength of gravity. He

was able to find equations of the state of the

universe that were exactly solvable at the time of

the Big Bang.

Living in the post-Big Bang era, we enjoy a

fairly smooth space-time. But before the Big Bang,

if such a time existed, there is the possibility thatthe universe was in a highly-fluctuating quantum

state in which even the usual concept of time might

have little meaning. Bojowald has found that the

sheer size of our present universe gives rise to a


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fundamental uncertainty in his equations that

prevents us from ever learning how big quantum

fluctuations before the Big Bang were.

This means that we may not, for example,

perform backwards calculations to trace back all

aspects of the universe prior to the Big Bang what

he calls "cosmic forgetfulness". The fact that someproperties cannot be predicted completely was very

unexpected, he confirmed. Nevertheless, Bojowald

added that aspects associated with classical

behaviour, such as the universe's size and

contraction rate, could in principle be determined

because they existed before the so-called Big-bang.

The fact remains that the said attributes are there in

an engram, though forgotten.

Applying this to forgetting design and using the

car drivers theory, the initial learning state, which

may not be known or remembered, was a state of

high-fluctutating quatum state and as an optimum

design time of a structure and as the time we enjoya fairly smooth space-time of the structure. The

structure remains in that state as long as its service

lasts or the time of its big-bang total

transformation (recycled).

As reported by Cartwright (2007), John Barrett,

a quantum-gravity theorist from the University ofNottingham in the UK, warns that LQG is not

widely-adopted among theorists, which could put

Bojowald's conclusions on shaky ground. "LQG is

a partially-baked cake," he said. "There are some

aspects one would need to make a complete

quantum theory of gravity that just aren't there yet."

4. Forgetter Mechanism

Forget it is one of a class of phrases of the

forgetter mechanism which is most sever in its

abberative effect, whereby denying the dataentirely to the analyser. The unusual practice, for

the recovery of a patient which depends on the life

units freed from his/her reactive bank, is to forget

such things, i.e. to forget as soon as possible to

have a complete healing. This, according toDianetics (the modern science of mental health),

does not work at all. Dianetics claim that anything

forgotten is a festering sore even when it hasdespair connected with it. According to Hubbard

(1992), an auditor (the listener) will find that every

time he locates that arch-denier or forget it, whichhas been suppressed, will be sitting down as a

somatic or a forget it in the contents of the

engram (a mental image picture which is a

recording of an experience containing pains,

uncousciousness and a real or fancied threat to

survival and it is found in the reactive mind,defined as portion of persons mind working on

stimulus-response basis under a volitional control

ie sum total of pictures containing charge, force or

energy). For instance when some files or

documents are deleted (to be forgotten) in a

computer, they are located in the recycle

(regenerate or reuse) bin and from where the files

are again deleted, but to where? What have been

deleted from the recycle bin would not go into the

swine but will be sitting down in the computer

engram. Forget it indicates that when a thinghas been put out of mind it has been put straight

into the reactive mind.

It is appropriate to note that when an analytical

mind (analyzer), which thinks, observes data,

remember it and resolves problems, is shot down or

suspended, the reactive mind starts to record. These

actions are not memories as such but engrams.

During a moment of intense pain, the action of the

analytical mind is suspended and the mental image

pictures of the experience are then recorded in the

reactive mind (bank). When the painful incident is

over, the analytical mind resumes recording. The

reactive mind however begins recording again ifthe person undergoes another painful experience

and so it goes. Reactive mind is never selective, It

faithfully records everything during a moment of

pain and it could be regarded as a survivor

mechanism by restimulating the engram. All it

takes to restimulate an engram during moments ofpain or fatigue is something in the current

environmemnt that appropriates the perceptions

stored in the engram words, sounds etc.

In such situations, the engram has the power of

command over the individual actions, body and

purposes, where a person acts in a certain manner

and not knowing what he is doing. Dianeticsdiscovered the source of mans irrationalities,

neuroses, pains etc and through auditing these

engrams could be found and eliminate (to

completely forget) their effects by erasing the

charge connected to them. The experience can berefilled in a standard memory bank of the analytical

mind.

In the case of man, Thetan or Higher Being

(Hubbard, 2008), senior to both body and mind is

supposed to erase engrams charge.

5. Forgetting Theory and Engineering DesignIn the design of an engineering element, there is the

need to adapt a material to its environment against

all odds. In the application of GAs, for instance, inan instance of the optimal design, the pseudocode

(canonical GA) will involve: A choice of initial

population; evaluating individuals fitness by

selecting individuals to reproduce, mate pairs

(parents) at random, apply crossover operator,

apply mutation operator and evaluate individualsfitness until the termination of the condition.

Because a child (offspring), who thinks others are

bad to him, was told by his mother that those guys

didnt really mean to be bad to him, They have


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good heart really, she assured her child. Because

the child wanted to be alright, he has to believe his

mother saying that: I love you very much and

dont worry honey, forget it now. The phrases

contain in his engram, a sympathy engram. When

this is encountered, it is discovered to have been

buried either in alignment with a purpose or it hasthe so called forgetter mechanism on it. The former

indicates a self-protection of the mind to give up

the engram only when enough tension is taken off

the case so the mind gets along with the engram or

by the latter, a repeater technique will begin to

release the phrase from various engrams and begin

to show up incidents. Hence, this remains utterly

out of sight (remember the files deleted to recycle

bin is only out of use, but it is there active) until the

case was almost finished and as soon as it is

contacted, the disintensified reactive bank is

collapsed.

In the second analysis, the GA pseudocode, forinstance, did not consider an offspring, a child who

could get an uncommon sympathy engram as an

incident that occurred when the child had been

badly hurt in an accident and remained in coma for

several days. He has never learnt that such an

accident could happen to him. In a forgettingtheory, a state-dependent learning or a state

dependent memory is an idea of learning and

recalling that which is based upon the physiological

and mental state of the organism. Factors affecting

state-dependent learning may include:

environment, intoxication, emotional state, and

sensory modality. Applying this to materialinvolves no deterministic process. A process where

debatable yet popular concept is "trace decay" can

occur in both short and long-term memory. This

theory, applicable mostly to short-term memory, is

supposedly contradicted by the fact that one is ableto ride a bike or drive a car even after not having

done so for decades. The fact is that, the bike rider

or a car driver has learnt riding or driving in a very

short-term that keeps him riding or driving, though

the learning ended (forgotten) at a time he couldride or drive alone without an aid. As in neuro-

psycho-pharmacology, a state-dependent learning

denotes the fact that information that has beenlearned while the animal is under the influence of a

certain drug (state) can only be recalled and used

to solve a task when the animal is in the same statein which the information was learned, but not in a

different state. In other words, material design will

be in the same state through out its life time and

beyond from the short term memory at the state

when the material forgets to remember any form

of failure. This will be made easy when applyingthe Simplified Protocol for Accelerated Aging in

data collection.

6. Data Collection

6.1 Simplified protocol for accelerated aging of

engineering materials

Researchers had located the genes for telomerase, a

protein that might help cells live longer. Now,related research from the University of Texas and

the Geron Corporation had confirmed that the

presence of telomerase actually does make ordinary

cells live longer. The telomerase is a cap of

repeating genes at the tip of the chromosome.

Every time the cell divides, the chromosome is

duplicated and its telomerase get shorter.

Logically, telomerase gets shorter perhaps it has

forgotten its original size and when it has reached

null (unless there is the limit to shortening of the

size), the cell may not be able to divide further and

the cell remains for as long as it reaches state of

physical demolition (Mahomed et al. 2009).Dyskeratosis congenita (DC) is characterized by

multiple features including mucocutaneous

abnormalities, bone marrow failure and an

increased predisposition to cancer. It exhibits

marked clinical and genetic heterogeneity. DKC1

encoding dyskerin, a component of H/ACA smallnucleolar ribonucleoprotein (snoRNP) particles is

mutated in X-linked recessive DC. Telomerase

RNA component (TERC), the RNA component and

TERT the enzymatic component of telomerase, are

mutated in autosomal dominant DC, suggesting

that DC is primarily a disease of defective telomere

maintenance. The gene(s) involved in autosomalrecessive DC remains elusive.

Walne et al (2007) showed that NOP10, a

component of H/ACA snoRNP complexes

including telomerase is mutated in a large

consanguineous family with classical DC. Affectedhomozygous individuals have significant telomere

shortening and reduced TERC levels. While a

reduction of TERC levels is not a universal feature

of DC, it can be brought about through a reduction

of NOP10 transcripts, as demonstrated by siRNAinterference studies. A similar reduction in TERC

levels is also seen when the mutant NOP10 is

expressed in HeLa cells. These findings identify thegenetic basis of one subtype of AR-DC being due

to the first documented mutations in NOP10. This

further strengthens the model that defectivetelomere maintenance is the primary pathology in

DC and substantiates the evidence in humans for

the involvement of NOP10 in the telomerase

complex and telomere maintenance (Vulliamy et al,

2001) Increasing the Life Expectancy of Human

Cells by Altering DNA.Scientists in the United States have been able to

make human cells live longer by altering their

genetic material. The research may offer renewed


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hope for age-related illnesses and the fight against

cancer.

According to the results, when chromosomes

divide they go through a process called DNA

replication. The double helix separates and

unwinds. When the double helix has completely

separated new nucleotides complete the basepairings. Then the new phosphate backbones are

made. In the end the two double helixes are

identical to the original. This happens every time

that a cell divides. Most cells in the body grow and

divide to form organs and tissues but cells can

divide only a limited number of times before they

stop. Now scientists have found out it has to do

with their chromosomes, the long threads of DNA

at the centre of each cell. The ends of each

chromosomes are capped to protect them by

structures called telomeres. Each time the cell

divides the telomeres gets shorter until the cell can

divide no more. Scientists at the University ofTexas have discovered that making the telomeres

bigger again encourages cells to divide and grow

once more. The American team stresses that it has

not discovered the key to everlasting life but says

the discovery may help in combating certain

diseases such as cancer where cells divideuncontrollably. British experts said the discovery is

important but add a note of caution.(123HelpMe,

2010).

The introduction of new or modified

engineering material products requires the

assurance that their life span can be an extended

period without any decrease in performance thatmay affect safety and efficacy under live and dead

loads. Because full-period, ambient-aged samples

usually do not exist for such products, it is

generally necessary to, aside the conduction of

accelerated-aging tests, analyse the effect of agingto support a performance until the full-period life

span is obtained. The ability of product designers to

accurately predict changes in properties of

engineering is of critical importance to the

construction industry. Modeling the kinetics ofsuch materials deterioration is difficult and

complex, and the difficulty is compounded by the

fact that a single-rate expression of degradation or achange developed over the short term may not be

valid over the long-term service life of the product

or material being studied. In order to design a testplan that accurately models the time-correlated

degradation of the materials, it is necessary to

possess an in-depth knowledge of the material

composition and structure, end-product use,

assembly and sterilization process effects, failure-

mode mechanisms, and unused-state conditions.A given engineering material may have many

functional chemical groups organized in diverse

ways (crystalline, glass, amorphous, etc.), along

with additives such as antioxidants, inorganic

fillers, plasticizers, coloration/painting, and

production aids. It is the sum of these variations

combined with variations in product use and

storage environment that determines the

degradation chemistry. Fortunately, the majority of

these materials are constructed from a limited

number of materials that have been well-characterized over extended-use periods. A

procedure known as the Simplified Protocol for

Accelerated Aging or a "10-degree rule" was

developed around the collision theorybased

Arrhenius model. When applied to well-

characterized engineering systems over moderate

temperature ranges, the test results obtained can be

within the required degree of accuracy.

The aging of materials refers to the variation of

their properties over time, the properties of interest

being those related to safety and efficacy.

Accelerated aging can be defined as a procedure

that seeks to determine the response of a device ormaterial under normal service conditions over a

relatively long time, by subjecting the product for a

much shorter time to stresses that are more severe

or more frequently applied than normal

environmental or operational stresses. Many

accelerated-aging techniques used for thequalification testing of materials are based on the

assumption of zero-, first-, and pseudo-first-order

chemical reactions following the Arrhenius

reaction rate function. This function states that an

increase or decrease in the reaction rate at which a

chemical reaction proceeds changes according to

the following equation:

(/kTAedt

dqr == 3)

where r= the rate at which the reaction proceeds;A

= the constant for the material (frequency factor);

= apparent activation energy (eV); k= Boltzmann's

constant (0.8617 x 104 eV/K); and T = absolute

temperature. With appropriate substitutions, the

simplified expression for the 10-degree rule can bederived:

(2 10/]12[ TTCdtdqr == 4)

Where C2 indicates the effect of the rule. It shouldbe noted that the 10-degree rule provides a

conservative acceleration factor at room

temperature for activation energies less than 0.7

eV. The 10-degree rule will likely be conservative

in the prediction of service life. However, the

technique depends on numerous assumptions thatmust be verified by real-time validation testing

conducted at room temperature for the targeted

service life. A well-designed product test program

will involve the use of continued more than "room-


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temperature" aging that is always greater in age

than the age of any material product under service

load. This is especially important when using these

techniques for the qualification of critical (life-

saving) components or elements. The approach

does involve some limited risk of potential recall,

in the event that room-temperature-aged testingshows a significant deficiency following real-time-

aged testing of the product. Applying accelerated-

aging test techniques in conjunction with a

comprehensive knowledge of the materials

involved is a prudent method of doing business and

with the benefits of early product introduction thus

outweighing the minimal risk of premature material

failure.

6.2 The 10-Degree Rule" Application in

Engineering

For any engineering material products, a simplifiedapproach for accelerated aging should be based on

conducting testing at a single accelerated

temperature and then employing the rule statingthat the rate of a chemical reaction will increase by

a factor Q10 for every 10C increase in temperature.

The typical relationship selected for commonly

used ductile and elasto-plastic materials Q10 = 2 is

assumed that is, a doubling of the reaction rate

for each 10C increase in the temperature above theuse or environmental or ambient temperature. The

simplified protocol for accelerated service-life

testing is not a replacement for a more complex and

advanced accelerated-aging protocol, but it isinstead a protocol for systems known to conform to

zero, first-order Arrhenius behaviour (Miguel et al,2005), which is always assumed in tests for

spontaneous heating of such substances, and places

a question mark over the validity of such tests. This

type of conservative relationship is appropriate for

a wide range elasto-plastic materials that have been

previously characterized (Wikipedia, 2009).For any accelerated aging and ambient

temperatures selected, the relationship of oven test

time to service-life time is as follows:

(5)

where T1 = oven aging temperature, TRT = roomtemperature (ambient/ use/storage), and Q10 =

reaction-rate coefficient. As an example of the

application of this formula, what test time in a 50C

oven would be required to achieve equivalency to 5

years of ambient service life aging of a product at

22C (i.e., T1 = 50C, TRT = 22C, Q10 = 2)? Thecorrect response is as follows:

(6)

In other words, an oven test time of 38 weeks at

50C would be equivalent to 5 years at 24C

ambient temperature (i.e., 7.5 weeks/year).

7. Genetic Algorithms (GA) and SymbolicRegression (SR)

Many efforts have been made to use genetic

algorithms to solve symbolic regression problems

that is, to generate symbolic functions to model

data. One of the problems that plagues most of the

efforts is finding a way to efficiently mutate and

cross-breed symbolic expressions so that the

resulting expressions have a valid mathematical

syntax. For example, if you mutate (2*x+3) into (x

2+3*) it isnt good, because it isnt syntactically

correct. One approach to this problem is to perform

a mutation, check the result and then try a different

random mutation until a syntactically validexpression is generated. Obviously, this can be a

time consuming process for complex expressions.

A second approach is to limit what type of

mutations can be performed for example, only

exchanging complete sub-expressions. The

problem with this approach is that if limitedmutations are used, the evolution process is

hindered, and it may take a large number of

generations to find a solution, or it may be

completely unable to find the optimal solution.

In order for a population to improve from

generation to generation innovations must occur

that cause some individuals to have qualities neverbefore seen. These innovations come about from

mutation. In gene expression programming there

are several types of mutations: simple random

changes in the symbols of genes and others more

complex as to reversing the order of symbols ortransposing symbols or genes within the

chromosome. Mutation is not necessarily

beneficial; often the change results in a less fit

individual or in an unviable individual who cannot

survive. But there is a possibility that a mutationmay produce an individual with extraordinary

qualities that must survive. The operation of

evolution depends on mutations producing someindividuals with greater fitness. Through natural

selection, their offspring improve the overall

quality of the population. As described above,elitism guarantees that a genius never dies unless a

better genius is found to take its place. It is possible

to convert K-expressions (Karva expression) as

devised by Ferreira (1996) Experiments have

shown that Gene Expression Programming (GEP)

is 100 to 60,000 times faster than older geneticalgorithms. Consider a gene with three symbols in

the head and which uses binary arithmetic

operators. The tail will then have 3*(2-1)+1=4

terminal symbols. Here is an example of such a


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gene. The head is in front of the comma, and the

tail follows the comma: +-/,abcd. Ignoring the

distinction between the head and the tail, this K-

expression can be converted to this expression tree

in Figure 2:

Figure 2 Expression tree

Note that the head of the gene consisted only offunctions, but the tail provided enough terminals to

fill in the arguments for the functions. During

mutation, symbols in the head can be replaced by

either function or terminal symbols. Symbols in the

tail can be replaced only by terminals. Using the

same example K-expression shown above, assumemutation replaces the / symbol with d. Then the

K-expression is: +-d,abcd. And the expression treein Figure 3. becomes

Figure 3 Result of the expression tree

Note that this expression tree has fewer nodes than

the previous one. This illustrates an important

point: by allowing mutation to replace functions

with terminals and terminals with functions, the

size of the expression can change was well as itscontent. As a further example, assume the next

mutation changes the first symbol in the K-

expression from + to c. The K-expression

becomes: c-d,abcd. The expression tree for this is:

The tree consists of a single node which is the

variable c. Note

8. Natural Selection and FitnessThe principle of natural selection is that healthy, fit

individuals should breed and produce offspring at a

faster rate than sick, unfit individuals. Through this

selection process, each generation becomeshealthier and more fit. In order for this to take

place, there must be some characteristics of

individuals that determine fitness for the

environment, and there must be a selection

mechanism that favours the breeding of individuals

with greater fitness. In gene expression

programming, fitness is based on how well an

individual models the data. If the target variable

has continuous values, the fitness can be based on

the difference between predicted values and actual

values. For classification problems with a

categorical target variable, fitness can be measured

by the number of correct predictions. DTREG

(Koza 2009, Vereira 2006) on Gene Expression

Programming and Symbolic Regression, provides a

variety of fitness functions that you can choose

from for an analysis. Evolution stops when the

fitness of the best individual in the population

reaches some limit that is specified for the analysis

or when a specified number of generations havebeen created or a maximum execution time limit is

reached.

All of the fitness functions produce fitness scores

in the range 0.0 to 1.0 with 1.0 being ideal fitness

that is, the individual exactly fits the data. If a

function is unviable for example it takes the

square root of a negative number or divides by zero

then its fitness score is 0.0. Once the fitness has

been calculated for the individuals in the

population, roulette-wheel sampling is used to

select which individuals move on to the next

generation. Each individual is assigned a slot of a

roulette wheel, and the size of the slot isproportional to the fitness of the individual.

Unviable individuals whose fitness is 0.0 have slots

that can never be selected, so they are not

propagated to the next generation. Roulette-wheel

sampling causes individuals to be selected with a

probability proportional to their fitness, and iteliminates unviable individuals. Since individuals

are not removed from the population once they are

selected, individuals may be selected more than

once for the next generation.

8. Application of GA-FTThe application of the GA-FT using the

forgetfulness theory involves the use of the

simplified protocol for accelerated aging of

engineering materials collection of data, and the

use of cues theory as well as the procedures GA-SRpseodocode (the pseudocode does not consider an

offspring (child) who could get an uncommon

sympathy engram. During mutation in a genetic

programming (GP) (Koza,1992), there is random

changes in an individual before it is introduced intothe subsequent population. Unlike crossover,

mutation is asexual and thus only operates on one

individual. During mutation either all functions andterminals are removed beneath an arbitrarily

determined node and a new branch is randomly

created, or a single node is swapped for another), itis then necessary to apply Forget it and key in

permanently for the material to forget all that it has

learnt to that time, so that it can be reactivated any

time it is stimulated to the analytical process. A

child is born into the world. He forgets (even his

mother) his experience gained at the exact time (tbb)of his birth - his big-bang, and unless he is

informed about it he would never remember. But

the essence of his birth remains the same till his last

time in this world. It is that essence (i.e. Forget it


9/13



is used as simplified protocol for accelerated aging

and experience curve analysis, that must be keyed-

in during mutation to experience or gain its new

environment at which the body will live. Figure 4

represents a simple concept of the analysis

9. GA-FT Numerical Example andComparison

The discrete weight optimization of a 10-bar

plane truss shown in Figure5 which has been

solved (Adedeji, 2007 using a floating point GA

and solved (Shaw, 2004) GP based work to the

results generated by a previous GA and GP based

systems where the solution is reported by splittingit into three sections of the Model, View and

Controller This example will solve this problem

using GA-FT. In this example, given the initial

structure, the goal was to minimise the structuresweight by varying its shape and topology.. Forty

two shapes taken from BS5950 manual are

available and are given in SI units in Table . The

assumed data are modulus of elasticity, E=68.9 x

103 MPa, density of the material, =2770 kg/m3,

allowable stress = 172 MPa and allowable

displacement = 50.8mm. Parameters M=500,G=51, pc=0.8, pm=0.1 (pt=0.04, pf=0.04, ps=0.02)

6.1 Modelling and Problem formulationThe Model represents the underlying data of the

object, in this instance the structural analysis and

evolutionary parts of the overall system. The fitness

function rewards a lightweight truss but penalises

any structure that violates a specified constraint e.g.

maximum allowable member stress. A penalty

based approach was employed, rather than outright

rejection because the good solutions will typically

be located on the boundary between the Figure 2:

10 member planar truss. The mathematical model

of minimum weight design using available member

sizes can be expressed as in the following:

5 3 1

1 27 9

5 6 9 m

8 10

6 4 2

3 4

445 kN 445 kN

9 m 9 m

Figure 5 Plane truss system with 10 steel-bars

Fitness StatesA B C=A B C = A A= 100 (Forget it)

100 B = 10C = 100 to 10% dropand reactivate

to Forget itUnits 10

Cost(Fitness)

0

0 10 1000 10 100

0 10 100Units Product Generation

Figure 4. Product (Element) optimum values


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)7(42,...2,1)(.1

)(=

==m

i

ijjn ijALCWMin

Subject to:

(a) Compressive and tensile stresses|i| - all 0, i = 1, 2, . . .10 (8)

Where i due to load in compression and tension

(b) Thermal stressesas in Equation (3), where i due to Arrheniusreaction rate function and a modified

Arrhenius equation, that makes explicit the

temperature dependence of the pre-

exponential factor. If one allows arbitrary

temperature dependence of the prefactor, the

Arrhenius description becomes overcomplete,and the inverse problem (i.e. determining the

prefactor and activation energy fromexperimental data) becomes singular. The

modified equation is usually of the form

(9)

where T0 is a reference temperature and

allows n to be a unit-less power. Clearly theoriginal Arrhenius expression above

corresponds to n = 0. Fitted rate constants

typically lie in the range -1


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Table 3 Properties of Steel Shapes

Shape Area

(mm2)

Shape Area

(mm2)

Shape Area

(mm2

1 1045 15 2342 29 7419

2 1161 16 2477 30 8710

3 1284 17 2497 31 8968

4 1374 18 2503 32 9161

5 1535 19 2697 33 10000

6 1690 20 2723 34 10323

7 1697 21 2897 35 10903

8 1858 22 2961 36 12129

9 1890 23 3097 37 12839

10 1993 24 3206 38 14193

11 2019 25 3303 39 14774

12 2181 26 3703 40 17097

13 2239 27 4658 41 19355

14 2290 28 5142 42 21613

As the present problem is a constrainedoptimization one, it is necessary to transform it into

an unconstrained problem. Many alternatives are

possible. In this study, a transitional exteriorpenalty approach is used. The transformed model is

expressed as follows:

=

=

+++=

m

i

i

i

i

i

k

k

i

iiijn ijeeePLACWMin1

1010

1

12

1

)( )15(42,...2,1)())1()1()1((.

in which = 1 for = 0 or = 0 and P is a penaltycoefficient.

In order to compare the results properly with

those from the literature, all the computations are

done in the original units used by the other

researchers, and then converted to SI units. The use

of PseudoCode program with the relevantparameters is given in the executional steps on GA.

6.2 Genetic operators

In this work, the following GA operators are

used: Tournament selection, Whole linear

crossover: From two parents P1 and P2, threeoffsprings are generated, namely 0.5P1+0.5P2,

1.5P1-0.5P2, and -0.5P1+1.5P2. The best two of

the three offsprings are then selected.

mutation: Though It allows new genetic patterns to

be formed, thus improving the search method butccasionally, it protects some useful genetic material

loss. During the process, a rate of mutation

determines the possibility of mutating one of the

design variables. If a variable (V) is chosen to be

mutated, its value is modified as follows:

V = V + (t, bU,L V) (16)

where t is the actual generation used as infinite

value, bU and bL the upper and lower bounds for the

variable, and (t, y) is given as (Turkkan,,2003),

(t, y) = y (1 r(1 c/T)2) (17)

where r is a uniform random number between 0 and

1, T, in a fogetter mechanism is the maximum

generation between 0 and 1, and c is a parameter

determining the degree of dependency on the

generation number 1

(t, y) = y (1 r2

) (18)

Elitist strategy: In standard GA the best possible

solution is not preserved, thereby increasing the

chance of loosing the obtainable best possible

solution. Elitist strategy overcomes this problem by

copying the best member of each generation into

the next one.

6.3 Comparison of results

The result produced in this example is

compared with the bit string coded GA solutions

obtained by several researchers as shown in Table4. It is believed that the minimum weight obtained

by Cal and Thierauf (1993) and the present work is

the global minimum, therefore the best possible

solution. The solutions obtained by Galante (1996)

and Ghasemi et al. (1999) are not acceptablesolutions in a mathematical sense, as they violate

slightly the displacement constraint. It is observed

that the vertical displacement of node 2 is the

binding constraint and a maximum stress of 99

MPa, well below 172MPa, is reached in member 5.

State of stress and displacement of the structure isshown in Fig. .


12/13



Because of its simplicity and ease of coding the

floating point GA procedure described here can be

applied to a wide variety of optimization problems.

It has been shown here that it can be also used

successfully in the discrete weight optimization of

structures. Despite the large design space of

permissible solutions, the procedure converged

rapidly towards the best possible solution.

Table 4. Comparison of the optimum solution for the truss system

Note: I: Rajeev and Krishnamoorty (1992), II: Cai and Thierauf (1993), III: Coello (1994), IV: Galante (1996)

V: Ghasemi et al. (1999) with population = 100, VI: GA (floating point) Adedeji (2007), VII: Present work

all = 50.8 mm, all = 172 MPa, and max = 99 MPa (number 5, ), all = 15 Mpa (due to thermal effect)

7. ConclusionApplying accelerated-aging test techniques in

conjunction with a comprehensive knowledge of

the materials involved is a prudent method with the

benefits of early product introduction thus

outweighing the minimal risk of premature materialfailure.

The result produced in this example is

compared with the bit string coded GA solutions

obtained by several researchers. It is believed that

the minimum weight obtained by Cal and Thierauf(1993) and the present work is the global

minimum, therefore the best possible solution. The

solutions obtained by Galante (1996) and Ghasemi

et al. (1999) are not acceptable solutions in a

mathematical sense, as they violate slightly the

displacement constraint. Contrary to the floatingpoint GA procedure used in the previous woks

relating to simple GA-SR and GP, GA-FT can be

applied to a wide variety of optimization problems.

It has been shown here that it can be also used

successfully in the discrete weight optimization of

structures. Despite the large design space of

permissible solutions, the procedure convergedrapidly towards the best possible solution.

References

Abernathy, E.M. (1940). The effect of changed

environmental conditions upon the resuls of

college eaminations. Journal of Psychology, 10,

293-301.

Adedeji, A. A. (2007), Genetic (Evolutionary)

Algorithm: Introduction and its use as an

engineering Design Tool, Old Publishers and

Printing Enterprises, 1 57.

Bojowald M, (2008), What happened before the bigbang?, A public lecture in the Living Reviews

in Relativity Anniversary Lectures Series.

blog.livingreviews.org/martin-bojowald-what-

happened-before-the-big-bang/

Cal, J. and Thierauf. G. (1993), 'Discrete

optimization of structures using an improved ...Engineering Optimisation. CrossRef, 21 (1993),

293306.

linkinghub.elsevier.com/retrieve/pii/S01410296

96000764

Cartwright, J. (2007), Cosmic forgetfulness shroudstime before the Big Bang, Physics World.com,

http://physicsworld.com/cws/home, Godden, D.

R. and Baddeley, A. D. (1975), Context-

dependent memory in two natural

environments: on land and under water, British

Journal of Psychology 66 (3), 325-331 325.Available at:

www.niu.edu/user/tj0dgw1/classes/411/Godden

Baddeley1975.pdf

en.wikipedia.org/wiki/Hermann_Ebbinghaus, 2009

Ferreira, J. F. (1996), Immunoquantitative analysis

of artemisinin from Artemisia annua using

polyclonal antibodies. Phytochemistry, 41(1),97-

104.www.ncbi.nlm.nih.gov/pubmed/8588880Fer

eire

Galante, M. (1996) Genetic Algorithms as an

Approach to Optimize Real-World Trusses, IntJ. Num. Meth. Engng.,39,361-382.

Ghasemi, M.R., Hilton, E. and Wood, R. D. (1999)

Optimization of Trusses Using Genetic

Algorithms for Discrete and Continuous

variables, Engineering Computations, 3, 272-

301.

Godden & Baddeley (1975), Retrieval State-

dependent Learning Godden & Baddeley (1975)

Method Weight

(kg)2y(mm)

Shape of members

1 2 3 4 5 6 7 8 9 10

I

II

III

IV

VVI

VII

2545.4

2490.6

2534.1

2475.9

2471.02490.5

2475.0

-50.5

-50.5

-50.5

-51.1

-51.2-50.5

-48.8

42

42

41

42

4242

42

1

1

1

1

11

1

38

39

39

38

3839

38

33

32

30

32

3132

33

1

1

1

1

11

1

1

1

1

11

32

26

31

28

2828

35

37

38

38

38

3938

38

37

38

38

38

3938

42

6

1

1

1

11

1


13/13



users.ipfw.edu/lundyb/P416/falsememoryhando

ut.pdf

Hax, A. C. and Majluf, N. S. (1982). Competitive

cost dynamics: the experience curve, Wikipedia

(2009) Interfaces 12(5), 5061.

doi:10.1287/inte.12.5.50.Hubbard, L. Ron. (2008), A description ofscientology, L. Ron Hubbard Library,

www.scientology.org 33 77

Koza J. R. (2003) Gene expression programming

and symbolic regression DTREG (with

Software for predictive modelling forcasting),

www.nextag.com/Genetic-Programming-On-

the.../specs-html,netLibrary

Koza J. R. (1992), Genetic programming: On the

programming of computers by means of natural

selection, Cambridge MA: MIT Press.

Mahomed, A., Hukins, D. W. L., Kukureka, S. N.,

Shepherd, D. E. T., (2009), Viscoelastic

properties of elastomers for small jointreplacements, FMBE Proceedings 13th

International Conference on Biomedical

Engineering, ICBME 2008 36 December 2008

Singapore, 10.1007/978-3-540-92841-6_292,

ed. Chwee Teck Lim and James C. H. Goh,

www.springerlink.com/index/j875r746707r108t.pdf

Michael Purdy Johns Hopkins University , 2000

Miguel A. G, Risto M. N. and Kazuo S, (2005),

Arrhenius and non-Arrhenius behaviour during

anisotropic etching, Sensors and Materials,

17(44), 189 199

123HelpMe.com (2000) Increasing the lifeexpectancy of human cells by altering DNA,. 21

May 2010

http://www.123HelpMe.com/view.asp?id=1293

39

Rajeev, S and Krishnamoorty, C.S. (1992),

Discrete Optimization of Structures Using

Genetic Algorithms, J.S. Engng., ASCE,

118(5)1233-1250

Shaw, D., Miles, J. and Gray, A., (2004),

Organisation of the adaptive computing in

design and manufacture, Conference in April20-22, 2004, Engineers House, Clifton, Bristol,

UK., Nature. 2001 Sep 27, 413(6854), 432-435.

Underwood, B.J. (1957). Interference and

forgetting, Psychological Review Wixted, J.

(2004), Annual Review of Psychology, (55),

235269.

Vereira C. (2006), Genetic Programming: On the

programming of computers by means of natural

selection , www.dtreg.com/gep.htm

Vulliamy T, Marrone A, Goldman F, Dearlove A,

Bessler M, Mason PJ, and Dokal I (2001), The

RNA component of telomerase is mutated in

autosomal dominant dyskeratosis congenita.Department of Haematology, Division of

Investigative Science, Faculty of Medicine,

Imperial College School of Science,

Technology and Medicine, Hammersmith

Hospital, Ducane Road, London W12 ONN,

UK., Nature. 2001 Sep 27, 413(6854), 370-1373.

Walne A J, Vulliamy T, Marrone A, Beswick R,

Kirwan M, Masunari Y, Al-Qurashi FH, Aljurf

M, Dokal I. (2007), Genetic heterogeneity in

autosomal recessive dyskeratosis congenita with

one subtype due to mutations in the telomerase-

associated protein NOP10. Hum Mol Genet,16(13),1619-29.

Wixted, J. T. (2004). The psychology and

neuroscience of forgetting. Annual Review of

Psychology, 55, 235-269.

26. Adedeji Generic Concept

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