Machine intelligence, adaptive business intelligence, and natural intelligence

Copyright © 2008 IEEEOne of the key observations of the author was that machine intelligence might be defined as the capability of a system to adapt its behavior to meet desired goals in a range of environments. Interestingly, the three components of prediction, adaptation, and optimization constitute the core modules of adaptive business intelligence systems. Clearly, the future of the business intelligence industry lies in systems that can make decisions, rather than tools that produce detailed reports.Zbigniew Michalewicz and Matthew Michalewic

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

Optimally Controlled Field-Free Orientation of the Kicked Molecule

Efficient and long-lived field-free molecular orientation is achieved using only two kicks appropriately delayed in time. The understanding of the mechanism rests upon a molecular target state providing the best efficiency versus persistence compromise. An optimal control scheme is referred to for fixing the free parameters (amplitudes and the time delay between them). The limited number of kicks, the robustness and the transposability to different molecular systems advocate in favor of the process, when considering its experimental feasibility.Comment: 5 pages, 2 figures (version 2 contains some minor additions and corrects many misprints

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

A study of order based genetic and evolutionary algorithms in combinatorial optimization problems

In Genetic and Evolutionary Algorithms (GEAs) one is faced with a given number of parameters, whose possible values are coded in a binary alphabet. With Order Based Representations (OBRs) the genetic information is kept by the order of the genes and not by its value. The application of OBRs to the Traveling Salesman Problem (TSP) is a well known technique to the GEA community. In this work one intends to show that this coding scheme can be used as an indirect representation, where the chromosome is the input for the decoder. The behavior of the GEA's operators is compared under benchmarks taken from the Combinatorial Optimization arena.(undefined

Coevolutionary optimization of fuzzy logic intelligence for strategic decision support

©2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.We present a description and initial results of a computer code that coevolves fuzzy logic rules to play a two-sided zero-sum competitive game. It is based on the TEMPO Military Planning Game that has been used to teach resource allocation to over 20 000 students over the past 40 years. No feasible algorithm for optimal play is known. The coevolved rules, when pitted against human players, usually win the first few competitions. For reasons not yet understood, the evolved rules (found in a symmetrical competition) place little value on information concerning the play of the opponent.Rodney W. Johnson, Michael E. Melich, Zbigniew Michalewicz, and Martin Schmid

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