Piero P. Bonissone's Research Interests:
Evolutionary Algorithms (EA), Optimization, and
Tuning
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Timeline
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Papers
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(2009) P.
Bonissone, R. Subbu, J. Lizzi, Multi Criteria Decision Making (MCDM): A
Framework for Research and Applications, IEEE
Computational Intelligence Magazine, 4(3):48-61, Aug 2009 - [GE GR
Technical Report, 2009GRC412, Apr 2009 (pdf)]. We
view Multi Criteria Decision Making (MCDM) as the conjunction of three
components: search, preference tradeoffs, and interactive visualization. The first
MCDM component is the search process over the space of possible solutions to
identify the non-dominated solutions that compose the Pareto set. The
development of efficient search algorithms has been the goal of
Multi-Objective Optimization (MOO), from classical mathematical programming
to evolutionary approaches. However MOO's emphasis has been on generating densely sampled,
well-distributed Pareto sets, without worrying about the solution selection
phase. The second component is the
preference tradeoff process to select a single solution (or a small subset of
solutions) from the Pareto set. The development of methods to capture and
aggregate preferences has been the goal of Bayesian and Fuzzy decision-making
techniques. However, their emphasis
has been on the aggregation mechanisms to select a solution, rather than the
solution generation phase. The
third component is the interactive visualization process to embed the
decision-maker in the solution refinement and selection loop. We often need
to embed the decision-maker in the solution refinement and selection loop. To
this end, we need to understand and present the impacts that intermediate
tradeoffs in one sub-space could have in the other ones, while allowing
him/her to retract or modify any intermediate decision steps to strike appropriate
tradeoff balances We introduce a requirement
framework to compare most MCDM problems, their solutions, and analyze their
performances. We focus on two research challenges and illustrate them with
three case studies in electric power management, financial portfolio
rebalancing, and air traffic planning.
(2009) P. Bonissone, X Hu, R. Subbu, A Systematic
PHM Approach for Anomaly Resolution: A Hybrid Neural Fuzzy System for Model
Construction, Proc. PHM 2009,
San Diego, CA, Sept 27-Oct 1, 2009. - [GE GR Technical Report, 2000, GRC839,
Sept. 2009 (pdf)]
(2008) P. Bonissone, Research Issues in
Multi Criteria Decision Making (MCDM): The Impact of Uncertainty in Solution
Evaluation, IPMU 2008, We consider Multi Criteria Decision
Making (MCDM) as the conjunction of three components: search, preference
tradeoffs, and interactive visualization. The first
MCDM component is the search process over the space of possible solutions to
identify the non-dominated solutions that compose the Pareto set. The second
component is the preference tradeoff process to select a single solution (or
a small subset of solutions) from the Pareto set. The third component is the
interactive visualization process to embed the decision-maker in the solution
refinement and selection loop. We focus on the intersection of these three
components and we highlight some research challenges, representing gaps in
the intersection. We introduce a
requirement framework to compare most MCDM problems, their solutions, and
analyze their performances. We focus on the impact of uncertainty in each of
these components and illustrate it with a real-world application.
(2007) R. Subbu, P.
Bonissone, Bollapragada, K. Chalermkraivuth, N. Eklund, N. Iyer, R.
Shah, F. Xue and W. Yan, A review of two industrial deployments of
multi-criteria decision-making systems at General Electric, First IEEE Symposium
on Computational Intelligence in Multi-Criteria Decision-Making (MCDM 2007),
Honolulu, Hawaii, Two
industrial deployments of multi-criteria decision-making systems at General
Electric are reviewed from the perspective of their multi-criteria
decision-making component similarities and differences. The motivation is to
present a framework for multi-criteria decision-making system development and
deployment. The first deployment is a financial portfolio management system
that integrates hybrid multi-objective optimization and interactive Pareto
frontier decision-making techniques to optimally allocate financial assets
while considering multiple measures of return and risk, and numerous regulatory
constraints. The second deployment is a power plant management system that
integrates predictive modeling based on neural networks, optimization based
on multi-objective evolutionary algorithms, and automated decision-making
based on Pareto frontier techniques. The integrated approach, embedded in a
real-time plant optimization and control software environment dynamically
optimizes emissions and efficiency while simultaneously meeting load demands
and other operational constraints in a complex real-world power plant
(2007) P. Bonissone, A. Varma, K.
Aggour, and Feng Xue, Design of local fuzzy models using evolutionary
algorithms, Computational Statistics and Data Analysis,
51:398-416, 2006, (pdf) 2007 - [GE GR Technical Report, 2006GRC594,
(2007) K. Aggour, P. Bonissone,
(2006) R. Subbu, P. Bonissone,
N. Eklund, W. Yan, N. Iyer, F. Xue, R. Shah, Management of Complex Dynamic Systems based on Model-Predictive
Multi-objective Optimization, CIMSA 2006, pp. 64-69, La Coruna,
Spain, Jul 12-14, 2006 (pdf) – [GE GR Tech. Report,
2006GRC456,
(2005) P.
Bonissone, R. Subbu, N. Eklund, and T. Kiehl, Evolutionary Algorithms +
Domain Knowledge = Real-World Evolutionary Computation, IEEE
Transactions on Evolutionary Computation, 10(3): 256-280, June 2006 (pdf)
- [GE GR Technical Report, 2004GRC142, We discuss implicit and explicit knowledge representation mechanisms for
Evolutionary Algorithms (EA's). We also describe
offline and on-line meta-heuristics, as examples of explicit methods to
leverage this knowledge. We illustrate the benefits of this approach with
four real-world applications. The first application is automated insurance
underwriting - a discrete classification problem, which requires a careful
tradeoff between the percentage of insurance applications handled by the
classifier and its classification accuracy. The second application is
flexible design and manufacturing - a combinatorial assignment problem, where
we optimize design and manufacturing assignments with respect to time and
cost of design and manufacturing for a given product. Both problems use
meta-heuristics as a way to encode domain knowledge. In the first application
the EA is used at the meta-level, while in the second application the EA is
the object level problem solver. In both cases the EA's
use a single-valued fitness function that represents the required tradeoffs.
The third application is a lamp spectrum optimization that is formulated as a
multi-objective optimization problem. Using domain customized mutation
operators we obtain a well-sampled Pareto Front showing all the non-dominated
solutions. The fourth application describes a scheduling problem for the
maintenance tasks of a constellation of 25 Low Earth Orbit satellites. The
domain knowledge in this application is embedded in the design of a
structured chromosome, a collection of time-value transformations to reflect
static constraints, and a time-dependent penalty function to prevent schedule
collisions.
(2005) R. Subbu, P. Bonissone, N. Eklund, S. Bollapragada, K. Chalermkraivuth,
Multiobjective Financial Portfolio Design: A Hybrid Evolutionary
Approach, 2005 IEEE Congress on Evolutionary Computation (CEC 2005),
pp. 1722-1729, Edinburgh UK, September 2-5, 2005 (pdf) - [GE GR
Tech. Report, 2005GRC389, A principal challenge in modern
computational finance is efficient portfolio design - portfolio optimization followed
by decision-making. Optimization based on even the widely used Markowitz
two-objective mean-variance approach becomes computationally challenging for
real-life portfolios. Practical portfolio design introduces further
complexity as it requires the optimization of multiple return and risk
measures subject to a variety of risk and regulatory constraints. Further,
some of these measures may be nonlinear and nonconvex,
presenting a daunting challenge to conventional optimization approaches. We
introduce a powerful hybrid multiobjective optimization approach that
combines evolutionary computation with linear programming to simultaneously
maximize these return measures, minimize these risk measures, and identify
the efficient frontier of portfolios that satisfy all constraints. We also
present a novel interactive graphical decision-making method that allows the
decision-maker to quickly down-select to a small
subset of efficient portfolios. The approach has been tested on real-life
portfolios with hundreds to thousands of assets, and is currently being used
for investment decision-making in industry.
(2005) F. Xue, A.C. Sanderson, P. Bonissone, R.J. Graves, Fuzzy
Logic Controlled Multi-Objective Differential Evolution, Proc. FUZZ-IEEE
2005, Reno NV, USA, May 22-25, 2005 (pdf)
- [GE GR Tech. Report, 2005GRC347, In
recent years, multi-objective evolutionary algorithms (MOEA) have generated a
large research interest. MOEA's attraction stems
from their ability to find a set of Pareto solutions rather than any single, aggregated optimal solution for a multi-objective
problem. As for single-objective evolutionary algorithms (SOEA),
multi-objective evolutionary algorithms also require parameter tuning to
achieve desirable performance. In the literature we can find Fuzzy Logic
Controllers (FLC's) applied to online parameter
control for SOEA. In this paper, we propose to use a FLC to dynamically
adjust the parameters of a particular Multi-Objective Differential Evolution
(MODE) algorithm. The fuzzy logic controlled multi-objective differential
evolution (FLC-MODE) is applied to a suite of benchmark functions. Its
results are compared to those obtained by using MODE with constant parameter
settings. We show that the FLC-MODE obtains better results in 80% of the
testing examples. Given that the benchmarks were synthetic test functions, we
designed the FLC using only our understanding of the working mechanism of the
MODE, without incorporating any additional problem-specific knowledge. When
addressing real-world applications, we expect the FLC to be an excellent way
for representing and leveraging their associated heuristic knowledge.
(2005) P. Bonissone, A. Varma, Predicting
the Best Units within a Fleet: Prognostic Capabilities Enabled by Peer
Learning, Fuzzy Similarity, and Evolutionary Design Process, Proc.
FUZZ-IEEE 2005, (2003) K. Aggour, M. Pavese, P. Bonissone, and W. Cheetham, SOFT-CBR: A self-optimizing fuzzy tool for case-based reasoning, Proc. 5th Int. Conference on Case-Based Reasoning (ICCBR) 2003, pp. 5-19, Lecture Notes in Artificial Intelligence, Trondheim, Norway, 2003 (pdf) A generic
Case-Based Reasoning tool has been designed, implemented, and successfully
used in two distinct applications. SOFT-CBR can be applied to a wide range of
decision problems, independent of the underlying input case data and output
decision space. The tool supplements the traditional case base paradigm by
incorporating Fuzzy Logic concepts in a flexible, extensible component-based
architecture. An Evolutionary Algorithm has also been incorporated into
SOFTCBR to facilitate the optimization and maintenance of the system.
SOFT-CBR relies on simple XML files for configuration, enabling its
widespread use beyond the software development community. SOFT-CBR has been
used in an automated insurance underwriting system and a gas turbine
diagnosis system. (2003) T. Khiel and P. Bonissone, Evolving
Artificial Biochemical Reaction Networks: First Steps, Proc.
International Conference on Systems Biology 2003,
(2003) R. Subbu and P. Bonissone, A Retrospective View of
Fuzzy Control of Evolutionary Algorithm Resources, Proc. FUZZ-IEEE 2003,
Best Paper Award, pp. 143-148, St..Louis, MO, 2003 (pdf) (2003) W.
Cheetham, K. Goebel, and P. Bonissone, Knowledge Engineering for
Decision Support using Soft Computing, in Applied Decision Support
with Soft Computing, Kacprzyk, Carlsson, and
X. Yu (Eds.), Physica-Verlag, 2003. General Electric's
(2002) P. Bonissone, R. Subbu, and K.
Aggour, Evolutionary Optimization of Fuzzy Decision Systems for
Automated Insurance Underwriting, Proc. FUZZ-IEEE 2002, pp.
1003-1008, Honolulu, HI, May 2002 (pdf) A robust method for automating the tuning
and maintenance of fuzzy decision-making systems is presented. A configurable
multi-stage mutation-based evolutionary algorithm optimally tunes the
decision thresholds and internal parameters of fuzzy rule-based and
case-based systems that decide the risk categories of insurance applications.
The tunable parameters have a critical impact on the coverage and accuracy of
decision-making, and a reliable method to optimally tune these parameters is
critical to the quality of decision-making and maintainability of these
systems.
(2001) P. Bonissone and K. Aggour, Fuzzy Automated Braking System
for Collision Prevention, Proc. FUZZ-IEEE 2001,
pp. 757-760, vol.3,
(1998) R. Subbu,
A. Anderson, P. Bonissone, Fuzzy Logic Controlled Genetic Algorithms versus
Tuned Genetic Algorithms: An Agile Manufacturing Application, IEEE
International Symposium on Intelligent Control, NIST,
(1996) P. Bonissone, P. Khedkar, Y-T Chen, Genetic Algorithms for Automated
Tuning of Fuzzy Controllers: A Transportation Application, Proceedings
of the 1996 IEEE Conference on Fuzzy Systems (FUZZ-IEEE'96), pages
674--680, |
Patents (Issued / Applications)
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(2009)
Systems and
methods for multi-objective portfolio analysis and decision-making using
visualization techniques, P. Bonissone, S. Bollapragada, K.
Chalermkraivuth, N. Eklund, N. Iyer, R. Subbu, US Patent 7,630,928 (Dec 8, 2009) (2009) Systems and methods for multi-objective portfolio optimization, K. Chalermkraivuth, S. Bollapragada, P. Bonissone, M. Clark, N. Eklund, N. White N. Iyer, R. Subbu, US Patent 7,542,932 (June 2, 2009)
(2009) Method and system for performing model-based multi-objective asset
optimization and decision-making, K. Chalermkraivuth, S.
Bollapragada, P. Bonissone, M. Clark, N. Eklund, N. Iyer, R. Subbu, US Patent 7,536,364 (May 19, 2009), (WO2005081902)
(2008)
Systems
and methods for efficient frontier supplementation in multi-objective
portfolio analysis, P. Bonissone, S.
Bollapragada, C. Chalermkraivuth, N. Eklund, N. Iyer, R; Subbu, US Patent No. 7,469,228 (Dec 23, 2008) (2008) Systems and methods for multi-level optimizing control systems for boilers, V. Badami, R. Subbu, A. Taware, P. Bonissone, N. Widmer, US Patent No. 7,389,151 (June 17, 2008) (2001) System and Method for Generating a Fuel-Optimal Reference Velocity Profile for a Rail-Based Transportation Handling Controller, P. Bonissone, Y-T Chen, P. Khedkar, P. Houpt, J. Schneiter, US Patent No. 6,243,694 (Jun. 5, 2001)
(2006) Method and system
for performing model-based multi-objective asset optimization and
decision-making, (165411), R. Subbu, P. Bonissone, N. Eklund N. Iyer, R Shah, W.
Yan, C. Knodle, J. Schmid, filed
(2006) Method and system for performing
multi-objective predictive modeling, monitoring, and update for an asset, R. Subbu, P. Bonissone, N.
Eklund N. Iyer, R Shah, W. Yan, C. Knodle; J.
Schmid, PUB_20060247798, filed
(2005) Systems and methods for initial sampling in
multi-objective portfolio analysis, S. Bollapragada, P. Bonissone, K.
Chalermkraivuth, N. Eklund, N. Iyer, R. Subbu, PUB_20050187849, filed
(2005) Systems and methods for multi-objective
portfolio analysis using Pareto sorting evolutionary algorithms, R. Subbu, S. Bollapragada, P. Bonissone,
K. Chalermkraivuth, N. Eklund, N. Iyer, PUB_20050187846, filed
(2005) Systems and methods for multi-objective
portfolio analysis using dominance filtering, N. Eklund, S. Bollapragada, P. Bonissone,
K. Chalermkraivuth, N. Iyer, R. Subbu, PUB_20050187845, filed
(2003) System for optimization of insurance
underwriting suitable for use by an automated system, P. Bonissone, R. Messmer, A. Patterson, D.
Yang, M. Pavese, R. Subbu, K. Aggour, PUB_20030187702 filed
(2003) Process for
optimization of insurance underwriting suitable for use by an automated
system, P. Bonissone, R. Messmer, A. Patterson, D.
Yang, M. Pavese, R. Subbu, K. Aggour, PUB_20030187701 filed |
Supervised MS -
PhD Theses
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MS Theses:
(2004) Multiobjective Evolutionary
Algorithms: Targeting Migrant Election and Selection Schemes on an
(2003) A Memory Enabled
Nondominated Sorting Genetic Algorithm, M. Abrams, ECSE Dept. [Advisor:
P.P. Bonissone]
(1999) Genetic
Algorithms for Autonomous Satellite Control, T. Khiel, CS Dept, [Advisors: P.P. Bonissone, M. Skolnick] (1996) Algorimes
genetics aplicats a problemes
d'optimitzacio de functions continues i discretes (Genetic algorithms applied to optimization
problems of continuous and discrete functions), I. Plancheria
i Coll, Universitat Politecnica
de Catalunya (UPC), CS Dept.
[Advisor: P.P. Bonissone] |
Projects
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Home Page| GE Research
Computer and Decision Sciences
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