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Agent-Based Configuration of (Metaheuristic) Algorithms - Doctoral dissertation
1. Agent-Based Configuration of
(Metaheuristic) Algorithms
- Doctoral dissertation -
M.Sc. Dagmar Monett Díaz
Artificial Intelligence, Computer Science Dept.
Humboldt University of Berlin
25.02.2005
2. Outline
• Introduction
– Metaheuristics; Configuration process; Why agents?
– PhD: Motivation – Goals - Contributions
• Agent-Based Configuration of (Metaheuristic)
Algorithms
– Architecture; agents; interaction protocols; other details
• Experimental results
• Ongoing projects
• Conclusions and Contributions
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
3. Outline
• Introduction
– Metaheuristics; Configuration process; Why agents?
– PhD: Motivation – Goals - Contributions
• Agent-Based Configuration of (Metaheuristic)
Algorithms
– Architecture; agents; interaction protocols; other details
• Experimental results
• Ongoing projects
• Conclusions and Contributions
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
4. Metaheuristics
A metaheuristic is “a master strategy that guides and modifies other heuristics
(like local search procedures) to produce solutions beyond
those that are normally generated in a quest for local optimality“
[Laguna 2002]
Examples of metaheuristics:
- Traditional approaches:
•EC (Evolutionary Computation): GA (Genetic Algorithms), ES (Evolution
Strategies), GP (Genetic Programming), etc.
•SA (Simulated Annealing), TS (Tabu Search), ANN (Artificial Neural
Networks), EDA (Estimation of Distribution Algorithms), ACS (Ant Colony
Systems), etc.
- Hybrid metaheuristics recent approaches!
New research goals in metaheuristics domain :
- To combine aspects from different metaheuristics, Artificial Intelligence,
Operations Research techniques, etc.
- Experimental design for configuring is important
- Optimization of parameters (i.e. configuration process) is a
relevant issue
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
5. Configuration of algorithms
(or “fine-tuning” of algorithms)
Shortcomings:
Not all metaheuristic algorithms are auto-adaptive (in particular the
hybrid approaches)
Usually, control parameters are set by hand or in the spirit of
brute-force mechanisms; time-consuming task
Very few published research works; not yet an established
research area
Distributed, remote or parallel execution of configuration
algorithms: not existing
Special topic in most recent conferences and workshops
(e.g. HM’04 at ECAI’04 and PSGEA’05 at GECCO’05);
current open question!!
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
6. Example: a Tabu Search Algorithm
• Fixed parameters:
- Tabu criterion (what is forbidden for a given number of iterations)
- Nr. of elite solutions (intensification strategy: restarting the search around them)
- Stop criterion (e.g. maximum number of iterations)
• Free parameter (i.e. factor in study): Tabu list length or tabu tenure
- Levels: 10, 20, 50 3 configurations
• Number of global runs (without varying any parameter): NTrials
one TS
1 TTenure=10 1 TTenure=20 1 TTenure=50
run!!
& & &
fixed factors fixed factors fixed factors
. . .
. + . + .
. . .
NTrials TTenure=10 NTrials TTenure=20 NTrials TTenure=50
& & &
fixed factors fixed factors fixed factors
► Which is the “most acceptable” configuration?
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
7. Why agents for configuring?
Could agents substitute humans in their tasks?
Could they behave as experts would do?
E.g.
• by alleviating the time required while configuring,
i.e., the cost of time-intensive fine-tuning?
• by entering data and processing results?
• by autonomously conducting the required experiments?
• by (semi) automating the configuration process?
Answer: Agent-based configuration !!
Multiagent Systems: properties that are of interest
- Distributed / Remote execution
- Cooperation among the agents
- Autonomy & Specialization
- Collaborative exploration / exploitation of the search space
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
8. Motivation – Goals – Contributions
user problems
Motivation:
Powerful algorithms are needed to (metaheuristic) algorithms
solve several real problems
Configuring them can be a very
difficult combinatorial problem
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
9. Motivation – Goals – Contributions
user problems
Goals:
To support the configuration (metaheuristic) algorithms
process of these algorithms
. Autonomously, distributed, remote, etc.
To (semi)automate both monitoring and
fine-tuning of parameters and conducting
experiments
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
10. Motivation – Goals – Contributions
user problems +CARPS
Contributions:
+CARPS, agent-based approach
(metaheuristic) algorithms
for configuring
. Distributed, collaborative problem solving
. Necessary information specialization/processing
. Flexibility: very important
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
11. Outline
• Introduction
– Metaheuristics; Configuration process; Why agents?
– PhD: Motivation – Goals - Contributions
• Agent-Based Configuration of (Metaheuristic)
Algorithms
– Architecture; agents; interaction protocols; other details
• Experimental results
• Ongoing projects
• Conclusions and Contributions
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
12. +CARPS : Architecture
+CARPS : Multi-Agent System for Configuring Algorithms
in Real Problem Solving
+CARPS
I/O Layer
Algorithm
Configuration
Layer Algorithm
Solution
Layer
Communication Layer
D. Monett (2004). Collaborative JADE Agents Enabling the Configuration of Algorithms. In
Proceedings of the International Conference on Advances in Intelligent Systems –
Theory and Applications, AISTA'2004, IEEE Computer Society, University of Canberra
and CRP Henri Tudor, Luxembourg-Kirchberg, Luxembourg.
D. Monett (2004). +CARPS: Configuration of Metaheuristics Based on Cooperative Agents. In
Proceedings of the First International Workshop on Hybrid Metaheuristics, HM'2004, at
the 16th European Conference on Artificial Intelligence, ECAI'2004, Valencia, Spain.
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
13. +CARPS : Agent interactions
Pf , Px , ag. lists GUI
MAS-User
UM interaction
Pf , ag. lists
best ag. lists
solutions paths to M
ABRRHC and i/o files
info and SM par. names,
ag. lists ag. lists
exch.
solutions solutions data
configs
configs M
solutions
AC AS
solutions TS, GA,
ES, etc.
STi initial results
configs
Algorithm’s execution:
optimization process
STi (user problem’s parameters)
ISM SCB Configuration process:
optimization process
(metaheuristic’s parameters)
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
14. Agent-Based Configuration Algorithm
Configuration algorithm:
Agent-Based Random-Restart Hill-Climbing
Particularities:
• Stochastic Hill-Climbing algorithm (restart from different candidate
solutions)
• Well-known metaheuristic; easy to implement
• Construction of a topology (neighbors per solution)
• Migration policy (AC agents exchange best-so-far obtained solutions)
• ABRRHC: itself a hybrid metaheuristic
Implementing a different only few changes in
configuration algorithm specialized agent!!!
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
15. Worth of a metaheuristic
Factors used to analyze metaheuristic’s performance:
• quality of the best solution found until a stop criterion is verified
• time to get the best solution
• algorithm's time to reach an “acceptable“ solution
• number of function evaluations performed until a stop criterion is
verified, etc.
Best-so-far configuration
• a worth equation indicates “how good” the metaheuristic is
• more than one factor at a time is considered
• minimization procedure
Example of worth equation (using a weighted sum approach)
worth( p ) w1 f s w2 f t w3 f v Consider quality of the
solutions, time to get
fx them and evaluations
& Normalization, e.g.: for all repetitions with the
max( f s , f t , f v ) same configuration (i.e. p)
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
16. Agents’ communications
Interaction Protocols (IPs)
• Define typical patterns of message exchange between agents
• Agents are supposed to behave consistently by following such IPs
• FIPA standards (e.g. Contract Net IP)
• New applications might need new IPs
– E.g. agent-based configuration of (metaheuristic) algorithms
• In +CARPS: IPs are implemented as FSM Behaviors from JADE
• Agent communications: ACL messages, FIPA compliant
D. Monett (2004). Interaction Protocols for +CARPS Agents: Booking and Getting Engaged
for Configuring. In Proceedings of the Workshop Concurrency, Specification, and
Programming CS&P'2004, volume 3: Multiagent Systems and Applications, Caputh,
Germany (Also in Special Issue of Fundamenta Informaticae –to appear–)
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
17. Agents’ communications
Most relevant IPs in +CARPS Helper-Booker-Protocol
AS, AC UM
Initiator Participant
Interaction Protocols propose
reject-proposal
Helper-Booker IP failure
accept-proposal
Engagement IP inform
failure
[dead-
Request IP line1]
confirm
cancel [deadline2]
- Which agents will participate?
- Among UM agent (booker) and
AC and AS agents (helpers)
- UM registers with the Directory Facilitator
- Helpers are initiators of the HBIPs; bookers are responders
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
23. Outline
• Introduction
– Metaheuristics; Configuration process; Why agents?
– PhD: Motivation – Goals - Contributions
• Agent-Based Configuration of (Metaheuristic)
Algorithms
– Architecture; agents; interaction protocols; other details
• Experimental results
• Ongoing projects
• Conclusions and Contributions
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
24. Metaheuristics that are considered
Genetic Algorithm (GA)
• GA optimal parameters unknown
• Parameter estimation in chemical reactions (copolymerizations)
D. Monett, J.A. Méndez, G.A. Abraham, A. Gallardo, J. San Román (2002). An
Evolutionary Approach to Reactivity Ratios Prediction. Macromol. Theory Simul.,
11(5):525-532. Wiley-VCH Verlag GmbH, Weinheim, Germany.
D. Monett (2001). On the automation of evolutionary techniques and their application to
inverse problems from Chemical Kinetics. In Proceedings of the GECCO'01 Graduate
Student Workshop, Genetic and Evolutionary Computation Conference, San
Francisco, California, USA.
• Data provided by colleagues from Institute of Polymer Science and
Technology, Superior Council of Scientific Research, Spain, and
Biomaterials Center, Havana University, Cuba
Evolution Strategy (ES)
• ES optimal parameters known
• Function optimization (Rechenberg 94)
• Data and program provided by I. Santibáñez-Koref, FG Bionik und
Evolutionstechnik , TU Berlin
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
25. Example: Configuring an ES
Evolution Strategy
(1, )-ES
One parent Number of offspring
on each generation (the only individuals that undergo selection)
• The ES itself optimizes a quadratic function (sphere model)
• ES efficiency: progress to the optimum on each generation
• Fixed parameters: = 10000, = 1e-30, t = 240
• Free parameters: and c
• Theoretical optima: (, c) = (5, 1) and (5, -1)
Experimental settings
60 search trials (ABRRHC)
4 neighbors / configuration
4 solutions to exchange among the AC agents
proportion of AC agents / parameter to fine-tune = 2
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
26. Obtained solutions
Solution qualities for varying control parameters
Total of best singular solutions
(including all trials or repetitions) = 2 104
Example of an “acceptable”
singular solution
(according to its quality)
(, c) = (5.15965245, 0.87023895)
0,004 5
4
0,003
3
quality
quality
0,002
2
0,001 1
0
0
1,00 3,00 5,00 7,00 9,00 -9,97 -7,15 -4,77 -2,42 -0,73 1,70 3,90 6,63 9,21
lambda c
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
27. Pareto-optimal singular solutions
60
50
40 Weight vector
quality
30 (w1, w2, w3) = (1.0, 0.0, 0.0)
20
10
0 The importance goes to the solution qualities
0 1000 2000 3000 4000 5000 6000
time (msec)
(when calculating the worth)
60
50 Calculated Pareto-optimal solutions,
40 according to all criteria,
quality
30 are presented to the user
20
(GUI + data files)
10
0
0 5000 10000 15000 20000 25000 30000 35000
func.eval.
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
28. Configuration process
Estimated time per agent:
AC 32,8282333 min
800
AS 3,25705868 hours
600
% from the total:
Configurators 14,382467 400
Solvers 85,617533
200
Total of algorithm’s
evaluations = 10 520 0
ABRRHC Solver
(i.e. ES evaluations)
time (min) 131,3129333 781,6940833
as all AC agents would as all AS agents would
sequentially run sequentially run
Other experiments already done with ES and GA:
- varying the worth equation and the weight vectors
- varying the proportion of agents, exchanges, neighbors per solution, etc.
- studying IPs (e.g. HBIP, EIP, RequestIP) in detail
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
29. Outline
• Introduction
– Metaheuristics; Configuration process; Why agents?
– PhD: Motivation – Goals - Contributions
• Agent-Based Configuration of (Metaheuristic)
Algorithms
– Architecture; agents; interaction protocols; other details
• Experimental results
• Ongoing projects
• Conclusions and Contributions
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
30. Ongoing projects
• Optimizing risk strategies in random investment
environments (GA, 4 free parameters, 2x agents)
(E. Navarro, D. Monett, V. Uc Cetina: Machine Learning Approaches for Investment
Strategies --- working paper)
• Adapting soccer agents’ movement, RoboCup 3D
Simulation League (ANN, 50 free parameters, 1x & 2x agents)
New to +CARPS:
- Connection Clients or CC agents
- TCP/IP communication instead data files transfer
- related ontologies, classes, and agent behaviors and interactions
• Including other configuration algorithms
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
31. Outline
• Introduction
– Metaheuristics; Configuration process; Why agents?
– PhD: Motivation – Goals - Contributions
• Agent-Based Configuration of (Metaheuristic)
Algorithms
– Architecture; agents; interaction protocols; other details
• Experimental results
• Ongoing projects
• Conclusions and Contributions
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
32. Conclusions and Contributions
+CARPS …
is an agent-based approach to the configuration of algorithms
including, but not limited to, metaheuristics
is a framework that helps monitoring control factors of metaheuristics
is a tool useful for conducting experiments when executing these
algorithms
follows a theoretical description and formalization of the configuration
problem
D. Monett (2003). Configuration of Metaheuristics: Overview and Theoretical Approach. In
Proceedings of the Workshop Concurrency, Specification, and Programming
CS&P'2003, volume 2, Czarna, Poland.
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
33. Conclusions and Contributions
+CARPS …
consists of different types of autonomous, cooperative agents that
support the configuration of metaheuristic algorithms in a distributed
fashion
implements a Random-Restart Hill-Climbing algorithm to search for
solutions during the configuration process
includes new interaction protocols that are followed by the agents in
order to cover communication requirements needed in the domain of
analysis
is a prototype implemented over JADE that follows FIPA specifications
provides an infrastructure for a distributed, remote and parallel
execution of configuration algorithms (i.e. ontologies, interaction
protocols, agent behaviors, and other supporting classes)
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
34. Agent-Based Configuration of
(Metaheuristic) Algorithms
- Doctoral dissertation -
M.Sc. Dagmar Monett Díaz
Artificial Intelligence, Computer Science Dept.
Humboldt University of Berlin
25.02.2005
35. Extra, additional slides
- Used in answers, examples, discussion, etc. -
M.Sc. Dagmar Monett Díaz
Artificial Intelligence, Computer Science Dept.
Humboldt University of Berlin
25.02.2005
36. “Fine-tuning” of metaheuristics
Auto-adaptive metaheuristics…
Brute force
- Every configuration is tested and the best one is selected
Meta-evolution as “configurator”
- Evolutionary algorithms fine-tune other evolutionary algorithms
- Examples:
meta-level Evo.Alg.
GA that produces ES+GA
meta-solutions
They optimize It optimizes a
benchmark single case of
functions GA GA the sphere model
[Grefenstette, 1986] [Bäck, 1994]
[Pham, 1995]
[Pedroso, 1997]
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
37. More general “configurators”
Using experimental design
- Statistical design + gradient descent [Coy et al., 2000] (two local search
heuristics)
- Fractional experimental design + local search [Adenso-Díaz and
Laguna, 2003] (Tabu Search and Simulated Annealing)
Racing algorithms
- Sequentially evaluate candidate configurations and discard
poor ones as soon as statistically sufficient evidence is gathered
against them
- Example:
F-Race: based on a statistical method for hypothesis testing
[Birattari et al., 2002] (MAX-MIN-Ant System)
Parallel approaches
- Independent runs: master-slave approach [Blesa and Xhafa, 2004]
(Tabu Search)
- Parallel implementations of metaheuristics have also contributed
to the topic…
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
38. Auto-adaptive metaheuristics
Examples of mechanisms that are used:
• Genetic operators encoded as part of the representation of individuals
(e.g. ES, first Evo.Alg. that considered self-adaptation)
• Variable population size / chromosomal length (e.g. GA)
• Mutation schedules for varying the mutation of individuals at early
and later evolution stages differently (e.g. GA)
E.g.: non-uniform mutation
r : random in [0, 1]
v (t ,UBk vk ), r 0.5 T : max. generation number
v 'k k b : system parameter determining
vk (t , vk LBk ), r 0.5 the degree of dependency on
(1 t )b iteration number t (e.g. b=5)
(t , y ) y (1 r T ) LB, UB: upper & lower bounds
• Cooling schedules for controlling the search and the probability of
accepting worsening solutions (e.g. SA)
t : temperature
E.g.: geometric and arithmetic cooling
r : random in [0, 1]
f ( s ) f ( s ') t t , 0 s, s’: solutions
e t r ? t t ,0 1 f: solution quality
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
39. Constructing neighbor configurations
AC agents construct neighbor configurations
ISM agent manages the instantiation strategies
Example: The new parameter value is randomly generated in a
neighborhood of the current value, with
NeighborsPercent
sizeN
index ( currentNei ghbor )
sizeN : size of the neighborhood
NeighborsPercent : percent of the original restriction to the free parameter
index(currentNeighbor) : number of neighbors already constructed + 1
• As the number of neighbors increases, it decreases the ratio of the
neighborhood around the current parameter value
• Decreasing the neighborhood size adds lower noise to the current
parameter value
• Considering other adaptive techniques local to the ISM agent !!
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
40. Stop criteria
TS 1 TTenure=10 1 TTenure=20 1 TTenure=50
& & &
fixed factors fixed factors fixed factors
. . .
NTrials = ?? .
. + .
. + .
.
NTrials TTenure=10 NTrials TTenure=20 NTrials TTenure=50
& & &
fixed factors fixed factors fixed factors
• Metaheuristics are not exact methods
• Goal: To find “acceptable” solutions beyond local optima
• Challenge: In less trials as possible
• One approach:
Stop when the fluctuations of averaged solution qualities with the
same configuration is not significant any more (-solution “stability”):
i i 1
s s
j 1
j
j 1
j
si si 1 NTrials = i+1
i i 1
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
41. Stability transition trial
Other performance indexes that could be considered:
- CPU time to get the solutions
- number of function evaluations
Related definitions:
-computational time “stability”
-evaluations “stability”
However, it can happen that
NTrials for -solution “stability”
NTrials for -computational time “stability”
NTrials for -evaluations “stability”
Stability transition trial Τ: Minimal number of trials at which the
metaheuristic is solution, computational time and evaluations stable
when evaluated with the same configuration
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
42. Expectation value
From Experimental Physics:
• Given: N independent measurements of a physical constant
• Mean of the measurements (“estimate” of the true value):
N
1
y
N
yi 1
i
N
1
• Expectation value or true value: y lim
N N
yi 1
i
Expectation value of the solution qualities
from the program outputs y(∙) when evaluating the metaheuristic
with the same configuration, say p
T
1
Correction: E y ( p) s y( p) s lim s i
T: stability transition trial
T T i 1
However, y(∙) may be not (and similarly for computational time, function
normally distributed… evaluations or any other performance index…)
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
43. Expectation value
Idea:
what to expect from repeated outcomes
• Discrete random variable X with values x1, x2, …, xN
• Probability function f(xi)=P(X= xi) (probability of obtaining each xi)
• Expectation value: Special case: If f ( x )
1
N i
N
E X xi f ( xi ) then N
1
i 1
E X x i X
N i 1
Expectation value of the solution qualities
from the program outputs when evaluating the metaheuristic
with the same configuration
(and similarly for computational time, function
evaluations or any other performance index…)
• Probability function is unknown
• It can be estimated after the trials are done, i.e. by simulation
• Like this: Relative frequencies could be tabulated. They are estimates
of the probabilities
1
Correction: f ( si ) is difficult to have…
T
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
44. Goal of the experiments: Twofold
Testing the system
How to use +CARPS: conducting experiments & monitoring the
configuration process
• Aim: to seek for optimum values to the user problem’s
parameters
• User problem: Chemical optimization problem (Parameter estimation
in chemical reactions)
• Former obtained solutions are reproduced; better ones are found
Evaluating the system
How does +CARPS work: functionality of the system
• Aim: to seek for optimum values to the algorithm’s parameters
• Configuration algorithm is tested
• Also communication among the agents, interaction protocols, etc.
• User problem: of secondary importance at this level
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
45. Technical information
+CARPS (by August 2004)
Agent framework: JADE 3.2
Programming language: JavaTM 2 SDK Standard Ed. 1.4.2
Development tool: BlueJ 2.0 beta
Source code lines: 22 500
(most important) Classes 113 (.java)
GAs (by 2002)
Programming language: Visual C++ 5.0
Development tool: Microsoft Developer Studio 97
Source code lines: 6 000
Total of classes: 10 (.cpp) + 10 headers (.hpp)
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
46. Agent-based configuration
Optimization Simulation Optimization
Agent-based Algorithm Algorithm
configuration execution functioning
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
47. +CARPS : Development
• software development: it was carried out in an evolutionary,
object-oriented fashion
• bottom-up strategy: behaviors and agent actions and objects
were designed, implemented, debugged, and tested as the
needs arose, from simple to more complex components.
For example, a draft for the configuration ontology was first
considered which later turned into the four vocabularies and
ontologies
• GUI: it was very important for testing agent interactions and
their functioning
• JADE (Java Agent DEvelopment Framework)
– FIPA specifications
• +CARPS classes are Java classes
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
48. Motivation – Goals – Contributions
Real problem
user problems
Motivation: (metaheuristic) algorithms
Powerful algorithms are needed to
solve several real problems
Developer
Configuring them can be a very
difficult combinatorial problem System
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
49. Motivation – Goals – Contributions
Real problem
user problems
Goals: (metaheuristic) algorithms
To support the configuration
process of these algorithms
Developer
. Autonomously, distributed, remote, etc.
To (semi)automate both monitoring and System
fine-tuning of parameters and conducting
experiments
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
50. Motivation – Goals – Contributions
Real problem
+CARPS
user problems
Contributions:
(metaheuristic) algorithms
+CARPS, agent-based approach
for configuring
. Distributed, collaborative problem solving Developer
. Necessary information specialization/processing System
. Flexibility: important
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
51. Agent-based Configuration: Steps
I: Initializationof variables to manage solutions, agent
Initialization of variables to manage solutions, agent
communications, and conditions for stop criteria. Initialization of the
search procedure.
II: Constructionof starting configurations with initial levels for for the
Construction of starting configurations with initial levels the
free parameters.
III: Agent-based configuration: application the search procedure
Agent-based configuration: application of of the search procedure
and exchange of best-so-far solutions among the agents, until stop
criteria meet.
IV: Organization of partialand global solutions. Report best-so-far
partial and global solutions. Report best-so-far
configuration and Pareto-optimal solutions.
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
52. Why agents for configuring?
Distributed execution
The (metaheuristic) algorithms and the agents could be physically
distributed over a network.
Remote execution
Local agents can interact with other agents situated on remote computers,
thus allowing for the remote execution of algorithms, which do not need
to be located where the users are.
Cooperation
Agents can decide whether to cooperate or not, as well as to ask for
Cooperation, if needed, in order to solve the original configuration
problem. Furthermore, they can cooperate by exchanging best-so-far
obtained solutions.
Autonomy & specialization
Agents that interact with the users do not need to know how to configure
algorithms, nor to solve them or to manage solutions (and vice versa),
for example, in order to operate and to have control over their actions.
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
53. Why agents …
Exploration of the search space
Subproblems are considered so as to cover the complete search space as
well as possible.
Exploitation of the search space
It is done by studying a free parameter in detail. The more different
parameter variations are considered, the wider the analysis and study of
the related parameter.
Incremental quality solution
Solutions are improved by each agent when applying the configuration
algorithm and solutions received from other agents can also improve the
ones obtained so far.
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
54. Fine-tuning (II)
Example by using GA
Factor in study: Population size, PopSize
Levels: 50, 100, 150 (3 configurations)
Number of runs for each configuration: NRuns
1 PopSize=50 1 PopSize=100 1 PopSize=150
+ + +
fixed factors fixed factors fixed factors
. . .
. . .
. . .
NRuns PopSize=50 NRuns PopSize=100 NRuns PopSize=150
+ + +
fixed factors fixed factors fixed factors
(Factori * Levelj * Runsk )
Which is the “most acceptable” solution?
Which is the “best-so-far” configuration?
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
55. Fine-tuning (III)
tunable parameters (or factors) = controllable parameters
= free parameters
parameter tuning = fine-tuning = parameter setting
= configuring = configuration process
Configuration
a specific setting or combination of free and fixed parameters
Restrictions
define the levels or different values that parameters may have
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
56. Worth of a metaheuristic
Pareto-optimum: Situation where it is not possible to improve (to decrease)
the value of an objective function without deteriorating
(increasing) that of at least one other
Example of worth equation (using the weighted sum approach)
worth(C ) w1 s2 w2 t2 w3 v2
where: m std. deviations
m
x xi 2 x
i 1
i Normalization (e.g.):
2
x i 1 , x 2
x
variances m averages
m
max( s2 , t2 , v2 )
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
57. Agent-based Config.: Algorithm
Configuration ABRRHC(c, p) {
// c : initial configuration from SCB agent
// p : index of the parameter to fine-tune
bestConfiguration = c; i = j = k = 0;
do while (i < MaxExchanges and condition1) {
do while (j < MaxTrials and condition2) {
do while (k < MaxNeighbors and condition3) {
nc = neighborConfiguration(c, p);
evaluate(nc);
if quality(nc) ≤ quality(bestConfiguration) then
bestConfiguration = nc; k++; }
c = bestNeighbor();
if isRestartAllowed then {
rc = restartConfiguration();
evaluate(rc);
if quality(rc) ≤ quality(bestConfiguration) then
bestConfiguration = rc;
c = rc; } j++; }
if isExchangeAllowed then {
ec = exchangeSolution(bestSolution);
if quality(ec) ≤ quality(bestConfiguration) then
bestConfiguration = ec;
c = ec; } i++; }
return bestConfiguration; } // end ABRRHC
58. +CARPS : Development & tools
• JADE (Java Agent DEvelopment Framework)
• +CARPS classes are Java classes.
• +CARPS packages enclose agents,
ontologies, and utilities, separately.
• Classes & hierarchies will be presented as
they are showed when using the BlueJ Java
development tool.
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
60. Config vocabulary and ontology
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
61. InstStrategy vocabulary and ontology
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
62. +CARPS Agents
Types of agents
UM User Mediators
ISM Instantiation Strategy Managers
SCB Starting Configuration Builders
AC Algorithm Configurators
AS Algorithm Solvers
SM Solution Managers
- Agent communication: relevant
- Specialization and distributed information processing: relevant
- Following the standards (e.g. FIPA specifications): important
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
63. SCB agent : Example
P D1 , P2 , P3
1
s1b randInit ,
b
s2 lowLevelInit ,
SCB1 ST1 b
s3 uppLevelInit ,
s b avgInit
4
level11 , level12 ,
level13 , level14
C1 : level11 , P2 , P3 C3 : level13 , P2 , P3
C 2 : level12 , P2 , P3 C 4 : level14 , P2 , P3
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
64. Algorithm Configurators: IPs
AC & AS agents help UM agents with the
configuration process
UM agents book their services
• HBIP : Helper-Booker Interaction Protocol
– HB Initiator Finite State Machine (AC & AS)
– HP Responder Finite State Machine (UM)
• HelperBookerProtoASResponder.java
• HelperBookerProtoACResponder.java
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
65. Helper-Booker-Protocol
AS, AC UM
Initiator Participant
propose
reject-proposal
failure
accept-proposal
inform
failure
[dead-
line1]
confirm
cancel [deadline2]
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
68. Agents’ communications
Most relevant IPs in +CARPS Engagement-Protocol
AC AS
Interaction Protocols Initiator Participant
query-if
Helper-Booker IP
refuse
Engagement IP inform
[deadline1]
Request IP confirm
[deadline2]
cancel
- Which agents will solve the algorithms?
- Among AC agents (configurators)
and AS agents (solvers)
- Particular case of the standard FIPA Query IP
- Configurators are initiators of the EIPs
- Solvers are responders
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
69. Algorithm Configurators: IPs
An engagement is a compromise, a contract,
between two parts
AS agents solve the algorithm
being configured
Engagement Interaction Protocol
AC agents need AS agents in the
configuration process
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
70. Algorithm Configurators: IPs
• HBIP : Helper-Booker Interaction Protocol
– HB Initiator Finite State Machine (AC & AS)
• HelperBookerProtoInitiator.java
– HP Responder Finite State Machine (UM)
• EIP : Engagement Interaction Protocol
– E Initiator Finite State Machine (AC)
• EngagementProtoInitiator.java
– E Responder Finite State Machine (AS)
• Request Interaction Protocol
– Request Initiator Finite State Machine (AC)
• RequestProtoInitiator.java
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
71. Algorithm Configurators: IPs
Engagement-Protocol
AC AS
Initiator Participant
query-if
refuse
inform
[deadline1] confirm
cancel [deadline2]
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
72. Algorithm Configurators: IPs
Engagement-Initiator-FSM
SELECT_SOLVER
2
SND_QUERY_IF REMOVE_SOLVER
1 4
6
5
RCV_RESPONSE SND_RES_NOTIFICATION
3
7
TERMINATE_PROTOCOL
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
73. Engagement-Responder-FSM
1
RCV_QUERY_IF
2
VERIFY_CONDITIONS
10
4 3
SND_REFUSE SND_INFORM
5
6 RCV_RESPONSE 7
8 9
ENGAGE SND_CANCEL
VERIFY_TERMINATION
11
TERMINATE_PROTOCOL
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
74. Agents’ communications
Most relevant IPs in +CARPS FIPA-Request-Protocol
Initiator Participant
Interaction Protocols request
refuse
Helper-Booker IP [refused]
agree
[agreed and
Engagement IP notification necessary]
Request IP
failure
[agreed]
inform-result: inform
- Solving the algorithms
- Among AC agents (configurators)
and AS agents (solvers)
- FIPA Request IP - like
- Configurators are initiators of the EIPs
- Solvers are responders
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
75. Agents’ communications
Request-Initiator-FSM
SND_REQUEST
8
1 RCV_RESPONSE
3 2
5
RCV_RES_NOTIFICATION UPDATE_CONTENT
4 6 7 9
RCV_DATA TERMINATE_PROTOCOL
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
76. Solver-FSM
PREPARE_DATA
1
VERIFY_REPETITIONS EXEC_ALGORITHM
5 4 3
2
VERIFY_SOLUTIONS UPDATE_COUNTER
6
7
PROCESS_RESULTS
CONSTRUCT_SOLUTIONS
UPDATE_VARIABLES
PROCESS_ERR_CODE
TERMINATE_SOLVER
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
77. AC-AS communications
paths to M and io files,
par. names, ag. lists
data
configs
AC AS M
solutions
results
input files
config
AC AS M .exe
singular
solution
output files
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
78. +CARPS : Tree
+carps
src
agent
AC
behaviours
AS
ISM
Agents and their behaviors
SCB
SM
UM
metah
copga Algorithms to configure
evoes
ontology
agentList
configuration Vocabularies and ontologies
strategy
userproblem
util
comm
graphics
gui Utilities
io
jExtra
table
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
79. <time.h>
GA <windows.h>
time.hpp time.cpp
commonDef.hpp
<math.h>
random.hpp random.cpp
<assert.h>
<fstream.h> utility.hpp utility.cpp
<iostream.h> gene.hpp gene.gpp
<iomanip.h> chromosome.hpp chromosome.cpp
objfunc.hpp objfunc.cpp
population.hpp population.cpp
ga.hpp ga.cpp
<ctype.h>
<stdlib.h>
copga.hpp copga.cpp
<string.h>
mainGA.cpp
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
86. Testing engagements
Elapsed time of the Engagement IPs
- Proportion of AC agents per parameter to fine-tune = 2
- Intra-platform communication
- AC agents run in a main container; AS agents, in a secondary one
- Number of parameter to fine-tune = 2 ( and c)
25
20
4 AC agents & 4 AS agents
time (sec)
15
10
Engagements:
5 AC1-AS2 (or AC1-AS4)
AC2-AS3
0
1 2 3 4 AC3-AS1
AC 0,094 11,688 23,359 0,094 AC4-AS4 (or AC1-AS2)
AS 23,875 0,359 12,11 0,344
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
87. Pareto-optimal singular solutions
60
50
40
Weight vector
(w1, w2, w3) = (1.0, 0.0, 0.0)
quality
30
20
10
0
The importance goes to the solution qualities
0 1000 2000 3000 4000 5000 6000 (when calculating the worth)
time (msec)
60
50
40 Four best Pareto-optimal singular
solutions according to their qualities
quality
30
20
10
lambda c quality
0
5,15965245 0,87023895 3,98E-05
0 5000 10000 15000 20000 25000 30000 35000 4,86909981 -0,98344576 5,70E-05
func.eval.
4,4049138 -1,00838506 3,43E-04
4,83154784 -1,04477195 3,57E-04
Total of Pareto-optimal
= 388
singular solutions
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
88. (cont.)
1,2E+04
1,0E+04
time (msec)
8,0E+03
6,0E+03
4,0E+03
2,0E+03 4,0E+04
3,5E+04
0,0E+00
1 2 3,0E+04
time (msec)
AC 10985 94 2,5E+04
AS 11266 141 2,0E+04
1,5E+04
1,0E+04
6,0E+04 5,0E+03
0,0E+00
5,0E+04 1 2 3 4
109 35843 11000 11015
time (msec)
4,0E+04 AC
AS 11375 203 11422 36172
3,0E+04
2,0E+04
1,0E+04
0,0E+00
1 2 3 4 5 6
AC 156 57953 13500 125 93 156
AS 437 14687 469 438 469 59328
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
89. (cont.)
14
12
10
best quality
8
6
4
2
0 2,50E-03 4,00E-03
1 30 59 88 117 146 175 204 233 262 3,50E-03
2,00E-03
t 3,00E-03
1,50E-03
best worth
2,50E-03
8n 6n 4n 2n
1,00E-03 2,00E-03
1,50E-03
5,00E-04
1,00E-03
0,00E+00 5,00E-04
5,0E-04
-5,00E-04 0,00E+00
4,0E-04 1 30 59 88 117 146 175 204 233 262
best quality
3,0E-04 t
2n 4n 8n 6n
2,0E-04
1,0E-04
0,0E+00
1 30 59 88 117 146 175 204 233 262
t
4n 8n
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
90. q9vp
1,8
1,6
r2
1,4
1,2
15
1 12
best fitness
2 2,5 3 3,5 4
9
r1
6
3
0,09 0
0,088 1 10 19 28 37 46 55 64 73 82 91 100
0,086 population
best distance
0,084
0,082 current best-so-far
0,08
0,078
0,076
0,074
1 10 19 28 37 46 55 64 73 82 91 100
population
current best-so-far
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
91. 2n&repeat
1,4E+06
1,2E+06
1,0E+06
time (msec)
8,0E+05
6,0E+05
4,0E+05
2,0E+05
0,0E+00
2n-1 2n-2 2n-3 2n-4 2n-5 mean
Solver 911399 798396 1136179 982935 992848 964351,4
ABRRHC 99601 87823 108009 112955 103573 102392,2
1,4E+06
1,2E+06
1,0E+06
time (msec)
8,0E+05
6,0E+05
4,0E+05
2,0E+05
0,0E+00
2n-1 2n-2 2n-3 2n-4 2n-5
Solver 911399 798396 1136179 982935 992848
ABRRHC 99601 87823 108009 112955 103573
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
92. (cont.)
2,5E+07
2,0E+07
time (msec)
1,5E+07
1,0E+07
5,0E+06
0,0E+00
1x 2x 3x
Solver 2414233 7422779 16841628
ABRRHC 185971 1141846 3319888
60
50
40
time (min)
30
20
10
0
1x 2x 3x
Solver 20,11860833 30,92824583 46,7823
ABRRHC 1,549758333 4,757691667 9,221911111
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
93. Related publications
D. Monett (2004). Interaction Protocols for +CARPS Agents: Booking and Getting Engaged
for Configuring. In G. Lindemann, H.-D. Burkhard, L. Czaja, A. Skowron, H.
Schlingloff, Z. Suraj, editors, Proceedings of the Workshop Concurrency,
Specification, and Programming CS&P'2004, volume 3: Multiagent Systems and
Applications, pages 507-518, Caputh, Germany. Informatik-Bericht Nr. 170.
(Also in Special Issue of Fundamenta Informaticae –to appear–)
D. Monett (2004). Collaborative JADE Agents Enabling the Configuration of Algorithms. In D.
Khadraoui, editor, Proceedings of the International Conference on Advances in
Intelligent Systems - Theory and Applications, AISTA'2004, IEEE Computer Society,
University of Canberra and CRP Henri Tudor, Luxembourg-Kirchberg, Luxembourg.
D. Monett (2004). +CARPS: Configuration of Metaheuristics Based on Cooperative Agents. In Ch.
Blum, A. Roli, M. Sampels, editors, Proceedings of the First International Workshop on
Hybrid Metaheuristics, HM'2004, at the 16th European Conference on Artificial
Intelligence, ECAI'2004, pages 115-125, Valencia, Spain.
D. Monett, J.A. Méndez, G.A. Abraham, A. Gallardo, J. San Román (2002). An
Evolutionary Approach to Reactivity Ratios Prediction. Macromol. Theory Simul.,
11(5):525-532. Wiley-VCH Verlag GmbH, Weinheim, Germany.
D. Monett (2001). On the automation of evolutionary techniques and their application to
inverse problems from Chemical Kinetics. In C. Ryan, editor, Proceedings of the
GECCO'01 Graduate Student Workshop, Genetic and Evolutionary Computation
Conference, pages 429-432, San Francisco, California, USA.
D. Monett (2003). Configuration of Metaheuristics: Overview and Theoretical Approach. In L.
Czaja, editor, Proceedings of the Workshop Concurrency, Specification, and
Programming CS&P'2003, volume 2, pages 353-364, Czarna, Poland. Zakład Graficzny
UW, zam. 591/2003.
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
94. Conclusions and Contributions
+CARPS, agent-based approach to the configuration of algorithms
including, but not limited to, metaheuristics is proposed
+CARPS consists of different types of cooperative agents that support the
autonomous configuration of metaheuristic algorithms and that follows
FIPA specifications in a distributed fashion
Ontologies, interaction protocols, agent behaviors, and other supporting
classes conform the +CARPS infrastructure for the agent-based
configuration
Conception and development of new interaction protocols that are
followed by the agents in order to cover communication requirements
+CARPS
needed in the domain of analysis I/O Layer
Algorithm
Configuration
Layer Algorithm
Solution
Layer
Communication Layer
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
95. Conclusions and Contributions
Theoretical description and formalization of the configuration problem
D. Monett (2003). Configuration of Metaheuristics: Overview and Theoretical Approach. In
Proceedings of the Workshop Concurrency, Specification, and Programming
CS&P'2003, volume 2, Czarna, Poland.
Implementation of a Random-Restart Hill-Climbing algorithm that some
specialized agents apply to search for solutions during the configuration
process
+CARPS is also a framework in which monitoring of control factors of
metaheuristics can be easily made
At the same time, it can be seen as a powerful tool useful for conducting
experiments when executing metaheuristic algorithms
+CARPS
I/O Layer
Algorithm
Configuration
Layer Algorithm
Solution
Layer
Communication Layer
D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005