Hidden (Tabu) Secrets of Successful Evolutionary Search Methods

gooseliverBiotechnology

Oct 22, 2013 (3 years and 9 months ago)

89 views

Hidden (Tabu) Secrets of Successful Evolutionary Search
Methods



Peng
-
Yeng Yin

National Chi Nan University, Taiwan

Fred Glover

University of Colorado, Boulder, CO, USA

2007 IEEE Congress on Evolutionary Computation

P.Y. Yin & Fred Glover




2

New Directions for Evolutionary Algorithms


We are witnessing a radical departure from the
early 1990s


Original theme for Solution Combination:
restrict attention to a handful of genetically
inspired “crossover” operations. (Basic simple
approaches suffice.)


Today

emphasize
:



New

solution

codings


New

forms

of

crossover



Exploit

problem
-
specific

knowledge


P.Y. Yin & Fred Glover




3

Limitations Still Exist!

Regarding Solution Combinations and Their Use


We still often focus on narrow metaphors for
combining solutions


Advantages

of

the

metaphors
:



Stimulate

researchers

to

create

methods

based

on

the

metaphors



Catch

the

attention

of

wide

audiences



(Easy

to

describe
.

Great

sources

of

publicity!)



Disadvantages

of

the

metaphors
:



Create

a

biased

perspective

about

preferred

forms

of

methods



Cause

valuable

alternative

strategies

to

be

overlooked


P.Y. Yin & Fred Glover




4

Remedy


An Orientation Shift


View Solution Combinations as a process of

Path

Relinking



Employ frameworks of
Adaptive Structured
Combinations



Exploit the
neighborhood concept

in generating
combinations


Integrate with
adaptive memory


P.Y. Yin & Fred Glover




5

Structured Combinations for Populations of Vectors


Property 1 (Representation Property). Each vector
represents a set of votes for particular decisions


Property 2 (Trial Solution Property). The set of votes
translates into a trial solution by a well
-
defined process


Property 3 (Update Property). If a decision is made
according to the votes of a given vector, thus producing
a residual problem requiring fewer decisions, a well
-
defined rule exists to update all vectors relative to the
residual problem so that Properties 1 and 2 continue to
hold



(Details and illustrations in F. Glover (1994) “Tabu
search for nonlinear and parametric optimization (with
links to genetic algorithms),”
Discrete Applied
Mathematics

49, 221
-
235.)

P.Y. Yin & Fred Glover




6

Path Relinking

X

A

B

C

Using A and
B as guiding
solutions

Using B and
C as guiding
solutions

Using C
as a
guiding
solution

X is the initial
solution


Valuable

information

can

be

contained

in

trajectories

from

bad

solutions

to

good

solutions

(or

from

good

solutions

to

other

good

solutions)


Path

relinking

selects

moves

that

introduce

attributes

contained

in

the

guiding

solution(s)

into

the

initial

solution
.

Basically

only

one

attribute

of

the

initial

solution

is

modified

at

each

time

a

move

is

made
.



Instead

of

interchanging

information

between

solutions

in

a

wholesale

fashion,

path

relinking

is

a

stepwise

approach

considering

neighborhood

structure

and

adaptive

memory

strategies

that

may

generate

solutions

not

accessible

by

EAs

P.Y. Yin & Fred Glover




7

Scatter Search


Useful

information

about

the

form

or

location

of

the

global

solution

is

typically

contained

in

a

sufficiently

diverse

collection

of

elite

solutions

Repeat until |
P
| =
PSize

P

Diversification Generation

Method

Subset Generation

Method

Improvement

Method

Solution Combination Method

Improvement

Method

No more new
solutions

Reference Set

Update Method

RefSet

Diversification Generation

Method

Improvement

Method

Stop if
MaxIter

reached

P.Y. Yin & Fred Glover




8

Neighborhood Construction


Candidate

list

strategy


Effectively

construct

a

neighborhood

structure

to

save

computational

time

or

even

to

construct

a

more

complex

(or

compound)

neighborhood

that

is

not

accessible

using

simple

moves
.

It

accommodates

variable

neighborhood

search

(VNS)

method



Ejection

chain



Filter

and

fan


P.Y. Yin & Fred Glover




9

Ejection Chain


Construct

neighborhood

by

combining

successive

interindependent

(component)

moves

to

form

a

single

compound

move
.



Tunnel

infeasible

region

by

successive

ejection

moves

and

transform

to

a

feasible

solution

by

a

trial

move
.



This

form

of

compound

neighborhood

structures

are

usually

not

accessible

by

feasibility
-
preserving

search

methods
.



t

tip

r

s
2

s
1

root

subroot

subroot

t

tip

r

s
2

s
1

root

subroot

subroot

Ejection move

Trial move

Traveling salesman problem

P.Y. Yin & Fred Glover




10

Filter and Fan


The neighborhood tree is explored
breadth first and is restricted by a
maximum number of levels L. Each
level is governed by the
filter

strategy
that selects a subset of moves induced
by the
fan

strategy


Filter & Fan is usually applied after a
local heuristic has been executed to
explore a larger neighborhood in
order to overcome local optimality


The two search strategies are
alternated when a new local optimum
is found until the Filter & Fan fails to
improve the current best solution


Protein

folding

problem

P.Y. Yin & Fred Glover




11

Strategic Oscillation


In many cases the elite (or globally
optimal) solution lies on the feasibility
boundary or the search method would
stop at a critical level


Strategic oscillation drives the search
toward or away from an
oscillation

boundary. The approach proceeds for a
specified depth beyond the boundary, and
turns around. The boundary again is
approached and crossed from the opposite
direction.


The oscillatory behavior is established by
generating modified evaluations and rules
of movement, depending on the regional
locality and trajectory direction


Choose oscillation pattern and change
rate to achieve an effective interplay
between intensification and
diversification


Oscillation boundary

Depth

Iterations

P.Y. Yin & Fred Glover




12

Adaptive Memory Programming


Adaptive memory programming (Tabu Search)
constitutes of adaptive memory and a set of
responsive strategies


Adaptive memory:
purposefully comparing previous
states or transactions to those currently contemplated



Recency, frequency, influence, quality


Responsive strategies: take advantage of adaptive
memory to exploit good solution features while
explore new promising regions



Tabu restriction, aspiration criteria



Intensification, diversification

P.Y. Yin & Fred Glover




13

Adaptive Memory Programming


Adaptive memory programming (Tabu Search)


Tabu restriction
: In each move iteration, the best
move (evaluated in aspects of quality, influence,
frequency) in the neighborhood is selected unless it is
tabu.
Tabu tenure

(static/dynamic/reactive) is
determined according to the adaptive memory


Aspiration criteria
: The tabu restriction can be
overruled if the corresponding move meets the
aspiration criteria such that the search can be guided
to a promising region along the course


Intensification/diversification strategies
: applying
incentives/penalties to induce attributes of good
solutions


path relinking, strategic oscillation, multi
-
start

P.Y. Yin & Fred Glover




14

Create Hybrid


Integrate EA and TS

Solution

combination


Path

relinking

exploiting

neighborhood

structure

and

adaptive

memory




directly

address

problem

context

without

metaphor

restrictions




stepwise

and

systematic,

reducing

the

probability

of

missing

valuable


information

on

the

course

Solution

improvement


Adaptive

memory

strategies

can

be

directly

applied

to

improve

the

combined

solution




accommodate

implicit

variable

neighborhood

search

that

prevents

from


local

optimality

Population

distribution

control


Tabu

search

has

provided

a

wealth

of

diversity

control

strategies




tabu

restrictions,

altering

move

evaluations,

strategic

oscillation

Constraint

handling


Strategic

oscillation

repeatedly

drives

the

search

across

the

feasibility

boundary





non
-
monotonically

change

the

mixes

of

feasible

and

infeasible


solutions

P.Y. Yin & Fred Glover




15

For Additional Background

Using

Google

search
:




“Path

Relinking”

returns

about

28
,
000

web

pages
.



“Scatter

Search”

returns

about

50
,
000

web

pages
.




The

first

references

encountered

on

Google

give

a

good

background

for

basic

understanding
.


(Incidental

remark
:

“Tabu

Search”

returns

about

530
,
000

web

pages
.
)


P.Y. Yin & Fred Glover




16


Portfolio

Management


Supply

Chain

Applications


Strategic

and

Operational

Planning


Financial

Planning


Manufacturing

Process

Flow



Resource
-
Constrained

Scheduling


Network

Planning


Routing

&

Distribution


Data

Mining


Biotechnology


Health

Care



OptTek Customized Simulation Optimization Applications

P.Y. Yin & Fred Glover




17

Metaheuristic


Based Simulation Optimization

P.Y. Yin & Fred Glover




18


Function

to

be

Optimized


Highly

Nonlinear


Nondifferentiable


Discrete or Continuous or Mixed



Function

Evaluations


Complex


Extremely

Computation

Intensive


One

second

to

One

Day

per

Evaluation!

The Optimization Challenge

P.Y. Yin & Fred Glover




19


Evolutionary

Scatter

Search


Advanced

Tabu

Search


Linear

&

Mixed

Integer

Programming


Pattern

Classification

&

Curve

Fitting


Neural

Networks


Support

Vector

Machines

&

Trees


SAT

Data

Mining


OptQuest
®

Components

P.Y. Yin & Fred Glover




20

Efficiency is Critical!

OptQuest
®

vs. RiskOptimizer

P.Y. Yin & Fred Glover




21


Given a set of opportunities and limited
resources…



…determine the best set of projects that
maximizes performance






Problem

P.Y. Yin & Fred Glover




22


Constraints
:



Budget



Resource

Availability



Scheduling

and

Sequencing

of

Projects



Project

Dependencies,

etc
.



Objectives
:


Maximize

N
et

P
resent

V
alue

(NPV)


Maximize

I
nternal

R
ate

of

R
eturn

(IRR)


Maximize

B
usiness
-
C
ase

V
alue

(BCV)







Portfolio Selection Problem

P.Y. Yin & Fred Glover




23


5

Projects
:


T
ight

G
as

P
lay

Scenario

(TGP)


O
il



W
ater

F
lood

Prospect

(OWF)


D
ependent

L
ayer

Gas

Play

Scenario

(DL)


O
il



O
ffshore

P
rospect

(OOP)


O
il



H
orizontal

W
ell

Prospect

(OHW)



Ten

year

models

that

incorporate

multiple

types

of

uncertainty




Evaluation

Time
:

1
s

/

Scenario




Application Example

P.Y. Yin & Fred Glover




24

Determine

project

participation

levels

[
0
,
1
]

that


Maximize

E(NPV)



Keep

s

<

10
,
000

M
$

(Risk

Control)


All

projects

start

in

year

1

Base Case

TGP = 0.4, OWF = 0.4, DL = 0.8, OHW = 1.0

E(NPV) = 37,393

s

=9,501

Base Case

P.Y. Yin & Fred Glover




25

Determine

project

participation

levels

[
0
,
1
]

AND


starting

times

for

each

project

that


Maximize

E(NPV)



Keep

s

<

10
,
000

M
$

(Risk

Control)


Projects

may

start

in

year

1
,

2
,

or

3

TGP
1

= 0.6, DL
1
=0.4, OHW
3
=0.2

E(NPV) = 47,455

s

=9,513 10
th

Pc.=36,096

Deferment Case

Deferment Case

P.Y. Yin & Fred Glover




26

Determine

project

participation

levels

AND


starting

times

for

each

project

that


Maximize

P(NPV

>

47
,
455

M
$
)



Keep

10
th

Percentile

of

NPV

>

36
,
096

M
$


Projects

may

start

in

year

1
,

2
,

or

3

TGP
1

= 1.0, OWF
1
=1.0, DL
1
=1.0, OHW
3
=0.2

E(NPV) = 83,972

s

=18,522
P(NPV > 47,455) =

0.99

10
th

Pc.=43,359

Probability of Success Case

Probability of Success Case

P.Y. Yin & Fred Glover




27


Cash

Flow

Control


Capital

Expenditure

Control


Reserve

Replacement

Goals


Production

Goals


Finding

Costs

Control


Dry

Hole

Expectations

Control


Reserve

Goals


Net

Profit

Goals


Extensions…

P.Y. Yin & Fred Glover




28

Treatment

Patient Arrival

Emergency Room (ER)

Approach=
optimize

current process,
redesign process and
re
-
optimize
.

Release

Admit

Joseph DeFee, CACI, Inc.

Hospital Emergency Room Process

P.Y. Yin & Fred Glover




29


Nurses


Physicians


P
atient

C
are

T
echnicians

(PCTs)


Administrative

Clerks


E
mergency

R
ooms

(ER)

ER Resources

P.Y. Yin & Fred Glover




30


Minimize

E[
T
otal

A
sset

C
ost]


Subject

to
:


E[
C
ycle

T
ime]

for

Level

1

Patients

<

2
.
4

hours


Number

of

Nurses

between

1

and

7


Number

of

Physicians

between

1

and

3


Number

of

PCTs

between

1

and

4


Number

of

Clerks

between

1

and

4


Number

of

ER

between

1

and

20

Problem

P.Y. Yin & Fred Glover




31


Set

up

OptQuest

to

run

for

100

iterations

and

5

runs

per

iteration


Each

run

simulates

5

days

of

ER

operation



Results
:


Best

solution

found

in

6

minutes



E[TAC]

=

$

25
.
2
K

(
31
%

improvement
)


E[CT]

for

P
1

=

2
.
17

hours

Solution

P.Y. Yin & Fred Glover




32

Possible

to

improve

E[CT]

for

P
1

even

further?


Arrive at

ER

Transfer to

room

Receive

treatment

Fill out

registration

OK?

Released

Admitted

Into

Hospital

Y

N

Current Process

Arrive at

ER

Transfer to

room

Receive

treatment

Fill out

registration

OK?

Released

Admitted

Into

Hospital

Y

N

Redesigned Process

Process Redesign

P.Y. Yin & Fred Glover




33


Set

up

OptQuest

to

run

for

100

iterations

and

5

runs

per

iteration


Each

run

simulates

5

days

of

ER

operation



Results
:


Best

solution

found

in

8

minutes


E[TAC]

=

$

24
.
6
K

(
new

best,

3
.
4
%

improvement
)


E[CT]

for

P
1

=

1
.
94

hours

(
12
%

improvement
)

Solution of the Redesigned Process

P.Y. Yin & Fred Glover




34



Simulation Optimization with OptQuest is able to
find high
-
quality solutions in reasonable time and
re
-
optimizes the redesigned model


These applications are
only a fraction

of the ways
that
metaheuristics and simulation

are used in
optimization involving
non
-
linearity and
uncertainty


Over 60,000 user licenses

of the system have
been sold (each licensed user might have multiple
kinds of problems)

Simulation Optimization with Metaheuristic

P.Y. Yin & Fred Glover




35


The genetic metaphors served by most EAs
today have caused
valuable alternative strategies
from human problem solving to be overlooked


The human brain and its higher level processes
already exist. The adaptive memory strategies
directly take advantage of human intelligence
that creates another dimension to adapt to
problem solving environment

Conclusions

P.Y. Yin & Fred Glover




36


The successes by integrating scatter search and
its path relinking extensions with tabu search
disclose potential advantages for evolutionary
algorithms that incorporate adaptive memory


It’s time for a new generation of evolutionary
algorithms


Cyber
-
Evolutionary Algorithms


that are evolutionary processes based on
neighborhood structures, adaptive memory, and
responsive strategies

Conclusions