Case study: An intelligent decision-support system

© 2005 IEEE.The explosive growth in decision-support systems over the past 30 years has yielded numerous "intelligent" systems that have often produced less-than-stellar results. In addition to generating data that users can't immediately apply to their tasks, such systems are often static, rendering them unable to respond to the dynamic nature of both business and the larger world. In this case study, the authors describe a thorny logistical problem: recommending the best distribution for used cars among various automobile auctions. They solved this problem by combining prediction, optimization, and adaptation techniques into one integrated system that has generated impressive profits for a large auto manufacturer.This article is part of a special issue on transportation and logistics.Zbigniew Michalewicz, Martin Schmidt, Matthew Michalewicz, and Constantin Chiria

PolyEDA: Combining Estimation of Distribution Algorithms and Linear Inequality Constraints

Algorithmus , Linearität , Ungleichhei

La nostalgia del presente in Proust, Helleu e Boldini

Descrivendo in Le Côté de Guermantes un quadro del pittore immaginario Elstir, che corrisponde esattamente al celebre Dejeuner des canotiers di Renoir, Proust spiega che la “poetica dell’istante” caratteristica dell’impressionismo implica un “sensus finis” e quindi una nostalgia per il presente felice ma minacciato di morte. Questa chiave di lettura viene applicata anche al pittore francese Helleu (che per altri aspetti è anch’egli un modello di Elstir) e a Boldini, la cui “mondanità” è accompagnata da una percezione dolente della realtà

Time series forecasting for dynamic environments: The DyFor Genetic Program model

Copyright © 2007 IEEESeveral studies have applied genetic programming (GP) to the task of forecasting with favorable results. However, these studies, like those applying other techniques, have assumed a static environment, making them unsuitable for many real-world time series which are generated by varying processes. This study investigates the development of a new ldquodynamicrdquo GP model that is specifically tailored for forecasting in nonstatic environments. This dynamic forecasting genetic program (DyFor GP) model incorporates features that allow it to adapt to changing environments automatically as well as retain knowledge learned from previously encountered environments. The DyFor GP model is tested for forecasting efficacy on both simulated and actual time series including the U.S. Gross Domestic Product and Consumer Price Index Inflation. Results show that the performance of the DyFor GP model improves upon that of benchmark models for all experiments. These findings highlight the DyFor GP's potential as an adaptive, nonlinear model for real-world forecasting applications and suggest further investigations.Neal Wagner, Zbigniew Michalewicz, Moutaz Khouja, and Rob Roy McGrego

Analysis and modeling of control tasks in dynamic systems

Copyright © 2002 IEEEMost applications of evolutionary algorithms deal with static optimization problems. However, in recent years, there has been a growing interest in time-varying (dynamic) problems, which are typically found in real-world scenarios. One major challenge in this field is the design of realistic test-case generators (TCGs), which requires a systematic analysis of dynamic optimization tasks. So far, only a few TCGs have been suggested. Our investigation leads to the conclusion that these TCGs are not capable of generating realistic dynamic benchmark tests. The result of our research is the design of a new TCG capable of producing realistic nonstationary landscapesRasmus K. Ursem, Thiemo Krink, Mikkel T. Jensen, and Zbigniew Michalewic

Computational intelligence for evolving trading rules

Copyright © 2008 IEEEThis paper describes an adaptive computational intelligence system for learning trading rules. The trading rules are represented using a fuzzy logic rule base, and using an artificial evolutionary process the system learns to form rules that can perform well in dynamic market conditions. A comprehensive analysis of the results of applying the system for portfolio construction using portfolio evaluation tools widely accepted by both the financial industry and academia is provided.Adam Ghandar, Zbigniew Michalewicz, Martin Schmidt, Thuy-Duong Tô, and Ralf Zurbrug

Evolutionary L∞ identification and model reduction for robust control

An evolutionary approach for modern robust control oriented system identification and model reduction in the frequency domain is proposed. The technique provides both an optimized nominal model and a 'worst-case' additive or multiplicative uncertainty bounding function which is compatible with robust control design methodologies. In addition, the evolutionary approach is applicable to both continuous- and discrete-time systems without the need for linear parametrization or a confined problem domain for deterministic convex optimization. The proposed method is validated against a laboratory multiple-input multiple-output (MIMO) test rig and benchmark problems, which show a higher fitting accuracy and provides a tighter L�¢���� error bound than existing methods in the literature do

Unexpected impact of D waves in low-energy neutral pion photoproduction from the proton and the extraction of multipoles

Contributions of $D$ waves to physical observables for neutral pion photoproduction from the proton in the near-threshold region are studied and means to isolate them are proposed. Various approaches to describe the multipoles are employed --a phenomenological one, a unitary one, and heavy baryon chiral perturbation theory. The results of these approaches are compared and found to yield essentially the same answers. $D$ waves are seen to enter together with $S$ waves in a way that any means which attempt to obtain the $E_{0+}$ multipole accurately must rely on knowledge of $D$ waves and that consequently the latter cannot be dismissed in analyses of low-energy pion photoproduction. It is shown that $D$ waves have a significant impact on double-polarization observables that can be measured. This importance of $D$ waves is due to the soft nature of the $S$ wave and is a direct consequence of chiral symmetry and the Nambu--Goldstone nature of the pion. $F$-wave contributions are shown to be negligible in the near-threshold region.Comment: 38 pages, 13 figures, 19 tables. Version to be published in Physical Review

Adaptive business intelligence in healthcare - A platform for optimising surgeries

Adaptive Business Intelligence (ABI) combines predictive with prospective analytics in order to give support to the decision making process. Surgery scheduling in hospital operating rooms is a high complex task due to huge volume of surgeries and the variety of combinations and constraints. This type of activity is critical and is often associated to constant delays and significant rescheduling. The main task of this work is to provide an ABI based platform capable of estimating the time of the surgeries and then optimising the scheduling (minimizing the waste of resources). Combining operational data with analytical tools this platform is able to present complex and competitive information to streamline surgery scheduling. A case study was explored using data from a portuguese hospital. The best achieved relative absolute error attained was 6.22%. The paper also shows that the approach can be used in more general applications.This work has been supported by FCT –Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/201

Free search in multidimensional space

One of the challenges for modern search methods is resolving multidimensional tasks where optimization parameters are hundreds, thousands and more. Many evolutionary, swarm and adaptive methods, which perform well on numerical test with up to 10 dimensions are suffering insuperable stagnation when are applied to the same tests extended to 50, 100 and more dimensions. This article presents an original investigation on Free Search, Differential Evolution and Particle Swarm Optimization applied to multidimensional versions of several heterogeneous real-value numerical tests. The aim is to identify how dimensionality reflects on the search space complexity, in particular to evaluate relation between tasks’ dimensions’ number and corresponding iterations’ number required by used methods for reaching acceptable solution with non-zero probability. Experimental results are presented and analyzed