# Hidden (Tabu) Secrets of Successful Evolutionary Search Methods

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22 Οκτ 2013 (πριν από 4 χρόνια και 6 μήνες)

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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

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!)

of

the

metaphors
:

Create

a

biased

perspective

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

Employ frameworks of
Combinations

Exploit the
neighborhood concept

in generating
combinations

Integrate with

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
Discrete Applied
Mathematics

49, 221
-
235.)

P.Y. Yin & Fred Glover

6

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

solutions

to

good

solutions

(or

from

good

solutions

to

other

good

solutions)

Path

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

.

of

interchanging

information

between

solutions

in

a

wholesale

fashion,

path

is

a

stepwise

approach

considering

neighborhood

structure

and

memory

strategies

that

may

generate

solutions

not

accessible

by

EAs

P.Y. Yin & Fred Glover

7

Scatter Search

Useful

information

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

constitutes of adaptive memory and a set of
responsive strategies

purposefully comparing previous
states or transactions to those currently contemplated

Recency, frequency, influence, quality

memory to exploit good solution features while
explore new promising regions

Tabu restriction, aspiration criteria

Intensification, diversification

P.Y. Yin & Fred Glover

13

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

-
start

P.Y. Yin & Fred Glover

14

Create Hybrid

Integrate EA and TS

Solution

combination

Path

exploiting

neighborhood

structure

and

memory

directly

problem

context

without

metaphor

restrictions

stepwise

and

systematic,

reducing

the

probability

of

missing

valuable

information

on

the

course

Solution

improvement

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

Using

search
:

“Path

returns

28
,
000

web

pages
.

“Scatter

Search”

returns

50
,
000

web

pages
.

The

first

references

encountered

on

give

a

good

background

for

basic

understanding
.

(Incidental

remark
:

“Tabu

Search”

returns

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

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

Joseph DeFee, CACI, Inc.

Hospital Emergency Room Process

P.Y. Yin & Fred Glover

29

Nurses

Physicians

P
atient

C
are

T
echnicians

(PCTs)

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

treatment

Fill out

registration

OK?

Released

Into

Hospital

Y

N

Current Process

Arrive at

ER

Transfer to

room

treatment

Fill out

registration

OK?

Released

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

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
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

It’s time for a new generation of evolutionary
algorithms

Cyber
-
Evolutionary Algorithms

that are evolutionary processes based on