[PDF] ALIO Back Matter

SC16
4 - Compensatory Fuzzy Logic: A New Axiomatic Approach for
Fuzzy Logic
Rafael Espin, Professor, Instituto Superior Politécnico José Antonio
Echeverría (CUJAE), CUJAE. Marianao, La Habana, CP 19930, Cuba,
espin@ind.cujae.edu.cu, Eduardo René Fernández González
Compensatory Fuzzy Logic, a new compensatory non associative axiomatic
approach for multivalued logic, is presented. Based Geometric Mean Compensatory
Logic (BGMCL), a multivalued approach from this perspective and some relevant
properties like an a special way of generalization of Boolean Logic, are presented
too.
■ SC16
Aula 385- Third Floor
The Next Ten Years in Metaheuristics Research
Cluster: Metaheuristics
Invited Session
Chair: Natalio Krasnogor, University of Nottingham, United Kingdom,
nxk@Cs.Nott.AC.UK
1 - How Many Good Ideas Do You Need?: An Investigation of Sizing
Effects in Meta-heuristics with Evolving Local Search Operators
Jim Smith, The University of the West of England,
Bristol Institute of Technology, United Kingdom
We investigate the search efficiency of a class of adaptive memetic algorithms where
the local search operators are co-evolved alongside a population of potential
solutions to the problem in hand. Such co-evolutionary mechanism requires a
means for assigning meme fitness based in some way on the improvement they
cause in solutions at a particular stage in the search process. We examine schemes
based on both the extremal and mean improvement caused, and compare these to
the implicit self-adaptive scheme. Simultaneously we examine the effect of using
different fixed or adaptive pivot functions and depths of search. Results show that
provided the fitness is correctly assigned the system successfully adapts the
global/local search trade-off via evolution of the memes’ search depth. The system is
also able to adapt the optimal choice of greedy or steepest ascent. Unlike recent
work on adaptive operator choice, results suggest that a fitness based on a meme’s
mean, rather than extremal affect provides more reliably effective optimisation
results. Despite the close coupling between the two population, the self-adaptive
schemes which use implicit fitness assignment are less successful than a well
designed co-evolutionary scheme. Finally we examine the effect of changing the
size of the meme pool and show that a surprisingly large number can be processed
and benefit evolution.
2 - Hyper-heuristics: Past, Present and Future
Graham Kendall, University of Nottingham, United Kingdom,
graham.kendall@nottingham.ac.uk
Hyper-heuristic research can be traced back to the 1960s, although the term was
only first used in the late 1990s. After outlining the motivation underpinning hyperheuristics,
we will present a brief history of the area and provide an overview of
some of the work that is being undertaken at the present time. Finally, some
potential research directions will be discussed, as well as some of the challenges that
we face.
3 - Automated Parameter Tuning with CMA-ES and Divide-and-evolve:
Lessons Learn and Challenges Ahead
Marc Schoenauer, Université Paris Sud, INRIA Saclay, 15,
rue Georges Clemenceau, Orsay Cedex, 91405, France,
marc.schoenauer@u-psud.fr
Parameter tuning has always be the Achille’s heel of Meta-Heuristics in general, and
Evolutionary Computation in particular. Several approaches will be surveyed in this
talk, from off-line setting to one-line adaptation of the meta-parameters of the
search algorithms. Whereas off-line tuning has now reached a mature state, with
several efficient approaches, on-line adaptation remains out of reach in most
contexts. CMA-ES (Covariance Matrix Adaptation Evolution Strategy) is one
remarquable exception. It will be briefly introduced, and some lessons that can be
learned from its success will be proposed, while some other remaining challenges
will be described.
4 - An Unorthodox View on Memetic Algorithms
Natalio Krasnogor, University of Nottingham, United Kingdom,
nxk@Cs.Nott.AC.UK
Memetic Algorithms have become one of the key methodologies behind solvers that
are capable of tackling very large, real-world, optimisation problems. They are being
actively investigated in research institutions as well as broadly applied in industry.
In this talk we provide a pragmatic guide on the key design issues underpinning
Memetic Algorithms (MA) engineering. We begin with a brief contextual
introduction to Memetic Algorithms and then move on to define a Pattern
Language for MAs. For each pattern, an associated design issue is tackled and
illustrated with examples from the literature. We then fast forward to the future and
mention what, in our mind, are the key challenges that scientistis and practitioner
will need to face if Memetic Algorithms are to remain a relevant technology in the
next 20 years.
ALIO / INFORMS International – 2010
40
■ SC17
Aula 387- Third Floor
Data Mining/Machine Learning II
Contributed Session
Chair: Maria Teresinha Arns Steiner, Universidade Federal do Paraná,
UFPR/Centro Politécnico/Jd das Américas, Curitiba, 81.531-990, Brazil,
tere@ufpr.br
1 - Predictive Modeling and Optimization
Steven Shugan, Professor, University of Florida, 2030 NW 24th
Avenue, Gainesville, FL, 32605, United States of America,
sms@ufl.edu
Many decisions depend on selecting the correct response function. Empirical
research sometimes uses predictive accuracy to make selections. However, there are
always wrong response functions that predict better than the true function. The
reason is not faulty estimation, simplicity, or parsimony. Moreover, whether
prediction is vastly better or barely better, wrong response functions dramatically
underestimate optimal expenditures.
2 - Technical Groups and Ranking in The KDD Context – Application to
the Itaipu Instrumentation Data
Maria Teresinha Arns Steiner, Universidade Federal do Paraná,
UFPR/Centro Politécnico/Jd das Américas, Curitiba, 81.531-990,
Brazil, tere@ufpr.br, Rosangela Villwock, Andrea Sell Dyminski
The monitoring of a dam can generate an enormous mass of data which analysis is
not trivial. Itaipu dam has more than 2,200 monitoring instruments. The goal of this
work is to present a methodology to carry out the ranking to the instruments. The
methodology was applied to extensometers. The Ward Method was used in the
clustering and it was possible to find technical justification for the formation of the
groups. The Factorial Analysis showed to be effective to realize the ranking.
■ SC18
Aula 384- Third Floor
Vehicle Routing I
Contributed Session
Chair: Sergio Daniel Conde, Professor, Universidad Argentina John F,
Kennedy, Carlos Crocce 757, Lomas de Zamora, 1832, Argentina,
sergiodanielconde@fibertel.com.ar
1 - An Approach for the IRP Based on Separable Cross Decomposition
and Harmony Search
Mayra Elizondo, PhD, Universidad Nacional Autónoma de México,
Cd. Universitaria Edif. Bernardo Quintan, México D.F., 04510,
Mexico, mayra.elizondo@hotmail.com, Roman Mora
The Inventory-Routing Problem is NP-hard. A three-phase strategy is used to solve
it: Phase 1: Which customers should be visited? Phase 2: What volume of products
should be delivered and what vehicle will be used to supply each customer? And,
Phase 3: Which route should be followed by each truck? The phase 2 uses Cross
Separable Decomposition. The phase 3 consists on many TSP and we use the
metaheuristic, harmony search to solve them. The result is a very efficient ranking
algorithm O(n3).
2 - Covering-routing Problems Considering Sensitive Areas
Maria José P. Lamosa, IEAv/CTA, Rod. dos Tamoios, km 5,5 - Putim,
São José dos Campos, 12228-001, Brazil, maju@ieav.cta.br,
Mónica Maria De Marchi, Daniel Merli Lamosa
This work aims to solve a routing problem in order to cover a region where certain
areas are considered sensitive and must therefore be revisited in the shortest time
possible. In this case, the objective is to find an efficient route that minimize the
maximum time that these areas remain uncovered. The results of this work can be
applied in search and surveillance systems and in disaster management where areas
require monitoring, such as in the case of floods.
3 - Designing an Algorithm for Finding the Minimum Number of
Vehicles to Ensure a Level of Service
David Becerra, Renting Colombia, Crr 52 No. 14 30, Medellín,
Colombia, dbecerr@gmail.com, Lorena Cadavid
In fleet operation, facts like service center enters for maintenance or crash repairs
low the level of service; for solving this problem, companies needs additional fleet to
replace vehicles when they are not available. This study examines the case for a car
rental company, whose goal is find the minimum quantity of extra cars required to
achieve a certain level of added service, considering demands and random events.